29 items tagged " market intelligence ,"

  • 3 Strategic Questions the Media Industry’s Future Depends On

    There is no question that the media industry is experiencing dramatic disruption on many fronts—in the way it creates content, distributes content to consumers, and monetizes audiences. These changes are driven by seismic shifts in consumer behavior and an explosion of both consumer- and B2B-facing technologies. The disruption reveals itself in the fast growth of newer content brands like Refinery29 and Vice, the increased use of technologies like Outbrain and Taboola to drive traffic, and the growth of programmatic approaches to advertising revenue. As we reflect on disruption across the industry there are key strategic questions, all of which fundamentally consider balance:

    • What is the right balance between humans and technology across the full media and advertising ecosystem?
    • How do we maximize our creativity as an industry while integrating data-driven approaches?
    • When and how do we shift our businesses from legacy operating models to ones that better reflect the future?

    It is these tensions that now shape the most important considerations for advertisers, their agencies, and the media companies that convene audiences at scale.

    Humans and Automation

    The tension between human-driven and technology-driven capabilities is often miscast, positioning automation as a threat to the people that drive our industry. However, the more strategic opportunity is to enable humans to do what they do best and leverage technology to drive processes that are best served either by highly repeatable algorithmic tasks or by analytical complexity that surpasses the capacity of the human brain. If we draw the line carefully between these complementary approaches, we can unleash the talent in our organizations and apply humans to areas of growth and competitive differentiation. The grounding principle is: let humans do what humans do best, and let technology do what technology does best.

    Approaches to content, distribution, and monetization across the media industry all afford opportunities to explore the nuances of blending people and automation:

    Content and creative. In the content arena, long-form, quality journalism depends on the highest-caliber talent in reporting and editing. However, new technologies like CrowdTangle are better suited to spotting trends from social media to inform reporting and to identifying the optimal promotional mix for a news organization. National Public Radio and Upworthy are just two media organizations using CrowdTangle to power fast-moving social media trend analysis and news curation. Advertising also offers opportunities to blend humans and automation. On the one hand, the recent creative from Nike celebrating “losers” could only have come from the raw ideation of the best creative minds, but at the automated end of the spectrum, hyper-targeted 1:1 digital campaigns may not only benefit from precision in finding consumers but perhaps also from dynamic creative strategies matching multiple creative options with precise audience targets, an approach which can only be executed at scale through technology — via tools like CPXi’s AdReady.

    Distribution strategies. In traditional marketing communications, a well-informed brief will shape a human-driven strategy and insight-guided planning process. Smart cross-functional teams cull through ideas on the best ways to find and influence consumers to embrace a particular perception or take a specific action. While that human ideation is still critical, the inputs get exponentially richer with the right use of data and technology. The transparency and volume of social media interactions, for example, enable us to look past traditional demographic or psychographic characteristics to find clusters of consumers or conversations that are defined by data science to have mathematical density and importance as real communities. Execution against such sophisticated targeting strategies is guided by human insight but also requires powerful data analysis and technology. In any data- or technology-driven process, if people don’t connect the dots between different parts of the strategy, add judgment and context to analyses, and help frame the questions that data enables us to answer, we will not achieve the right outcome. The balance is subtle and sophisticated.

    Monetization. Traditionally, marketplaces for media were largely created by people. Sales people from media companies, agency account teams serving brands, and the clients themselves connected demand with supply. Briefs from clients informed RFPs from agencies, which informed responses and pitches from media companies. Of course, at this moment, particularly for any media that is digital, supply and demand can intersect in real time via bidding in a range of auctions and exchanges. Programmatic technologies allow us to perform a match between placement and price in a highly dynamic, high-volume environment, as a complement to the people-driven processes. To declare that programmatic will become the entire marketplace of the future is too extreme and undervalues the balance between humans and technology. The future demands a balance between big ideas like sponsorships and branded entertainment that can only be developed through conversation and human ideation, and highly efficient media amplification strategies that can best be executed via technology.

    The more sophisticated our approach to balancing humans and technology, the more likely that we can simultaneously unleash the creativity and intelligence of our teams, while making them able to get more impact out of the ideas they create through scalable technologies.

    Creativity and Data

    Creativity sits at the core of brand stewardship, advertising, and content creation. Chief marketing officers and their teams contemplate and shape the brand attributes that best define the relationship between a product or service and its customers. Creative agencies unfurl their best ideas to make advertising memorable while informing or entertaining audiences. And at media companies, journalists, photographers, video producers, and illustrators bring their talent, skills, and experience to shaping stories and features, large and small. But the increased availability of data, and perhaps more importantly, the ability to derive meaningful insights from it, provide new opportunities to inform our creative ideas and to measure their impact.

    A willingness to embrace data strategies as part of a creative process can become a point of differentiation and advantage:

    Content creation. Whether we are shaping important news stories as journalists or producing award-winning advertising creative, storytelling is the means by which we connect messages to audiences in resonant, meaningful ways. Historically, content creation was an exclusively human process but careful blending of technology into the mix can drive even greater editorial or advertising success. Think of the story-building inspiration of a mood board, which is a collage of visual stimuli that evoke the essence, tone, identity, and intent of a potential advertising campaign hoping to reach a specific target audience. By contrast, contemplate the possibility of monitoring a data-defined cluster of that same target audience to evaluate the visual media (think Instagram, Pinterest, memes) that they might be sharing in real time on Twitter. A world of transparent social media engagement offers powerful new sources of insight into the content that most readily engages communities of customers. This data-driven approach yields a dynamic mood board algorithmically calculated based on tweet and retweet volumes within the target audience cluster (full credit to Scale Model at Betaworks for this concept). Data-driven processes cannot replace human creativity and judgment, but they can be a rich complement.

    Marketing strategies. A sharp creative mind can generate ideas to engage audiences by bringing stories to life. Revlon’s Times Square billboard, which projects real-time images of people gathered below on the street over the tagline “Love Is On” (also displayed online), would not have surfaced but for creatives who connected strategy with a means to bring the brand to life. It is one of countless examples where the sheer power of human ideas defines success. But as channels and platforms proliferate, it becomes less feasible to see creativity as the sole factor in deciding how to impact audiences—from media-mix models to precise digital targeting approaches. As the options continue to multiply, data becomes an objective means to evaluate potential strategies across paid, owned, and earned channels. And, instead of traditional demographic breaks dominating the media choices, data reveals more dynamic and meaningful views of audience segmentation to elicit true engagement. Still, while data can offer a starting point for more nuanced views of clustering, human judgment that allows us to discern the data worth a keener focus.

    Measurement. Data is used most robustly for measuring the results and impact of engagement strategies. Whether the metric is outcome-based, like sales, leads, and traffic, or more qualitative, like brand perception and lift, data is widely used to understand the effect of campaigns and to develop audience. And yet, measurement is perhaps the arena most challenged on the metrics front. The fragmentation of digital platforms has fostered a lack of consistent standards, and many of the most innovative experiences rely solely on proprietary publisher-owned metrics for reporting. Impact measurement only becomes more complex as ideas are executed across platforms. And most measurement scenarios do not offer a seamless view across paid, owned, and earned data sets. Yet even as better, more consistent measurement emerges, data will only take us so far in the journey to understand impact. Thoughtful analytics are best married to human judgment to derive insight laden with broader context. Ultimately, human judgment is best poised to truly understand the more subtle dimensions of brand equity and influence.

    Data enables us to free up time previously deployed against the manual parsing and review of the many marketing, communications, and media options, offering new opportunities to apply human creativity to bigger ideas that capture audiences’ imaginations.

    Future Transformation

    The future requires change on a massive scale for most organizations, and the best approach involves leadership’s embracing the complexity, not only of developing the right strategy but executing it with deep attention to the details that matter. Operating models can shift but require a conscious approach to a range of issues, including organizational structure, workflows, technology platforms and overall change management. The product mix can be re-architected to rely increasingly on newer and high-growth offerings, but not before buyers are ready to embrace the new opportunities beyond experimentation. The challenge is to lead the marketplace and be sure new supply connects with demand in real time. And from a financial perspective, investment decisions and revenue expectations require careful forecasting and pacing against expectations to understand the multifaceted shift from legacy business lines to newer ones.

    Disruption of the media industry often feels like a brute force, moving quickly and without discretion. However, the ways we must respond as participants in the ecosystem is quite the opposite, requiring judicious, nuanced approaches. The critical concept is to balance the tensions to drive powerful results.

  • 6 Steps to create a winning market entry strategy

    Market entry strategies

    market entry strategy is a key tool for clarifying what you aim to achieve and how you’re going to achieve it when entering a new market. While an export plan tends to focus on just a few products or services, your market entry strategy will provide you with a roadmap for your whole business.

    A typical market entry strategy can take six to 18 months to implement. That timeline is well worth the effort as it will ensure you have the best distribution channels in place, that you are launching the right product and that your goals align with those of your stakeholders.

    Here are five steps, recommended by Carl Gravel (Director International Expansion at BDC) you can follow to build a winning market entry strategy and start exporting into previously unknown territory.

    1. Set clear goals

    Be specific about what you want to achieve in your new market, including the level of sales you can expect to reach. Keep referring back to these goals as you flesh out your strategy to help you stay on track and confirm that your opportunity, products/services and overall business goals are aligned.

    2. Research your market

    Use every means at your disposal to get to know your new market—including going there in person. Gravel suggests attending trade shows as a participant or exhibitor to meet people, learn about the competition and make business contacts in the area. Market research is a specialism. Especially when it comes to selecting and entering new markets.

    When getting to know your market, it’s important that you learn about it in every dimension—not just business

    -wise but also socially, culturally and politically. If you’re entering a region with a different language or cultural norms than Canada, think about how you’ll communicate with key contacts.

    Explore all the rules that could affect your product and how you produce and deliver it. You’ll also need to understand your labelling requirements to ensure your packaging complies with local regulations. Learn about different distribution channels, too. At this stage, says Gravel, it’s advisable to seek information and counsel from embassies, consulates and industry associations.

    3. Study the competition

    A detailed competitive analysis based on your research and visits to the target market will help you make key decisions—for example, if you need to modify your product or service to customize it for that market. Competitor analysis also is a specialism not every organisation posesses in house.

     

    Gravel says most businesses underestimate the degree of competition existing in new markets. Getting the expert advice of a consultant www.Hammer-intel.comcan help clarify the challenges.

    4. Choose your mode of entry

    There are many ways to enter a new market. You can use the services of a distributor or agent located there. You might become a franchisee or acquire an existing business. You can even construct an entirely new brick-and-mortar facility.

    Gravel says in his experience a lot of companies start by going into the U.S. first—and most choose to partner with an existing distributor. If you choose that path, make sure your strategy includes a unique value proposition for the distributor. Your partner will want to understand what’s in it for them, and how your product or service is different enough to stand out in the marketplace, but not so different that buyers won’t understand what it is.

    5. Figure out your financing needs

    Find out if you'll need to get any financing to support your export venture. You may also want to get insurance that protects your company against losses when a customer cannot pay. EDC offers credit insurance that can help you avoid cash flow issues when an international customer fails to pay.

    6. Develop the strategy document

    Once you’ve worked out the details of your strategy, you’ll be ready to write it out. Once created, this document will be your blueprint going forward, detailing your goals, research findings, contacts, budgets, major action items and timelines, and how you’ll monitor and evaluate your success on an ongoing basis.

    “Be as structured as possible,” says Gravel. “Once you have a plan, it is easier to follow the action items and not be overwhelmed.”

    He also advises having your accountant, lawyer and an external specialist review your strategy. You want to ensure you haven’t missed something that will prevent you from entering the market or require you to pull back after you get there.

     

    Source: Business Development Bank of Canada

    Delivered by Hammer, market intelligence (www.Hammer-intel.com)

     

  • BC - Business & Competitive - Intelligence

    BC (Business & Competitive) Intelligence

    Business Intelligence is zo´n begrip dat zich nauwelijks letterlijk laat vertalen. Bedrijfsintelligentie zou in de buurt kunnen komen, maar valt net als andere vormen van intelligentie moeilijk precies te duiden. Bedrijfsinzicht of -begrip komen wellicht nader in de buurt. Andere benaderingen (van andere auteurs) voegen daar bedrijfs- of omgevingsverkenningen als alternatieve vertaling aan toe.

    Om Business en Competive Intelligence goed te begrijpen maken we hier gebruik van een analytisch schema (tabel 1.1). Daarmee wordt het mogelijk de verschillende verschijningsvormen van BI te onderscheiden en daarmee de juiste variant bij het juiste probleem toe te passen. Belangrijk is dat het hierbij gaat om stereotypen! In de praktijk komen mengvormen voor.

    Het uitgangspunt is dat BI wordt gezien als een informatieproces waarbij met behulp van data, kennis of inzicht wordt geproduceerd.

     

    Data over de interne bedrijfsvoering

    Data over de bedrijfsomgeving

    Bedrijfskundige

    benadering

    A

    B

    Technologische

    benadering

    C

    D

    Tabel 1.1

    In de tabel is een bedrijfskundige van een technologische benadering te onderscheiden. BC Intelligence behandeld BI vanuit de bedrijfskundige processen die dienen te worden ondersteund. Er bestaat ook een technologisch perspectief op BI. Het uitgangspunt van deze benadering is veeleer te profiteren van de mogelijkheden die informatietechnologie biedt om bedrijfsinzicht te verkrijgen.. Op de andere as in het schema worden data over de interne bedrijfsvoering (interne data) van data over de bedrijfsomgeving (externe data) onderscheiden. We spreken met nadruk over onderscheiden in plaats van gescheiden categorieën. In de gebruikspraktijk blijken de categorieën namelijk wel te onderscheiden maar nauwelijks te scheiden. Ze kunnen niet zonder elkaar en zijn vaak ondersteunend of complementair.

    Business Intelligence

     

    Data over de interne bedrijfsvoering

    Data over de bedrijfsomgeving

    Bedrijfskundige

    benadering

    A

    B

    Technologische

    benadering

    C

    D

    Hoewel het onderscheid arbitrair is en de term BI net zo goed voor het totale quadrant gereserveerd zou kunnen worden (met CI als deelverzameling) hebben veel BI projecten betrekking op de cellen A en C.

    BI gaat dus vaak op het optimaliseren van bedrijfsprocessen waarbij het accent ligt op het verwerwen van bedrijfsinzicht uit data over de onderneming zelf. Deze data genereren doorgaans kennis over de huidige situatie van de onderneming. Kennis die voor strategievorming en optimalisatie van bedrijfsresultaten (denk aan Performance Management) onontbeerlijk is.

    De technoloische component van BI wordt  door cel C gerepresenteerd. Helaas heeft deze invalshoek bij veel dienstverleners de overhand. Het accent ligt daarbij op de inrichting van een technologische infrastructuur die adequate kennis over de onderneming en haar prestaties mogelijk maakt. In cel C kunnen daarom zowel ETL-tools, datawarehouses als ook analytische applicaties worden gedacht.

    Redactioneel BI-kring:

    In de cel A hebben wij eigenlijk nauwelijks een categorie gedefinieerd.  Wat mij betreft zou daarPerformance Management thuis horen. Die term zou ik dus willen toevoegen. Als Key words kun je denken aan: Key Performance Indicators, Performance Process Management, Organizational Performance,  PDCA (Plan Do Check Act) Cycle, Performance Planning.

    Voor wat betreft C kunnen we verwijzen naar bovenstaande tekst:Datawarehousingen OLAPzijn daar de centrale elementen.Key words zijn dat databases, ETL (Extraction, Transformation and Load), , architecture, data dictionary, metadata, data marts.

    Met betrekking tot OLAP zijn key words:analytische applicaties, reporting, queries, multidimensionale schema’s, spreadsheet, Kubus, data mining.

    Competitive Intelligence

     

    Data over de interne bedrijfsvoering

    Data over de bedrijfsomgeving

    Bedrijfskundige

    benadering

    A

    B

    Technologische

    benadering

    C

    D

    CI is het proces waarin data over de omgeving van de onderneming in een informatieproces worden getransformeerd in ´strategisch bedrijfsinzicht´. Hoewel de term Competitor en Competitive Intelligence vanaf de tachtiger jaren wordt gebruikt heeft deze benadering ook in de jaren zeventig al aandacht gehad onder de noemer 'environmental scanning'.

    CI speelt een belangrijke rol in strategische maar ook andere bedrijfskundige processen. Prestaties van de onderneming, concurrentiepositie, mogelijke toekomstige posities als ook innovatievermogen kunnen slechts worden bepaald met behulp van kennis over de bedrijfsomgeving.

    Redactioneel BI-kring:

    Competitive Intellience heeft dus te maken met alle informatievoorziening die wordt georganiseerd om de concurrentiepositie van ondernemingen te kunnen bepalen, beoordelen en veranderen. Het raakt dus direct aan strategie, strategische intelligence, concurrentie-analyse, concurrentiepositie, en alle intelligence die nodig is om de positie van de onderneming in de omgeving goed te kunnen beoordelen.

    Het organiseren van CI is in organisaties nog steeds zwaar onderbelicht. Het blijkt moeilijk structuur aan te brengen in de noodzakelijke informatieprocessen als ook om ze uit te voeren. De inrichting van een ´systeem´ dat dit proces zou moeten realiseren staat in het middelpunt van de aandacht maar is voor veel organisaties ook nog een brug te ver. Een verantwoorde ontwikkelbenadering vergroot de succeskansen echter aanzienlijk.

    Data over de bedrijfsomgeving zijn vaak ongestructureerd van aard en in de organisatie voorhanden. De kunst is deze data beschikbaar te maken voor de besluitvorming. Wanneer de data niet in de onderneming beschikbaar is verschillen de technieken en instrumenten die moeten worden ingezet om deze data te ontsluiten van de bij BI gebruikte technieken. De technieken varieren vandocumentmanagementsystemen tot information agents die zelfstandig het internet afzoeken naar interessante bouwstenen (data!). Bij het structureren en analyseren van de ongestructureerde documenten wordt text mining gebruikt (in geval van www; web-content-mining).

    Redactioneel BI-kring

    Om competitive Intelligence adequaat te ondersteunen en met name ook primaire data beschikbaar te maken ten behoeve van het proces zijn Collaboration tools populair. Het gaat hier over kennismanagement achtige systemen en shareware toepassingen die de data-, informatie- en kennisdeling faciliteren. Key words: kennismanagagement, shareware, sharepoint, knowledge management.

    Overzicht data categorieen BI-kring

    Cel A format

    • Performance Management

    Key words:Key Performance Indicators, Performance Process Management, Organizational Performance,  PDCA (Plan Do Check Act) Cycle, Performance Planning

    Cel C format

    • Datawarehousing

    Key words:databases, ETL (Extraction, Transformation and Load), , architecture, data dictionary, metadata, data marts., Big Data

    • Online Analytical Processing

    Key words:analytische applicaties, reporting, queries, multidimensionale

     schema’s, spreadsheet, Kubus, data mining, dashboarding.

    Cel B format

    • Competitive Intelligence

    Key words:strategie, strategische intelligence, concurrentie-analyse, concurrentiepositie, competitor intelligence, technological intelligence, environmental scanning, environmental intelligence.

    • Content (Competitive Intelligence als product)

         Key words:

    Cel D format

    • Collaboration

    Key words:kennismanagagement, shareware, sharepoint, knowledge management.

    • Search methodologies

                     Key words:documentmanagement systemen, spider technologie,

    ongestructureerde informatie, information agents, text mining, content mining. Search technologies.

    Integraal tav hele schema (intelligente organisaties hebben het hele model integraal geimplementeerd)

    • Intelligente organisatie

    Key words:Management informatie, Intelligente organisatie, lerende organisatie, organisationeel leren, leren, Intelligence change management.

     

    Bron: Egbert Philips

     

  • Big Data changes CI fast!

    Few professions are left unchanged by technology. From healthcare to retail to professional sports, professionals of every stripe make use of technology to do what they do better through the application of technology’s most prolific output: data. So it would make sense that an entire industry based on the analysis of data and information also is undergoing a revision.

    Although it’s one of those industries few people think about, the competitive intelligence (CI) field has been gaining

    CI-BIG-DATA

     ground steadily since it was officially recognized as a discipline soon after World War II. The industry really did not get its official CI designation until the early 1980s, when its trade association, The Strategic and Competitive Intelligence Professionals (SCIP), was established.

    There are a few variations on the CI theme, customer intelligence and market intelligence being the most widely recognized outside of the profession. Because of the explosion of data sources, cheap processing power, and many analytics vendors today, “integrated intelligence” is taking over as the umbrella term under which all data collection, analysis, and interpretation and dissemination activities take place.

    “CI has expanded far beyond human intelligence and primary data collection at this point,” said SCIP Executive Director Nannette Bulger. “CI professionals are handling market sizing, segmentation, strategic analysis to support mergers and acquisitions, and so on.”

    As one would expect based on its name, CI is all about providing businesses with insights so they can best the competition. But with the rise of the Internet and the widespread dissemination of information and data, what was once an ivory tower type of pursuit involving high

    ly trained specialists is now, in many respects, the job of everyone in an organization. Data are everywhere. The ability to make sense of it all is still a rare skill, but its collection and organization are no longer the primary job of CI professionals.

    And this is challenging many in the business to take fresh look at what they do, said Bulger. Where once there were just a handful of players claiming CI leadership, now the industry is under siege by a growing host of data-focused startups as well as business intelligence software vendors, marketing automation players, and anyone else who analyzes data to understand business better.

    Bulger trots out example after example of companies that have figured out new business-focused uses for analytics tools developed for other purposes.

    “Before, it was just a lot of data dissemination,” she said. “Now, you have people coming out of MIT’s Media Lab working with cosmetics vendors, for example. There’s a tidal wave coming that the established vendors are trying to ignore.”

    The crest of that wave is companies coming into the CI market that may not be seen as threats by current vendors. There is a supply chain mapping company, for example, that is now doing analytics on their data to help companies avoid disruptions to their operations if a source of raw materials suddenly goes away. Providing a company with the knowledge it needs to switch suppliers quickly and continue operations is a serious competitive advantage in times of scarcity.

    While not CI directly but a great example of unlooked-for-uses of technology, the Formula One racing team McLaren is helping doctors monitor infants in intensive care units, said Bulger. Auto racing teams use real-time monitoring from sensors all over their racecars. As it turns out, this same technology is ideal for monitoring people’s vital signs.

    It is this type of disruption that has the profession in a state of flux, making even deciding who is an “intelligence” vendor and who is not problematic. Tech industry research house IDC has gone so far as to rebrand the entire industry, calling any business that provides other businesses with intelligence “value-added content” (VAC) providers:

    “Big Data and analytics (BDA) solutions are fueling a demand for more and wider varieties of data every day,” IDC wrote in a 2014 opinion brief about HGdata, a big data analytics company. “A raft of new companies that provide a range of data types – from wind speed data to data about what people are watching, reading, or listening to – are emerging to coexist with and sometimes replace more traditional data vendors in the information industry. What’s more is that organizations in many industries are curating and adding value to that content, in some cases transforming it completely and finding new ways of deriving economic value from the data. Value-added content (VAC) is an emerging market. Social media, blog posts, Web transactions, industrial data, and many other types of data are being aggregated, curated, enhanced, and sold to organizations hungry to understand their customers and products as well as the markets in which they exist.”

    At the end of the day, the big data revolution is not about data. It’s about doing what we do better. Whether improving a process, finding a cure for a rare disease, taking over market share from a competitor, or just understanding how things really work, all of these things can be done better through the analysis of data – the more data, the better. Like the world when viewed through the lens of an ultra-slow motion camera or at the tip of an electron microscope, big data gives people the ability to see things they otherwise would not be able to see.

    “I believe this is a huge growth area,” said Heather Cole, president of business intelligence solutions company Lodestar Solutions. “Companies are beginning to feel the effects of ‘digital disruption.’ They must be innovative to thrive. Customer intelligence is a valuable part of innovation. Companies that identify why their clients buy from them find new clients to serve or a new product to serve their existing clients, and will find it is much easier to hold margins and market share even in a highly competitive market.”

  • Bol.com: machine learning om vraag en aanbod beter bij elkaar te brengen

    0cd4fbcf0a4f81814f388a75109da149ca643f45Een online marktplaats is een concept dat e-commerce in toenemende mate blijft adopteren. Naast consumer-to-consumer marktplaatsen zoals Marktplaats.nl, zijn er uiteraard ook business-to-consumer marktplaatse waarbij een online platform de vraag van consumenten en het aanbod van leveranciers bij elkaar brengt.

    Sommige marktplaatsen hebben geen eigen assortiment: hun aanbod bestaat voor 100 procent uit aangesloten leveranciers, denk bijvoorbeeld aan Alibaba. Bij Amazon bedraagt het aandeel van eigen producten 50 procent. Ook bol.com heeft een eigen marktplaatsen: ’Verkopen via Bol.com’. Deze draagt bij aan miljoenen extra artikelen in het assortiment van Bol.com.

    Bewaken van contentkwaliteit

    Er komt veel kijken bij het managen van zo’n marktplaats. Het doel is duidelijk: ervoor zorgen dat de vraag en het aanbod zo snel mogelijk bij elkaar komen, zodat de klant direct een aantal producten krijgt aangeboden die voor hem relevant zijn. En met miljoenen klanten aan de ene kant en miljoenen producten van duizenden leveranciers aan de andere kant, is dat natuurlijk een hele klus.

    Jens legt uit: “Het begint bij de standaardisatie van informatie aan zowel de vraag- als de aanbodkant. Bijvoorbeeld, als je als leverancier een cd van Tsjaikovski of een bril van Dolce & Gabbana bij bol.com wilt aanbieden, dan zijn er vele schrijfwijzen mogelijk. Voor een verkoopplatform als ‘Verkopen via bol.com’ is de kwaliteit van de data cruciaal. Het in stand houden van de kwaliteit van de content is dus een van de uitdagingen.

    Aan de andere kant van de transactie zijn er natuurlijk klanten van bol.com die ook allerlei variaties van termen, zoals de namen van merken, in het zoekveld intypen. Daarnaast wordt er in toenemende mate gezocht op generieke termen als ‘cadeau voor huwelijk’ of ‘spullen voor een feestje’.

    Vraag en aanbod bij elkaar brengen

    Naarmate het assortiment groter wordt, wat het geval is, en de klanten steeds ‘generieker’ gaan zoeken, is het steeds uitdagender om een match te maken en relevantie hoog te houden. Door het volume van deze ongestructureerde data en het feit dat ze realtime geanalyseerd moeten worden, kun je die match niet met de hand maken. Je moet hiervoor de data slim kunnen inzetten. En dat is een van de activiteiten waar het customer intelligence team van bol.com, een onderdeel van customer centric selling-afdeling, mee bezig is.

    Jens: “De truc is om het gedrag van klanten op de website te vertalen naar contentverbeteringen. Door de woorden (en woordcombinaties) die klanten gebruiken om artikelen te zoeken en producten die uiteindelijk gekocht zijn te analyseren en met elkaar te matchen, kunnen synoniemen voor desbetreffende producten worden gecreëerd. Dankzij deze synoniemen gaat de relevantie van de zoekresultaten omhoog en help je dus de klant om het product sneller te vinden. Bovendien snijdt het mes snijdt aan twee kanten, omdat tegelijkertijd de kwaliteit van de productcatalogus wordt verbeterd. Denk hierbij aan verfijning van verschillende kleurbeschrijvingen (WIT, Wit, witte, white, etc.).

    Algoritmes worden steeds slimmer

    Het bovenstaande proces verloopt nog semi-automatisch (met terugwerkende kracht), maar de ambitie is om het in de toekomst volledig geautomatiseerd plaats te laten vinden. Om dat te kunnen doen worden er op dit moment stap voor stap machinelearningtechnieken geïmplementeerd. Als eerste is er geïnvesteerd in technologieën om grote volumes van ongestructureerde data zeer snel te kunnen verwerken. Bol.com bezit twee eigen datacenters met tientallen clusters.

    “Nu wordt er volop geëxperimenteerd om deze clusters in te zetten voor het verbeteren van het zoekalgoritme, het verrijken van de content en standaardisatie”, geeft Jens aan. “En dat levert uitdagingen op. Immers, als je doorslaat in standaardisatie, dan kom je in een selffulfilling prophecy terecht. Maar gelukkig nemen de algoritmes het beetje bij beetje over en worden ze steeds slimmer. Nu probeert het algoritme de zoekterm zelf aan een product te koppelen en legt het deze aan diverse interne specialisten voor. Concreet geformuleerd: de specialisten krijgen te zien dat ‘de kans 75 procent is dat de klant dit bedoelt’. Die koppeling wordt vervolgens handmatig gevalideerd. De terugkoppeling van deze specialisten over een voorgestelde verbetering levert belangrijke input voor algoritmes om informatie nog beter te kunnen verwerken. Je ziet dat de algoritmes steeds beter hun werk doen.”

    Toch levert dit voor Jens en zijn team een volgende kwestie op: waar leg je de grens waarbij het algoritme zelf de beslissing kan nemen? Is dat bij 75 procent? Of moet alles onder de 95 procent door menselijk inzicht gevalideerd worden?

    Een betere winkel maken voor onze klanten met big data

    Drie jaar geleden was big data een onderwerp waarover voornamelijk in PowerPoint‑slides gesproken werd. Tegenwoordig hebben vele (grotere) e-commercebedrijven een eigen Hadoop-cluster. Het is de volgende stap om met big data de winkel écht beter te maken voor klanten en bij bol.com wordt daar hard aan gewerkt. In 2010 is bij het bedrijf overgestapt van ‘massamediale’ naar ‘persoonlijk relevante’ campagnevoering, waarbij er in toenemende mate gepoogd wordt om op basis van diverse ‘triggers’ een persoonlijke boodschap aan de klant te bieden, real-time.

    Die triggers (zoals bezochte pagina’s of bekeken producten) wegen steeds zwaarder dan historische gegevens (wie is de klant en wat heeft deze in verleden gekocht).

    “Als je inzicht krijgt in relevante triggers en niet‑relevante weglaat”, stelt Jens, “dan kun je de consument beter bedienen door bijvoorbeeld de meest relevante review te tonen, een aanbieding te doen of een selectie vergelijkbare producten te maken. Op deze manier sluit je beter aan bij de klantreis en is de kans steeds groter dat de klant bij je vind wat hij zoekt.”

    En dat doet bol.com door eerst, op basis van het gedrag op de website, maar ook op basis van de beschikbare voorkeuren van de klant, op zoek te gaan naar de relevante triggers. Nadat deze aan de content zijn gekoppeld, zet bol.com A/B‑testen in om de conversie te analyseren om het uiteindelijk wel of niet definitief door te voeren. Immers, elke wijziging moet resulteren in hogere relevantie.

    Er komen uiteraard verschillende technieken bij kijken om ongestructureerde data te kunnen analyseren en hier zijn zowel slimme algoritmes als menselijk inzicht voor nodig. Jens: “Gelukkig zijn bij ons niet alleen de algoritmes zelflerend, maar ook het bedrijf, dus het proces gaat steeds sneller en beter.”

    Data-scientists

    Outsourcen of alles in-house doen is een strategische beslissing. Bol.com koos voor het laatste. Uiteraard wordt er nog op ad-hocbasis gebruikgemaakt van de kennis uit de markt als dat helpt om processen te versnellen. Data-analisten en data scientists zijn een belangrijk onderdeel van het groeiende customer centric selling team.

    Het verschil spreekt voor zich: data-analisten zijn geschoold in ‘traditionele’ tools als SPSS en SQL en doen analysewerk. Data scientists hebben een grotere conceptuele flexibiliteit en kunnen daarnaast programmeren in onder andere Java, Python en Hive. Uiteraard zijn er doorgroeimogelijkheden voor ambitieuze data-analisten, maar toch wordt het steeds lastiger om data scientists te vinden.

    Hoewel er in de markt keihard gewerkt wordt om het aanbod uit te breiden; hebben we hier vooralsnog met een kleine, selecte groep professionals te maken. Bol.com doet er alles aan om de juiste mensen te werven en op te leiden. Eerst wordt een medewerker met het juiste profiel binnengehaald; denk aan iemand die net is afgestudeerd in artificial intelligence, technische natuurkunde of een andere exacte wetenschap. Vervolgens wordt deze kersverse data scientist onder de vleugels van een van de ervaren experts uit het opleidingsteam van bol.com genomen. Training in computertalen is hier een belangrijk onderdeel van en verder is het vooral learning-by-doing.

    Mens versus machine

    Naarmate de algoritmes steeds slimmer worden en artificial‑intelligencetechnologieën steeds geavanceerder, zou je denken dat het tekort aan data scientists tijdelijk is: de computers nemen het over.

    Dat is volgens Jens niet het geval: “Je zult altijd behoefte blijven houden aan menselijk inzicht. Alleen, omdat de machines steeds meer routinematig en gestandaardiseerd analysewerk overnemen, kun je steeds meer gaan doen. Bijvoorbeeld, niet de top 10.000 zoektermen verwerken, maar allemaal. Feitelijk kun je veel meer de diepte én de breedte in. En dus is de impact van jouw werk op de organisatie vele malen groter. Het resultaat? De klant wordt beter geholpen en hij bespaart tijd omdat hij steeds relevantere informatie krijgt en daarom meer engaged is. En brengt ons ook steeds verder in onze ambitie om onze klanten de beste winkel te bieden die er bestaat.”

    Klik hiervoor het hele rapport.

    Source: Marketingfacts

  • Companies collect CI, but don't use it

    The first requirement for being competitive is to know what others in your space are offering or plan to offer so you can judge the unique value proposition of your moves. This is just common sense. The second requirement is to anticipate response to yourcompetitive moves so that they are not derailed by unexpected reactions. That’s just common sense, too.

    sitting on data

    The third requirement is to ask the question: Do we have common sense?

    In my work in competitive intelligence I have met many managers and executives who made major decisions involving billions of dollars of commitments with only scant attention to the likely reaction of competitors, the effect of potential disruptors, new approaches offered by startups and the impact of long-term industry trends. Ironically, they spent considerable time deliberating potential customers’ reactions, even as they ignored the effect of other players’ moves and countermoves on these same customers. That is, until a crisis forced them to wake up.

    In my experience, the competitive perspective is almost always the least important aspect i

    n managerial decision-making. Internal operational issues including execution, budgets, and deadlines are paramount in a company’s deliberation, but what other players will do is hardly ever in focus. This “island mentality” is surprisingly prevalent among talented, seasoned managers.

    The paradox is that companies spend millions acquiring competitive or market “intelligence” from armies of vendors and deploy the latest technology disseminating the information internally. Some estimate the market for market research alone at $20 billion annually. Specific competitor information is another $2 billion. On the other hand, management never questions the actual use of this information by employees in brand, product, R&D, marketing, business development, sales, purchasing or any other market-facing function. Instead, management implicitly assumes the information is being used, and used optimally. Leadership is happy to ask that proposals and presentations be backed by “data.”  Every middle manager is familiar with the requirement for a 130- slide deck of tables, graphs and charts in the appendix for presentations to top executives.

    Yet no one asks: which of the information purchased at high cost from the outside army of research vendors and consultants was ignored, missed, discounted, filtered or simply not used correctly?

    What Did Peter Drucker Really Say?

    Peter Drucker is often quoted as coming up with the managerial bromide, “What gets measured gets managed.” Yet this does not actually represent his thoughts on measurement. Some have argued that he never actually said that at all; others have claimed that the quote has been mangled, and that in context, it was part of a larger lamentation that managers would only manage what was easy to measure, rather than what was important or useful. Regardless, it’s clear from Drucker’s writings that he worried that management often measures the wrong things, and believed that some critical aspects of management can’t actually be measured.

    And the impact of competitive information on an organization’s decisions is one of those things that c

    an hardly ever be measured. It is neither direct, nor unambiguous. Since impact can’t be measured and therefore results can’t be directly attributed to the competitive information, management resorts to measuring the wrong thing, exactly as Drucker feared. For example, in several companies I worked with, management measured output. How many reports did the analysts issue? How many research projects were completed within budget and on time? This is the equivalent of searching for your car keys under the street lamp simply because that’s where the light is.

    The failure to measure the impact of competitive data leads to an interesting dilemma for companies: even when it’s obvious that the company has missed an opportunity or been blindsided by a threat because they failed to consider competitive data, managers are at a loss how to improve the situation.

    Improving decision quality – measured as the extent to which decision makers use all available competitive information- requires focus on usage rather than production of intelligence. This is a major mindset leap for most companies but if offers a way to improve decisions without directly measuring the elusive impact. Just ensuring that managers look at and consider competitive perspective should in principle, improve decisions. How can companies achieve that?

    A Simple Yet Powerful Suggestion

    Improving competitive intelligence usage requires an “audit” of major decisions – at the product/service or functional level – before they are approved by management. This competitive intelligence sign-off is simple to institutionalize. It replaces the haphazard dissemination effort of mass of information (much of it may be just noise to the user) with systematic competitive perspective.

    Would a manager submit to a “sign-off” voluntarily? Maybe. Over the years I have encountered many managers who wanted to stress-test their plans and thinking against third parties’ likely moves via war games. But war games are the more advanced step, and they are typically not systematically applied in an organization. A competitive “audit” or review is the more basic level. The potential cost saving or growth opportunities afforded by institutionalized competitive reviews of major initiatives and projects can be significant. A byproduct of these reviews would be better use of costly information sources, or rationalization of the cost of these sources.

    That said, a company can’t force its managers to use information optimally. It can, however, ensure they at least consider it. In many areas of the corporation, mandatory reviews are routine- regulatory, legal, financial reviews are considered the norm. Ironically, competitive reviews are not, even though the cost of missing out on understanding the competitive environment can be enormous. Consider this admittedly extreme example. Financial institutions are known to spend billions on mandatory regulatory and legal reviews of their practices. How much do they spend on mandatory competition review? To judge by the measly performance of mega banks’ in the past two decades, compared with more locally focused smaller banks, not much (The Economist, March 5, 2015, “A world of pain: The giants of global finance are in trouble”).

    Drucker did say, “Work implies not only that somebody is supposed to do the job, but also accountability.” If managers choose deliberately to ignore the competitive perspective, they should be held accountable. And it is only reasonable to ask top management to apply the same principle to itself: a systematic, mandatory, institutionalized strategic early warning review may keep major issues on the table.

    Think about it this way: If competition reviews were mandatory at Sears, Motorola, Polaroid, AOL, Radio Shack and A&P, to name a few, would they have failed to change with the times? We would never know. Common sense suggests a company shouldn’t wish to find out.

    Author: Ben Gilad

    Source: Harvard Business Review

  • Data Lakes necessary for advanced market intelligence

    When The Weather Company wanted to up its game in the forecasting world, executives knew the answer was to analyze even more data. However, the company's data warehouse was too constricting; it accepted only structured data and required a

    data lakess long as six months to develop appropriate schemas.

    "Our goal was to inject data into our businesses as fast as possible to be able to see new opportunities," says Bryson Koehler, executive vice president, CTO and CIO of The Weather Company. "It's not realistic for a business to go dark on a project for any extended period of time just to clean up data. So much changes on a daily basis -- so many new sources of data -- that that journey would never be complete."

    Koehler wanted to bring in data from anywhere it originated, including personal weather stations and Internet of Things sensors, to enrich analysis. With traditional data warehouses, this would have been near impossible because of the unstructured nature of the new data, the volume, and the lengthy development time necessary to process and validate it.

    "We get data from a lot of startups, and I can't ask these companies to create a specialized format for us," Koehler says. "They would go somewhere else that would take it [as is],and that would take away a competitive advantage."

    To ward off that potential, two years ago The Weather Company became an early adopter of data lakes. This approach allows enterprises to ingest, analyze and store unstructured, semi-structured and structured data in an agnostic manner, providing a more flexible repository than traditional data warehouses.

    Author: Sandra Gittlen

    Source: Computerworld

     
  • De blik vooruit: digitaal transformeren met Big Data

    blik vooruitHet ‘Big’ in Big Data staat voor velen alleen voor de grote hoeveelheden data die organisaties in huis hebben. Die data lijken in veel praktijkvoorbeelden vooral voedingsbodem voor aanscherping van verdienmodellen – zoals in de advertentie-industrie – of totale disruptie zoals de kentering die eHealth op dit moment veroorzaakt.

    Een eenzijdig beeld misschien, want data leveren net zo goed inzichten voor interne procesverbetering of een betere klantbenadering met digitale oplossingen. Het gesprek over Big Data zou vooral moet gaan over de enorme potentie ervan: over de impact die de data kunnen hebben op bestaande producten en organisatievraagstukken en hoe die helpen bij een eerste stap richting digitale transformatie.

    Big Data in de huidige praktijk
    De praktijk leert dat er voor ieder bedrijf vier manieren zijn om data in te zetten. Terug naar het realisme in Big Data: de mogelijkheden, voorbeelden en natuurlijke grenzen.

    Excellentie: onderzoek toont aan dat de manager die zich puur door zijn gevoel of intuïtie laat leiden, kwalitatief slechtere beslissingen neemt en dus aanstuurt op een minder goede dienst of experience. Door de harde feiten uit gecombineerde data te benutten, zijn bottlenecks in processen te herkennen en zelfs te voorspellen.

    Zo kunnen Amerikaanse politiekorpsen sinds eind vorig jaar op basis van realtime data schatten waar misdrijven gepleegd zullen worden. Hoewel het al decennia gebruikelijk is om op willekeurige wijze door de stad te patrouilleren – de crimineel weet dan immers niet waar agenten zich bevinden en zou daardoor gehinderd worden – wordt die werkwijze nu losgelaten.

    Het algoritme van de analyticsoplossing PredPol belooft aan de hand van plaats, tijd en soort criminaliteit te voorspellen waar agenten met hun aanwezigheid een misdrijf kunnen voorkomen. In de film Minority Report werd het nog weggezet als voorspelling voor het jaar 2054, nu blijkt dat excelleren met voorspellende data in 2015 al de normaalste zaak van de wereld is.

    Productleiderschap: niet ieder bedrijf hoeft het nieuwe Spotify of Airbnb te worden. Wel moet er rekening worden gehouden met de disruptieve kracht van deze spelers en hun verdienmodellen.

    De leidende positie die Netflix heeft ingenomen heeft het bedrijf deels te danken aan het slimme gebruik van Big Data. De enorme hoeveelheid content die online beschikbaar is, maakt het mogelijk te grasduinen in films en series. Dat surf- en kijkgedrag vertaalt Netflix in dataprofielen waarmee het eigen product zichtbaar verbetert. De data resulteren in aanbevelingen die – voor de kijker – onverwachts bij iemand zijn smaak passen en waarmee het bedrijf je als klant verder bindt.

    De video on demand-dienst staat er bij gebruikers inmiddels om bekend te weten wat de kijker boeit en is in staat je meer te laten afnemen dan je van plan bent. Met de data die het platform continu verbeteren, heeft Netflix de entertainmentindustrie en de groeiende markt van video op afroep opgeschud.

    Het bedrijf heeft zelfs gezorgd voor het nieuwe fenomeen van binge watching, waarbij de kijker urenlang aan de hand van aanbevelingen van de ene aflevering in de andere wordt gezogen. De algoritmes die hiervoor zorgen zijn zo belangrijk dat Netflix een miljoen dollar belooft aan diegene die met een beter alternatief komt.

    Intimiteit: meer te weten komen over de klant is misschien wel de bekendste kans van Big Data. Sociale media, online tracking via cookies en open databronnen maken dat iedere organisatie diensten op maat kan bieden: generieke homepages maken plaats voor met gebruikersdata gepersonaliseerd portals, de serviceverlening verbetert nu er op de juiste plekken in de organisatie een completer plaatje is van de klant.

    Hoe ver die intimiteit gaat, bewijst Amazon. Het bedrijf zegt producten te kunnen bezorgen nog voor deze zijn besteld. Amazon kan op basis van locatie, eerdere bestellingen, zoekopdrachten, opgeslagen verlanglijsten en ander online gedrag voorspellen welke behoefte een klant heeft. De gegevens zouden al zo accuraat zijn dat het winkelbedrijf eerder weet welke producten daarvoor moeten worden besteld dan de klant zelf. Zo nauwkeurig kunnen data soms zijn.

    Risicobeheersing: als data eindelijk goed worden ontgonnen en gecombineerd, maken ze duidelijk welke risico’s bedrijven realtime lopen. Met dank aan Big Data zijn audits sneller en doelgerichter in te zetten.

    Schoolvoorbeeld daarin zijn financiële instellingen. De tienduizenden financiële transacties die deze bedrijven iedere seconde verwerken, genereren zoveel data dat door middel van patroonherkenning fraude snel kan worden opgespoord. Bestel je online een artikel waar bijvoorbeeld een ongebruikelijk prijskaartje aan hangt, rinkelt binnen no-time de telefoon om de transactie te verifiëren.

    En hoewel het kostenbesparende aspect geen verdere toelichting behoeft, is een groot deel van de bedrijven onvoldoende voorbereid. Volgens wereldwijd onderzoek van EY erkent 72 procent van de bedrijven dat Big Data belangrijk zijn voor risicobeheersing, maar zegt 60 procent ook dat zij daarin nog belangrijke stappen moeten zetten.

    De verzekeraar: Big Data als middel om te transformeren
    Van uitlatingen op sociale media tot aan sociaal demografische gegevens, van eigen data over koopgedrag tot aan openbare data als temperatuurschommelingen: hoe rijker de data hoe scherper het inzicht. Wanneer er een organisatievraagstuk ligt, is de kans groot dat data een middel zijn om tot de benodigde transformatie te komen. Met de aanpak wordt daartoe een belangrijke stap gezet.

    • Detectie – Op basis van een Big Data-analyse kan er zicht ontstaan op eventuele behoeften onder de doelgroep. Bijvoorbeeld: Maakt een zorgverzekeraar een digitale transformatie door, dan is het raadzaam profielen van klanten uit een specifieke generatie met onderzoeksdata te verfijnen. Hoe begeven zij zich door de customer journey en welke digitale oplossingen verwachten zij gedurende een contactmoment?
    • Doel- en vraagstelling – Creëer potentiële scenario’s op basis van de data. Bijvoorbeeld: de jongste doelgroep van de verzekeraar groeit op in een stedelijke omgeving. Welk (mobiel) gedrag is specifiek voor deze groep en hoe beïnvloedt dit de de digitale oplossingen waar deze jongeren om vragen? Bepaal waar de databronnen zich bevinden – welke interne en externe databronnen zijn benodigd voor beantwoording van de vragen? Denk daarbij aan interne klantprofielen, maar ook aan open data-projecten van de overheid. Sociale media – uitingen en connecties – in combinatie met demografische kenmerken en postcodegebieden verreiken de profielen. De gegevens vertellen meer over de voorkeuren en invloed die directe omgeving en online media hebben.
    • Controleer de data – Vergeet niet te kijken naar wet- en regelgeving en met name wat privacyregels verbieden. De Wet Bescherming Persoonsgegevens gaat behoorlijk ver: de wet is zelfs van toepassing op gegevens die je tijdelijk binnenhaalt ter verwerking.
    • Analyse – De data worden geïnterpreteerd door analisten. Zo wordt duidelijk dat er een aantoonbaar verband is tussen leeftijd, woonomgeving en gebruik van digitale oplossingen. Bijvoorbeeld: jonge stedelingen zijn digital native en willen een online portal met eHealth-oplossing. Hierin willen zij eigen Big Data uit apps kunnen koppelen voor een beter beeld van de gezondheid.
    • Verankering – Door klantprofielen te blijven monitoren wordt snel duidelijk of er toekomstig afwijkingen optreden. Indien nodig is de transformatie bij te sturen.

    Grenzen aan Big Data
    Het is vooral belangrijk te waken over realisme in het denken over Big Data. Want hoewel data veel antwoorden geven, zit er ook een grens aan de mogelijkheden. Zodra databronnen naast elkaar lopen, kan het zijn dat analyses elkaar beïnvloeden: uitkomsten die correleren blijken achteraf louter op toeval te berusten.

    De mens blijft ook in de toekomst een belangrijke schakel in de kansen die Big Data bieden. Cruciale kennis moet op persoonsniveau behouden blijven, alleen de mens is in staat de juiste interpretatie te leveren. Wat te doen met inzichten is aan hen voorbehouden: een onverwachts dwarsverband kan aantonen dat een groep consumenten een verhoogt bedrijfsrisico oplevert. Wat te doen met deze inzichten als die een groep mensen stigmatiseren? De ethische grens moet altijd worden bewaakt.

    In veel organisaties betekent de komst van data een ware cultuurverandering. Behalve dat managers te weten komen dát er iets verandert, weten zij door die data ook hoe en in welke richting zich iets in de toekomst ontwikkelt. Met Big Data kan de blik weer vooruit.

    Source: Emerce

  • Don't Get Ubered: Rethinking Your Competitive Intelligence Approach

    market intelligence“‘You’ve been Ubered’ will become part of our lexicon to describe industries blindsided by the future,”says Tony Chapman, a Canadian consumer and branding expert, in reference to the challenges that “Big Taxi” is currently facing due to the growing popularity of the Uber rideshare app.


    In fact, every industry now has to face the fact that you “are either Uber, or you’re being Ubered.”

    So, while it is important to look at what your direct competitors are doing when evaluating the competitive landscape for your ecommerce business, it’s even more important to be aware of key trends that will shape where your market is headed.

    To help you create a competitive business strategy that keeps your business agile and adaptable to continuously evolving market conditions and competitors, let’s take a look at what some business strategists and analysts are recommending to startups and entrepreneurs.  After all, most businesses will need to adopt startup strategies in order to remain relevant 10 to 20 years from now. 

    Understanding Your Competitive Landscape

    To understand the big picture of how your business can thrive in the future, it helps to look at your competitive landscape from many different angles. The handy SlideShare presentation from Startup Next (see full presentation above) on market sizing and competitive analysis recommends to evaluate 3 competitive categories in your environment which include:

    1. Direct competitors

    • Big retailers in your industry (e.g. Amazon and Walmart)
    • Other businesses that “solve your unique problem” (including smaller, niche mom and pop shops or even Etsy shops)

    2. Indirect competitors

    • Economic and political trends (e.g. customer privacy)
    • Regulation, government legislation and trade agreements

    3. Future threats

    • Cultural shifts (e.g. usage of mobile devices overtaking desktop computers)
    • Tech innovations (e.g. wearables, 3D printing and virtual reality)
    • Possible changes that your business partners or suppliers are planning

    Once you know who those competitors are, it’s time to evaluate the market opportunities and consumer touchpoints related to each of them.

    A Startup Approach For Evaluating Future Competitors And Market Opportunities

    “We need a different way to represent the competitive landscape when you are creating a business that never existed or taking share away from incumbents by re-segmenting an existing market,” saysSteve Blank, a serial entrepreneur, Stanford professor and author whose book The Four Steps to the Epiphany was influential in the launch of the Lean Startup movement.

    So, if every business now needs to think like a startup in order to avoid “getting Ubered,” why not begin evaluating your market opportunities like a startup right now?

    Image via SteveBlank.com

    To do so, Blank recommends putting your business at the center of your competitive analysis diagram (versus plotting it out on an x, y axis – with your startup at the top right) and then branch out to key adjacent market segments that exist today. This will help to identify where you think your new customers might come from in the future.

    He calls this a “Petal Diagram” and his argument for this approach is that he always thought of his startups as “the center of the universe.” The example diagram above is for a startup education software platform. But the same format could be applied to any retail or ecommerce business.

    So, say your ecommerce business sells specialty sports equipment. You may want to add market segments that you don’t cater to right now but might in the future – thanks to new technologies. For example, advances in 3D printing technologies will allow you to sell to customers in countries to which you don’t currently ship your products. Or, you may be able to work with new distributors or manufacturers with whom you don’t currently have business relationships.

    Blank says that there is no limit to the number of “petals” or adjacent markets that you can map out. And for better visualization, he recommends that the size of the petals can be scaled to the size of the market opportunity for each segment.

    Image via SteveBlank.com

    “The petal diagram is where you [startups an entrepreneurs] develop your first hypothesis about who your customers are,” says Blank.

    While you probably already know who your existing customers are if you are running a thriving ecommerce business, it’s still important to consider that your future customers may look and behave a lot differently.

    And although the purpose of the petal diagram is to show potential investors why they should put their money into a startup, the same diagram can help your leadership team decide where to place their biggest bets and/or to allocate budgets towards R&D for future business growth.

    Tools For Evaluating Your Competition Online


    Image via Pixabay

    Whether you want to size-up your direct competitors, or research current and evolving industry trends, there’s an app or (sometimes free) online tool for that.

    Below are a few suggestions for where you can gather useful competitive intelligence data.

    1. Upstream Commerce
    While you do have to pay for this service, Upstream Commerce offers “automated, real-time intelligence analytics” and insights to help you evaluate competitive pricing, merchandising, promotion and product intelligence – across a number of retail industry categories. The company boasts that its data is easily customizable to help you build detailed, filterable results.

    2. Channel IQ
    Similar to Upstream Commerce, Channel IQ offers competitive intelligence analytics for price monitoring, product intelligence and more. But what sets it apart is that it offers competitive brand intelligence and protection tools. This includes paid search brand protection – allowing you to monitor PPC & keyword “brand-jacking,” so that your competitors can’t “illegally divert your traffic using your registered trade name.”

    3. The Google Keyword Planner tool (or other similar free tools) and Google Trends can help to evaluate consumer demand (via search queries) for your competitors’ products. These tools can also help you evaluate demand for specific products that you or your competitors carry.

    In addition, by signing up to receive Google Alerts via email whenever your competitors are covered by media or bloggers online, you can stay up-to-date on when they launch new products or when the company is receiving positive or negative press.

    4. Social media monitoring platforms
    In addition to looking at online search behavior, it’s important to look at what people are actually saying about your brand – and whether the conversation is moving in a positive or negative direction.

    There are a lot of social media monitoring platforms available for listening to what customers are saying on social networking sites. Some of the more popular ones include: HootSuite, TweetDeck andSysomos. Here’s a helpful blog post from Ryan Holmes, the CEO of HootSuite on how to “listen” to the competition via social media.

    5. eMarketer.com
    While the research, insights and benchmark reports written by eMarketer analysts are crafted with a marketing slant, the company’s ecommerce and mobile commerce reports provide rich, aggregated data from some of the most important research companies that study digital trends today.

    And even if you can’t afford to pay for their full reports, if you sign-up for their free newsletter and search through their public articles, you can access a lot of the most important highlights and charts for use in your own competitive intelligence analysis and strategy development.

    6. Alexa.com and other web analytics tools
    While the insights that you can glean from the free version of the Alexa tool are limited, you can still gain a high-level overview of:

      • how your competitors’ websites rank online (both worldwide or in your own country),
      • how their website performs overall (via graphs highlighting the bounce rate, pageviews per visitor and average visitor’s time spent on the website), and
      • where they may be investing in online marketing efforts via the top “upstream websites” graph (i.e. the top websites that send traffic to your competitors’ websites).

    The paid version of the tool is more robust – giving you further intel into sites linking in, keywords driving traffic to the site and overall website comparisons.

    7. comScore and HitWise
    These tools also do a lot of what the paid version Alexa tool does and are pretty popular for gathering online consumer behavioral data. And like Alexa, both products will enable you to look at traffic on your competitors’ sites – offering variations on how people get there, what popular search terms were used and who those people are. But the way the data is collected is different: comScore data is collected via a panel of users who opt-in to be tracked, and HitWise data is collected based on aggregated ISP user data.

    If you have the budget, then I’d say pay for both of these tools. If not, the free Alexa tool is a great place to start.

    8. Finally, although Mary Meeker is a person (and a very influential one at that) and obviously not an app, her annual Internet Trends Report has become an important destination for anyone who wants to know what’s happening online today – or prepare for will happen in the future. So, I suggest you add her slide deck to your “future threats” intelligence arsenal.

    For a list of even more competitive intelligence tools, check out this post on the Shopify blog.

     

  • Down to Business: Seven tips for better market intelligence

    business-analysisMaking decisions about product and service offerings can make or break your success as a business. Business owners, executives and product managers need good information and data to make the most informed product decisions.

    This critical information about markets, customers, competitors and technology is called market intelligence. Market intelligence combined with analysis provides market insight and allows better decision making.

    Here are seven tips for better market intelligence:

    1. Develop a process: Your ability to harness, manage and analyze good data is vital to your success. Assure you develop a process for gathering, storing and utilizing market intelligence. Take the time to train your team and invest in a robust market intelligence process. It's an investment with an excellent return.

    2. Gather data when you lose: Often when a company loses an order we ask the salesperson what happened and they offer an opinion. It's important to drill down and really understand why you lost an important order. I recall a situation years ago where a salesperson's opinion was very different from what ultimately was the actual reason we lost this large order. Understanding the real reason for the loss assures you are far more likely to choose correct strategies to win the order in the future. Trust, but verify.

    3. Attend trade shows: You should attend trade shows and use them as a fact-finding mission. Trade shows are like one-stop shopping for market intelligence. There are industry analysts, suppliers, customers and industry media all in one location. Use your time wisely to engage with as many people as possible and utilize your listening skills. It's always best to plan ahead for trade shows, to make the best use of your limited time there. Make sure you stay at the hotel suggested by the show organizers. The "show hotel" may cost a little more than other hotels in the area, but you will have far more opportunities to gather information. You can also consider hiring someone, who does not work for your company, to gather information at trade shows, or speak with an industry analyst. This "stealth mode" of gathering market intelligence can provide added benefits.

    4. Take a customer to lunch: Understanding your customers, their challenges and their perception is one of the best ways to gain market insight. Ultimately it is your customer's perceptions that determine your brand positioning. Spending time with your customers, listening to them and acting on these insights, can provide you with an amazing competitive advantage.

    5. Build a database: Data can be hard to find as time moves forward and people leave an organization. It's worthwhile to build a central database of your market intelligence. By indexing this data it becomes easy for your product managers and executives to have access to the best information when making decisions.

    6. Assure you have good data: It takes good, accurate data for the best results; never forget this point. Good data means better decisions. Accuracy can be improved by using multiple sources and considering how any specific source may be biased. Bad information leads to poor decisions. Ensure you are gathering good data.

    7. Train your team: You cannot gather good data that provides market intelligence unless you have a team of professionals that understands how to gain the best market insights. Assure you have a team that is trained not only on how to gather market intelligence, but how to analyze and use the data for better decision making. As an example we offer a product management boot camp that covers this subject in detail, among others.

    Developing market intelligence takes work as well as a robust methodology. It's not a one-time event, but a continuous process. The absence of good data leads to suboptimal decisions. Good data leads to better decision-making and success for your organization.

  • Forrester’s Top Trends For Customer Service In 2016

    It’s a no-brainer that good customer service experiences boost satisfaction, loyalty, and can influence top line revenue. Good service — whether it’s to answer a customer’s question prior to purchase, or help a customer resolve an issue post-purchase should be easy, effective, and strive to create an emotional bond between the customer and the company. Here are 5 top trends – out of a total of 10 – that I am keeping my eye on. A full report highlighting all trends can be found here:

    Trend 1: Companies Will Make Self Service Easier. In 2015, we found that web and mobile self-service interactions exceeded interactions over live-assist channels, which are increasingly used by customers as escalation paths to answer harder questions whose answers they can’t find online. In 2016, customer service organizations will make self-service easier for customers to use by shoring up its foundations and solidifying their knowledge-management strategy. They will start to explore virtual agents and communities to extend the reach of curated content. They will start embedding knowledge into devices — like Xerox does with its printers — or delivering it via wearables to a remote service technician.

    Trend 2: Field Service Will Empower Customers To Control Their Time. 73% of consumers say that valuing their time is the most important thing a company can do to provide them with good service — whether on a call, in a chat, or while waiting for a service technician to troubleshoot and fix their product. In 2016, customer service organizations will better support customer journeys that start with an agent-assisted service interaction and end with a service call. They will explore lighter-weight field service management capabilities, which give customers self-service appointment management capabilities and allow agents to efficiently dispatch technicians and optimize their schedules.

    Trend 3: Prescriptive Advice Will Power Offers, Decisions, And Connections. Decisioning — automatically deciding a customer’s or system’s next action — is starting to be heavily leveraged in customer service. In 2016, organizations will use analytics in a much more prescriptive manner – for example to prescribe the right set of steps for customers or agents to more effectively service customers; to correlate online behavior with requests for service and prescribe changes to agent schedules and forecasts. Analytics will be used to better route a customer to an agent who can most effectively answer a question based on skills and behavior data, or to better understand customer call patterns and preempt future calls.

    Trend 4: Insights From Connected Devices Will Trigger Preemptive Service and Turn Companies Into Services-Based Ones. Companies use support automation to preemptively diagnose and fix issues for connected devices. For example, Tesla Motors pushes software patches to connected cars. Nintendo monitors devices to understand customer actions right before the point of failure. In 2016, the Internet of Things (IoT) will continue to transform companies from being products-based to services-based . Examples abound where companies are starting to monitor the state of equipment via IoT, and realizing new streams of revenue because of their customer-centric focus. To make the business model of IoT work, companies must keep a close eye on emerging interoperability standards: device-to-network connectivity, data messaging formats that work under constrained network conditions, and data models to aggregate, connect with contact center solutions, and act on the data via triggers, alerts to service personnel or automated actions.

    Trend 5: The Customer Service Technology Ecosystem Will Consolidate. The customer service process involves complex software that falls into three main categories: queuing and routing technologies, customer relationship management (CRM) customer service technologies, and workforce optimization technologies. You need to use solutions from each of these three software categories, which you must integrate to deliver quality customer service. We believe that the combination of: 1) mature software categories in which vendors are struggling with growth opportunities; 2) the rise of robust software-as-a-service (SaaS) solutions in each category; 3) rising buyer frustration; and 4) the increasing importance of delivering simpler and smarter customer service makes for ripe conditions for further consolidation to happen in the marketplace, This consolidation will make it easier for buyers to support the end-to-end customer service experience with a single set of vendor solutions.

    Source: customer think

  • Gaat Business Intelligence nu eindelijk belang externe data ontdekken?

    De terminologie “business intelligence” blijkt in de praktijk veelal te maken te hebben met technologie en interne data. Maar gaat business intelligence daar nu alleen over? In mijn gesprekken met BI-professionals valt het me vaak op dat er verwarring heerst over de terminologie. Dus waar hebben we het eigenlijk over? Is het data science, DWH, ERP, ETL, market intelligence, customer intelligence, dashboarding? Wat maakt nu eigenlijk allemaal onderdeel uit van business intelligence?

    Definitie Business Inteligence

    Volgens Wikipedia staat business intelligence voor: “het verzamelen vangegevensbinnen de eigen handelsactiviteit en het proces van gegevens omzetten in informatie, dat vervolgens zou moeten leiden tot kennis en aanzetten tot adequate actie. Business intelligence heeft als doel competitief voordeel te creëren en organisaties slimmer te kunnen laten werken”.

    Soorten informatie

    Over welke gegevens hebben we het als we praten over het creëren van competitief voordeel? Business intelligence zou gericht moeten zijn op hetverzamelen, analyseren en distribueren van informatie (over klanten, concurrentie, marktontwikkelingen en economische, technologische en culturele trends), die van belang is voor beslissingsprocessen teneinde goed onderbouwde operationele en strategische plannen te verkrijgen. Hierbij staat vooral het business perspectief centraal. Daarnaast bestaat er ook nog het technologische perspectief waarbij ICT wordt ingezet om data te verzamelen, te integreren, te analyseren en te distribueren.

    Technologie & Gestructureerde data

    Hoe komt het toch dat het meeste geld wordt geïnvesteerd in technologie en het verzamelen en analyseren van interne en vooral financiële data? Natuurlijk is het logisch dat het management stuurt op financiële resultaten en dat de wetgever inzicht vereist in bepaalde aspecten van het reilen en zeilen van het bedrijf. Maar waarom wordt er toch zo weinig geïnvesteerd in gestructureerd verzamelen en analyseren van ongestructureerde en vooral externe data, die van cruciale invloed zijn op de performance van het bedrijf? Wellicht ligt het antwoord verscholen in het feit dat dit proces vaak niet direct te linken is aan de bedrijfsperformance van het bedrijf. Er zijn meestal geen aparte functies gecreëerd voor dit soort werkzaamheden. Men moet het naast de bestaande werkzaamheden doen en krijgen daardoor te weinig aandacht. Hiermee wordt het belang van dit soort cruciale informatie niet onderkent.

    De ontwikkelingen in de externe omgeving hebben meer impact dan ooit en daarmee neemt ook de druk op bestaande bedrijven en business modellen toe. Een voorbeeld is de opkomst van de zelfrijdende auto. Dit zal vele directe en indirecte effecten hebben. Een direct effect zal zijn dat er banenverlies optreedt bij taxibedrijven, vrachtwagenchauffeurs en rijscholen. Het zal waarschijnlijk ook het delen van auto’s verder stimuleren waardoor er een daling van eigendom van auto’s plaats vindt. Het indirecte effect is dat mensen meer besteedbaar inkomen en tijd beschikbaar hebben. Daarnaast zullen er minder auto-ongelukken optreden, waardoor er weer minder ziekenhuispatiënten zullen zijn en ook weer minder orgaandonoren. Deze ontwikkelingen hebben enorme impact op de overlevingskansen van bedrijven en daarmee neemt het belang van externe intelligence toe.

     

     

      self driving car2

     

    Investeren in externe (ongestructureerde) data

    Bedrijven zouden juist nu meer dan ooit moeten investeren in het relatief onontgonnen deel van business intelligence – het structureren van externe data (lees: market intelligence) - vanwege de steeds sneller veranderende economie mede door het gebruik van informatietechnologie. Door de (financiële) crisis hebben bepaalde herstructureringen versneld plaats gevonden waardoor ook de capaciteit om pro-actief externe veranderingen te monitoren en inzichten te creëren om nieuwe business modellen te ontwikkelen is weggesneden. Hierdoor kennen bedrijven nog minder goed hun markt, de meer algemene bedrijfsomgeving en daarmee de drivers for growth or decline. Een bevestiging van bovenstaande ontwikkelingen is terug te vinden in een significante daling van de gemiddelde levensduur van bedrijven. Het is enerzijds te hopen dat bedrijven met het oog op hun eigen performance meer belang gaan toekennen aan het structureren en analyseren van externe data om te voorkomen dat de niet geziene impact van de externe omgeving hen fataal wordt. Anderzijds is het te hopen, dat er een meer toegankelijke informatietechnologie komt, die een bedrage gaat leveren in het vereenvoudigen van dit proces.

     

    Bron: Ruud Koopmans, RK-Intelligence

     

     

     

  • Gebruik van market intelligence nog steeds uitdaging

    Data is er te over in de hedendaagse marketingomgeving. Het doen van onderzoek naar klanten en con
    currenten was nog nooit zo laagdrempelig en directe feedback uit veel verschillende 

    MI

    gelederen van de organisatie is ook laagdrempelig beschikbaar, nu bijna alle bedrijfsfuncties via CRM en ERP systemen digitaal in verbinding staan. 
    Het is niet voor niets dat Chief Marketing Officers de data-explosie de grootste uitdaging noemen. Naast databeschikbaarheid is het ontbreken van inzicht een probleem. Doordat alles in principe meetbaar lijkt te zijn geworden en er van alle touchpoints data terug de organisatie invloeien komt er veel data beschikbaar. 
    Het mappen van markten is een steeds belangrijkere en noodzakelijke voorwaarde voor Marketing Intelligence. Maar geeft nog geen garantie op inzicht. Een bestuurder van een auto kan niet meer zonder zijn dashboard en alle parameters van de interne en externe omgeving.  Maar tegelijkertijd staan deze data niet op zichzelf. Data kunnen slechts tot waarde worden gebracht als ze geconfronteerd worden met marketinginzicht. Als dat niet gebeurd ontstaat het gevaar dat marketeers steeds tactischer gaan opereren. Gefocused op steeds kleinere deelgebieden, omdat ze het geheel niet meer overzien. De verwerkingscapaciteit is te beperkt, de selectiviteit in waar nu echt op gestuurd moet worden, ontbreekt. Er is kortom geen overzicht. Er zal de komende jaren daarom niet alleen op databeschikbaarheid en fact based kennis van markten maar ook meer gestuurd moeten worden op de indikking van marketing data naar marketing intelligentie. De intelligentie die we bedoelen, vraagt een unieke combinatie van kennis van data, de toepasbaarheid ervan en de toepasselijkheid. Een combinatie van onderzoekskunde en bedrijfskunde, dus. Idealiter ontstaat er bij fact-based marketing een samenwerking tussen onderzoekers die businesswise of marketingwise goed onderlegd zijn en marketeers die affiniteit hebben met het werken met en interpreteren van data. Dat bereikt u niet zomaar, maar vraagt om het doelbewust inrichten van de Marketing Intelligence organisatie die bij uw organisatie past.
    Het opbouwen van een Marketing Intelligence functie binnen een organisatie gaat niet van vandaag op morgen. De organisatie groeit naar zo’n functie toe, vaak ongemerkt en vanuit een toenemende en vaak pas laat geïdentificeerde behoefte aan externe kennis en inzichten. 
     
    BI-kring redactie
  • Google wil telecomdiensten aanbieden

    Ggoogleoogle is van plan telecomdiensten aan te gaan bieden in de Verenigde Staten, via de mobiele netwerken van de telecombedrijven Sprint en T-Mobile US. Dat meldden ingewijden rond Google.

    Google zou al afspraken gemaakt hebben met Sprint en T-Mobile US over het gebruik van hun netwerken. Als Google inderdaad de telecommarkt opgaat, zal het bedrijf de concurrentie aangaan met aanbieders die mobieltjes met de Android-software van Google verkopen. De grootste telecombedrijven in de Verenigde Staten zijn AT&T en Verizon Communications, gevolgd door Sprint en T-Mobile US.

    Het is nog niet duidelijk hoe breed de telecomdiensten van Google zullen worden, wat de kosten zijn en wanneer de verkoop begint. Mogelijk kan Google eerst op beperkte schaal in een aantal Amerikaanse steden met de dienstverlening beginnen

    Automatiseringsgids, 22 januari 2015

  • Heeft de nieuwe ISO normering impact?

    Een onderwerp dat veel meer aandacht krijgt in de 2015 normering is de oriëntatie op de externe context waarin de organisatie opereert. Welke eisen en verwachtingen hebben de stakeholders van de organisatie? Interne en externe risicofactoren moeten worden benoemd en daarbij moet inzichtelijk worden gemaakt, hoe de processen leiden tot acceptabele risiconiveaus. Waarom is de externe omgeving voor een organisatie nu zo van belang. Het is natuurlijk zo dat de klant uiteindelijk bepalend is wat er geproduceerd wordt en dat concurrenten invloed hebben op de manier waarop een bedrijf zicht organiseert maar ook positioneert. Maar momenteel kunnen we toch wel spreken van een revolutie waardoor de oriëntatie op de externe context en beheersing van risico’s extra belangrijk is geworden. Klik hier om het hele artikel te lezen.

    Revolutie

    Tijdens de industriële revolutie vond er een omschakeling plaats van handmatig naar machinaal vervaardigde goederen. Deze revolutie heeft een enorme impact gehad op productieprocessen, de inzet van kapitaal (mens) en de prijs van producten. Deze revolutie heeft ook invloed gehad op de manier waarop we onszelf organiseren maar ook de levensduur van bedrijven. De opkomst van automatisering heeft dit proces nog verder versterkt.

    Tijdens de laatste grootste revolutie, de digitale revolutie, vindt er een ware omslag plaats. Consumenten worden producenten (bijv. van energie). Daarnaast wordt ons gedrag steeds meer vast gelegd door middel van data en daardoor blijken onze voorkeuren steeds meer en beter te voorspellen (bijv. Emmy-winnende televisieseries zoals “Orange is the new Black”).

    Business modellen

    Business modellen van bedrijven komen hierdoor onder druk te staan en moeten significant veranderen om te overleven. De gemiddelde levensduur van bedrijven in de top 500 van de Amerikaanse beurs was in 1939, ten tijde van de grote crisis, 75 jaar. In 2011 is deze al gedaald tot 18 jaar. Naar alle waarschijnlijk zal deze de komende jaren nog verder dalen. De digitale revolutie zet bedrijven met hun bestaande business modellen steeds verder onder druk. Bedrijven zullen versneld moeten veranderen om te kunnen anticiperen op de snel ontwikkelende technologie om te overleven.

    ISO

    Organisaties die willen blijven voldoen aan de eisen van ISO zullen elementen en vooral de externe context, de kansen en bedreigingen, de eisen van stakeholders en geldende wet- en regelgeving expliciteren moeten maken en gebruiken voor de risicoanalyse en het benoemen van de beheersmaatregelen. Om te voldoen aan de nieuwe ISO norm zullen organisaties uiterlijk eind 2018 moeten overstappen naar ISO norm: 2015.

    Voordeel van de nieuwe norm

    1. De nieuwe normen bieden kansen voor een betere aansluiting van de strategie en ’governance’ van de organisatie op basis van de externe context en de implementatie in de operatie.

     

    Bron: Ruud Koopmans (RK Intelligence), 15 januari 2015

     

  • How data analytics changes marketing strategies in the near future

    Marketing analyticsOver the course of last year, we saw the marketing industry monitor a number of emerging trends including wearables and facial/voice recognition, and experiment with new tools and techniques such as VR and augmented reality. 

    For example, in October, we a saw a campaign from New Zealand health insurance company Sovereign that won an International ECHO Award for integrating a wide range of datasets into a campaign which drove customer signup, lead generation and sales. They integrated new data streams from activity trackers, gym networks and grocery stores to reward customers for healthy behavior. This new data also powered timely, tailored notifications across platforms. Notwithstanding the large undertaking, Sovereign was able to improve health outcomes and increase policy renewals, reversing a negative trend for the company.

    In 2018, I expect that these features will evolve in ways that will help marketers better understand businesses, consumers, and competitors. Here are a few predictions for what we can expect to see this year: 

     It’s all about relationshi s based on Truth, Results and Trust – 1:1 Relationships at scale

    Data is a horizontal that cuts across all of marketing, yet to date many organizations (some very large organizations) are not yet data-driven. They are realizing that today’s technology and processing power enables organizations to use data informed techniques to enhance customer experience. They’re realizing that to be competitive they must pivot toward data-driven marketing techniques including data-informed design and messaging to personalize offers that resonate with individual customers based on their individual needs and interests. Look for deep-pocketed advertisers like P&G to play catchup with a vengeance in the data-driven marketing space.

    Data Quality, Brand Safety, Transaction Transparency and Transaction Verification

    We all know that massive amounts of data can be overwhelming. And of course, transforming data into actionable insight is the key to maximizing marketing ROI and enhancing the customer experience. Yet there is too much spurious data that is dangerous and costly. While it is a cliché, “garbage in equals garbage out” still rings true. This has been particularly evident in the digital advertising space with bad actors using bots to mimic human behavior. 

    Additionally, some algorithms have gone awry in the digital ad space causing potential harm to brands by placing ads in undesirable spaces. Client-side marketers cannot tolerate fraud or waste. Consequently, the supply-side has been injured as client-side marketers began reducing their digital ad buys. Look for supply-side solution providers to increase their efforts to attack such problems utilizing tools and techniques like massive processing engines, blockchain technology, better machine learning and collective concentrations of power like trade associations that bring organizations together to collectively identify and address issues that organizations struggle to solve on their own. 

    Timing and the Propensity to Buy

    While algorithms may be able to predict the next site at which a potential customer will land, they haven’t yet fully incorporated the ages old data-driven marketing technique of correctly timing a compelling offer. Look for leading solution providers to utilize more machine learning and AI to better incorporate timing into their ‘propensity to buy’ calculations.

    Third Party Data and the Burgeoning Duopoly 

    There is a balance of power issue developing in the digital ad space as Google and Facebook continue to gain dominate market share momentum in the digital ad spend space (presently estimated at a combined 84%!). Look for “rest of the world” market forces to develop innovative solutions to ensure that competition and innovation thrives in this space. 

    Responsibility 

    The data and marketing industry thrives on innovation and the technological advancement that allows us to build connections with our customers based on truth, results and trust. Acting responsibly is paramount to building brand loyalty. As more hacks and breaches occur, this large problem will attract entrepreneurs seeking opportunities to solve such problems. While it is very disturbing the see large organization like Equifax fall victim to a data breach, our data and marketing industry is stocked with brilliant minds. Look for highly encrypted cloud-based security vaults to surface. And I suspect that while many organizations may feel reluctant to house their data in the cloud, look for them to realize that it is far more secure than keeping it “in house.”

    Education will Evolve

    While a bachelor’s degree is a critical requirement for many marketing jobs, the marketing degree hanging on the wall can’t keep marketers up to speed with the ever-increasing rate of change in our data-driven marketing industry. IoT, big data, attribution woes, and integrating online and offline touchpoints, identity across platforms, channels and devices, emerging technology and techniques are all examples of daunting challenges. 

    In 2018, expect to see a surge in continuous talent-development programs, not just from academics, but from practitioners and commercial solution providers that address new challenges every day. Look for powerful video-centric platforms like DMA360, a crowdsourced platform for solution providers to bring their solutions to the market which incorporates social media techniques to curate the content through user upvotes. We all know that knowledge drives the competitive edge!

    Author: Tom Benton

    Marketing analytics(chief executive officer at the Data & Marketing Association)

  • How To Use Competitive Intelligence To Drive Email ROI

    Naamloos

    Marketers who use competitive intelligence tools enjoy an average of three times more email generated revenue than those who don’t, according to a recent report by The Relevancy Group.

    Yet one of the most common questions I'm asked when I present a client with a competitive analysis is: "There's no point in doing this more than once a year, right?"
    Think again. There’s a lot you can -- and should -- do with competitive intelligence tools to drive ROI on a regular basis. Here's a short list to get you started:
    1. Learn from your competitor's tests, not just your own. We all talk about testing, but did you realize that you can double your efforts by gleaning ideas from competitors? If you see what works for them, you can test it for yourself. And if you see something that doesn’t work, you can deprioritize that test, and put more lucrative efforts first.
    2. Identify key subject lines, phrases, creative, etc. Chances are, if it engages your competitors’ audience, it will probably engage yours, too. It’s worth sorting through creative examples to get ideas for what you can test next.
    3. Quickly see what is new in marketing. It can be difficult to find the newest innovations, tools, or techniques that can drive your results and make your job easier. A competitive analysis tool can help you keep tabs on your competitors so you can identify when they are doing something that you can’t. Think about all the technologies we now use that were virtually unknown 10 years ago: real-time suggestion engines, dynamic image generation, and more. Just by asking, “how did they do that?”, you might uncover that your competitors are using a new tool or technique that you could implement to help drive your ROI too.
    4. Prove you need a bigger budget. A competitive tool can help you see exactly how much effort your competitors are putting into their email channel. Based on those competitive insights, you can prove that you need a bigger budget to keep up.
    5. Track benchmarks. It’s helpful to understand how you stack up against competitive benchmarks, such as read rates or share of voice. It can be even more helpful to know how those metrics change in different seasons and during different holidays. This can support your budget requests or even potentially help you restructure your program.
    Clearly there is a lot you can learn from your competitors. Once a year definitely won’t cut it if you want to keep your program fresh and continue to drive ROI. Instead, consider a two-part approach:
     
    Weekly and/or monthly: Make quick dives into the competitive tools you use to see creative changes on a regular basis. This is strictly to generate ideas that you can use to update your own testing grid. It will help you with the top three items above. A frequent check-in will keep this from taking too much time, because you’ll have enough familiarity with the competitive landscape to scroll through quickly.
     
    Bi-monthly or quarterly: Keep your more formal reporting to a less frequent schedule. This type of reporting is important because it will help you with the last two items on the list above. But it is the part that doesn’t change often. Quarterly may work, or you may decide that there are certain timeframes that are so important to your business that you need to adjust your reporting schedule around them. Even with adjustments, a formal reporting schedule shouldn’t be more often than every other month.
     
    Source: mediapost.com, November 16, 2016
  • Localization uses Big Data to Drive Big Business

    There’s growing interest in using big data for business localization now, although the use of customer data for optimal orientation of busi

    localization

    ness locations and promotions has been around for at least a decade.

    There’s growing interest in using big data for business localization now, although the use of customer data for optimal orientation of business locations and promotions has been around for at least a decade.

    In 2006, the Harvard Business Review declared the endof big-box retail standardization in favor of catering to customers’ local and regional tastes, fostering innovation, and – not incidentally – making it harder for competitors to copy their store formats by changing up the one-size-fits-all approach. A decade later, analytics are affordable for businesses of all sizes, giving smaller players in a variety of industries the ability to localize as well.

    An example of early localization of items sold comes from Macy’s. Executive search firm Caldwell Partners describes the department-store chain’s vast localization project, which began in the mid-2000s to differentiate store inventories for customer preferences, beginning in markets such as Miami, Columbus, and Atlanta. This strategy has helped Macy’s remain profitable despite ongoing major declines in department-store sales in recent years.

    Localization for stronger consumer appeal, better product offerings

    In hospitality, hotel chains now use localization strategies to compete with locally owned boutique hotels and with Airbnb rentals that promise a “live like a local” experience.

    Visual News reports that Millennials’ tastes and preferences are driving this trend. These younger travel enthusiasts want a unique experience at each destination, even if they’re staying in properties owned by the same hotel brand.

    Hospitality Technology notes that today’s customer profile data gives hotel chains a “360 degree view of customer spending behavior across industries, channels, and over time,” for more precise location orientation and targeted marketing.

    In fact, any consumer-facing business can benefit from using local-market data. GIS firm ESRI has described how individual bank branches can orient their loan offerings to match the needs and risk profiles of customers in the immediate area. Other elements that can be localized to suit area customers’ tastes and spending power include product prices, menu items, location hours, staffing levels, décor, and product displays.

    Localization for more effective marketing

    Outside the store itself, localization is a powerful tool for improving the return on marketing. By using detailed data about local customer behavior, retailers, restaurants and other businesses can move from overly broad promotions to segmented offers that closely align with each segment’s preferences.

    In some cases, this type of marketing localization can reduce expenses (for example, by lowering the total number of direct-mail pieces required for a campaign) while generating higher redemption rates.

    Localization of marketing efforts goes beyond cost savings to the establishment of customer loyalty and competitive advantage. Study after study shows that consumers expect and respond well to offers based on their preferences, but companies have been slow to provide what customers want.

    An international study reported by Retailing Today in June found that 78% of consumers make repeat purchases when they receive a personalized promotion, and 74% buy something new. Despite this, the study found that less than 30% of the companies surveyed were investing heavily in personalization.

    A similar 2015 study focusing on North American consumers, described by eMarketer, found that more than half of the consumers surveyed wanted promotions tailored to their product preferences, age range, personal style, and geographic location. That study found that although 71% of the regional retailers in the survey say they localize and personalize promotional emails, half the consumers said they got promotional emails that didn’t align with their preferences.

    Clearly, there’s room for improvement in the execution of localized marketing, and businesses that get it right will have an advantage with customers whose expectations are going unmet right now.

    Smart localization and orientation involve understanding the available data and knowing how to use it in cost-effective ways to give customers the information they want. It also involves rethinking the way businesses and consumers interact, and the role geography plays in business.

    Localization and careful audience targeting may be the keys to business survival. A 2013 Forrester report proclaimed that in the digital age, “the only sustainable competitive advantage is knowledge of and engagement with customers.”

    With so much power of choice in the hands of consumers, it’s up to retailers, restaurants and other businesses to earn their loyalty by delivering what they want in real time, no matter where they’re located.

    Author: Charles Hogan

    Charles Hogan is co-founder and CEO at Tranzlogic. He has over 20 years of experience in fintech, data analytics, retail services and payment processing industries. Follow on twitter @Tranzlogic)

  • Making Content Marketing Work

    Making Content Marketing Work

    Speeds & feeds. “Hero” shots. Print ads. Product placement. Really expensive TV advertisements featuring celebrity endorsements.

    Pitching a product and service back when those phrases dominated marketing and advertising discussions seems very quaint today.

    In an era where the incumbent media companies are seeing their audiences fragment across a host of different devices and online sites (including the online versions of the incumbent media providers), those old school techniques are losing their juice.

    Consumers no longer want a spec sheet or product description that tells them what the product or service is — they want to be shown what the product or service can do for them. And they want to see how other actual people — just like them — use the product or service.

    As if that wasn’t tough enough, today’s consumers can spot inauthentic pitches from a mile away. They will happily share your lack of authenticity with millions of their closest friends on Facebook, via Twitter etc., etc. and etc.

    Content marketing has emerged in the past three years as a practice that allows marketers to maintain the balance between richer, deeper information, or content, about their products and doing it authentically.

    Like so many things in life, describing what content marketing is, and what it can accomplish, is way easier than actually doing content marketing successfully.

    In one of Gartner’s earlier docs on content marketing, my colleague Jake Sorofman exhorted marketers to “think like publishers.” Sound advice but many marketers find that to be difficult. To-date, while many marketers are getting much better at sourcing and distributing the kind of content elements for their needs, measuring content marketing’s contribution is not easy. But it can be done.

    Using content analytics gives content marketers insight into how their efforts are being received by consumers, providing the kind of objective measures that previous generations of marketers dreamed of having. Jake’s most research round-up on content marketing has some timely examples of companies which have wrestled with the content marketing challenge and are realizing the value of not merely finding, creating and distributing content, they’re also focusing on using all the tools available to amplify their efforts. The story about IKEA’s work in the area is particularly interesting.

    Yep, times have changed and it’s a much more complex field than marketing used to be. Digital, content, social, mobile marketers are jobs titles that didn’t exist 15 years ago, for the most part. The good news is that the tools and techniques those new job titles require are increasingly available.

    By Mike McGuire | April 6, 2015 |

  • New kid on the block in Market Intel

    radar ontvangers

    Market intelligence neemt een vlucht. Nu ondernemingen hun interne informatiehuishouding in toenemend mate in orde hebben

    gaat de aandacht (opnieuw?) uit naar de informatievoorziening met betrekking tot de markt van ondernemingen. Opnieuw? Ja, opnieuw!

    Als sinds de ’60er jaren staat het onderwerp midden in de belangstelling maar onder invloed van informatietechnologische ontwikkelingen werd het steeds naar de achtergrond gedrongen door aandacht voor de interne optimalisering van de informatiehuishouding. Executive informatiesystemen (een term uit de jaren ’80) leidde tot BI en BI tot DWH, ETL, Reporting en score carding. De toename van data op social media, het net en de mogelijkheden op het gebied van ongestructureerde data – data mining maar ook machine learning voedden nu opnieuw de aandacht voor toepassing van technologie bij het beter kennen van de bedrijfsomgeving. Het belang daarvan is dus niet veranderd maar de mogelijkheden nemen wel toe.

    Drie jaar geleden werd Hammer, market intelligence opgericht met als doel bedrijven van market intel te voorzien met gebruikmaking van moderne data technologie. Egbert Philips (Director van het in Arnhem gevestigde Hammer); “Wat betreft management informatie zou er minstens zo veel aandacht moeten zijn voor het kennen en doorgronden van markt en bedrijfsomgeving als voor de interne prestaties. Dit zou niet afhankelijk moeten zijn van technologische mogelijkheden. De ontwikkeling van data science en big data technologieën maken het wel mogelijk market intelligence beter en efficiënter in te richten. Daar richten we ons met Hammer op. We willen een partner zijn voor bedrijven die hun markten structureel willen kennen en doorgronden. Informatie technologie blijft daarbij een middel maar wel een heel belangrijk middel.”

    Hammer is weliswaar een jonge onderneming maar bepaald niet nieuw in het veld. De oprichters zijn reeds jarenlang actief in market intel en de toepassing daarvan in onder ander strategische planning vraagstukken. Hammer ondersteunt echter ook meer tactische beslissingen. Vraagstukken met betrekking tot pricing, sourcing/inkoop, het kiezen van distributiepartners, productontwikkeling en business development kunnen niet goed worden beantwoord zonder input van marktinformatie.

    Eind november organiseert Hammer een klantevent. Wanneer u geïnteresseerd bent stuur dan een e-mail naar info@hammer-intel.com

    http://www.hammer-intel.com

     

  • Predictive analytics in customer surveys: closing the gap between data and action

    Schermafbeelding 2018 01 24 om 10.02.51Customer surveys are a conduit to the voice of the customer (VoC). However, simply capturing survey data is no longer enough to achieve better results.

    When used appropriately, customer surveys can help companies more effectively identify new markets with the most potential for success, create a data-driven pricing strategy, and gauge customer satisfaction. However, capturing survey data is only the first step.

    Companies must analyze and act on survey data to achieve their goals. This is where predictive analytics comes into the picture. As illustrated in Figure 1, companies using predictive analytics to process survey data achieve far superior results across several key performance indicators (KPIs), compared to those without this technology. 

    Since happy customers are more likely to maintain or increase, their spend with a business, growth in customer lifetime value among predictive analytics users signals improvement in customer satisfaction rates. Similarly, companies using this technology also attain 4.6 times the annual increase in overall sales team attainment of quota, compared to non-users. This correlation indicates that predictive analytics can help companies convert survey data into top-line revenue growth.

    Use of predictive analytics to forecast and predict the likelihood of certain events, such as potential sales or changes in customer satisfaction, requires companies to have a comprehensive view of customer and operational data. Most organizations don’t struggle with a lack of survey data given the wealth of insights they glean through the activities noted above. Instead, they are challenged with putting this data to good use. Indeed, findings from Aberdeen’s May 2016 study, CEM Executive's Agenda 2016: Aligning the Business Around the Customer, show that only 15% of companies are fully satisfied with their ability to use survey data in customer experience programs.

    How to Use Predictive Analytics to Maximize Your Performance

    Data shows that Best-in-Class firms (see sidebar) are 20% more likely to be fully satisfied with their use of survey data when conducting customer conversations. A closer look at these organizations reveals that they have 59% greater adoption rate when it comes to predictive analytics, compared to All Others (35% vs. 22%).

    For any organization not currently using predictive analytics to analyze survey data, this technology holds the key to significant performance improvements. As such, we see that with a mere 35% adoption rate, many top performers could use predictive analytics to do even better.

    One mistake companies make when adopting new technologies is assuming that simply deploying the technology will result in sudden – and recurring – performance improvements. The

    situation is no different with predictive analytics. The fact of the matter is, if an organization is looking to increase customer lifetime value or profit margins, the organization must design and execute a well-crafted strategy for utilizing predictive analytics in conjunction with customer surveys.

    On a high level, predictive analytics can be used in two ways:

    1. Systematic analysis: Organizations can establish an analytics program to measure and manage survey data on a regular basis. These programs are aimed at accomplishing certain goals, such as gauging customer satisfaction levels at regular intervals to correlate changes in customer satisfaction rates with changes in the marketplace and overall business activities.

    2. Ad-hoc analysis: Companies can also analyze survey data on an as-needed basis. For example, a company could conduct a one-time analysis of the potential customer spend in a new market to decide whether to enter that market.

    It’s important to note that companies can use both systematic and ad-hoc analysis. Use of systematic analysis allows organizations to continuously monitor their progress towards ongoing performance goals, such as improving customer satisfaction. Ad- hoc analysis, on the other hand, allows companies to use the same analytical capabilities to answer specific questions that may arise.

    Having outlined the two general ways companies use predictive analytics, it’s also important to share the two general types of processes that can be used to produce such analysis:

    1. Statistical analysis: Predictive analytics can provide decision maker across the business with insights into hidden trends and correlations. For example, companies conducting statistical analysis can identify how use of certain customer interaction channels (e.g. web, email, or social media) correlates with customer satisfaction rates as revealed through surveys. This, in turn, allows companies to identify which channels work best in meeting (and exceeding) the needs of target clientele.

    2. Modeling: This second type breaks into two sub-categories:

    1. Forecasting: Companies can use historical and real- time survey data to forecast the likelihood of certain outcomes. For example, a company curious about the potential sales uplift to be expected from a new market would survey potential buyers in the area and ask about their intent to buy and preferred price-points. The forecasting capability of their predictive analytics platform would then allow the company to forecast potential sales numbers.

    2. Predicting: This analysis refers to analyzing historical and real-time survey data to estimate a specific result that might have already happened, might happen currently or will happen in the future. For example, an organization might decide to build a model that helps identify customer spend in a specific market. This might start by developing a model for past sales results where the model produces a result similar to the actual results observed by the company. Having ensured the accuracy of the model, the organization can now use it to predict current and future sales based on changes in the factors built into the same predictive model.

      The difference between forecasting and predicting is that the former only looks at future events or values whereas the latter can look at future, current or historical events when building models. Also, the former requires relying on already available past data (e.g. snow blower purchases) to make forecasts whereas the latter allows companies to predict a certain outcome, in this case snow blower purchases by looking at related factors influencing this result, including recent temperatures, change in average income, and others.

     

    Conclusions:

    Companies have many ways to capture survey data, however only 15% are fully satisfied in their ability to use this data. Predictive analytics helps companies alleviate this challenge by answering business questions designed to improve performance results.

    However, it’s imperative to remember that the statistical insights gleaned through predictive analytics, as well as the models predictive analytics can produce, will only yield results if companies act on the intelligence thus acquired. Don’t overlook the importance of coupling analysis and action. If you are planning to invest in this technology (or have already invested but seek to improve your results), we recommend that you make bridging the gap between data and action a key priority for your business. 

    Author: Omer Minkara

    Source: white paper Aberdeen Group (sponsored by IBM)

  • Retailers are using big data for better marketing

    Durjoy-Patranabish-Blueocean-Market-IntelligenceToday, the customers’ expectations are growing by leaps and bounds and the credit goes to the technology that has given ample choices to them. Retailers are leaving no stone unturned to provide better shopping experience by adapting to analytical tools to catch up with the changing expectations of the consumers. Durjoy Patranabish, Senior Vice President, Blueocean Market Intelligence divulged Dataquest about the role of analytics in retail sector. 

    How retailers are using big data analytics to drive real business value?
    The idea of data creating business value is not new; however, the effective use of data is
    becoming the basis of competition. Retailers are using big data analytics to make variety of intelligent decisions to help delight customers and increase sales.

    These decisions range from assessing the market, targeting the right segment, forecasting demand to product planning, and localizing promotions. Advanced analytics
    solutions such as inventory analysis, price point optimization, market basket analysis, cross-sell/ up-sell analytics, real-time sales analytics, etc, can be achieved using
    techniques like clustering, segmentation, and forecasting. Retailers have now realized the importance of big data and are using it to draw useful insights and managing the customer journey.

    How advanced clustering techniques can be used to predict better purchasing behaviors in targeted marketing campaigns?
    Advanced clustering techniques can be used to group customers based on their historical purchase behavior, providing retailers with a better definition of customer segmentation on the basis of similar purchases. The resulting clusters can be used to characterize different customer groups, which enable retailers to advertise and offer promotions to these targeted groups. In addition to characterization, clustering allows retailers to predict the buying patterns of new customers based on the profiles generated. Advanced clustering techniques can build a 3D-model of the clusters based on key business metrics,

    such as orders placed, frequency of orders, items ordered or variation in prices. This business relevance makes it easier for decision makers to identify the problematic clusters that force the retailers to use more resources to attain a targeted outcome. They can then focus their marketing and operational efforts on the right clusters to enable optimum utilization of resources.

    What trends are boosting big data analytics space?

    Some of the trends in the analytics space are:


    „„1. The need for an integrated, scalable, and distributed data store as a single repository will give rise to the growth of data lakes. This will also increase the need for data governance.
    „„2. Cloud-based big data analytics solutions are expected to grow three times more quickly than spending on on-premises solutions.
    „„3. Deep learning which combines machine learning and artificial intelligence to uncover relationships and patterns within various data sources without needing specific models or programming instructions will emerge
    4. „„ The explosion of data coming from the Internet of Things will accelerate real-time and streaming analytics, requiring data scientists to sift through data in search of repeatable patterns that can be developed into event processing models
    „„5. Analytics industry will become data agnostic, primarily having analytics solutions focused around people and machine rather than on structured and unstructured data
    6. „„ Data will become an asset which organizations can monetize by selling or providing value added content.

    What are your views on ‘Big Data for Better Marketing’. How retailers can use analytics tools to be ahead of their competitors?

    Whether it is to provide a smarter shopping experience that influences the purchase decisions of customers to drive additional revenue, or to deliver tailor made relevant real-time offers to customers, big data offers a lot of opportunities for retailers to stay ahead of the competition.


    Personalized Shopping Experience: Data can be analyzed to create detailed customer profiles that can be used for micro-segmentation and offer a personalized shopping experience. A 360 degrees customer view will inform retailers how to best contact their customers and recommend products to them based on their liking and shopping pattern.
    Sentiment analysis can tell retailers how customers perceive their actions, commercials, and products they have on offer. The analysis of what is being said online will provide retailers with additional insights into what customers are really looking for and it will enable retailers to optimize their assortments to local needs and wishes.
    Demand Forecast: Retailers can predict future demand using various data sets such as web browsing patterns, buying patterns, enterprise data, social media sentiment, weather data, news and event information, etc, to predict the next hot items in coming seasons. Using this information, retailers can stock up and deliver the right products
    and the right amount to the right channels and regions. An accurate demand forecast will not only help retailers to optimize their inventory and improve just-in-time delivery but
    also optimize in-store staffing, thus bringing down the cost.
    Innovative Optimization: Customer demand, competitor activity, and relevant news & events can be used to create models that automatically synchronize pricing with inventory levels, demand and the competition. Big data can also enable retailers to optimize floor plans and find revenue optimization possibilities.

    Source: DataQuest

  • Surprise! There's More To The Future Of Marketing Than Just Big Data

    CXU7qL3WcAAr9jkBig data has come to advertising. Marketers use computers and algorithms to collect and analyze reams of data on customer behavior. That information is funneled into sophisticated software that can, in real time, adjust online ad design to the preferences of specific browsers in order to maximize the chance of a sale.

    Big data can be and is powerful. But marketers must avoid becoming overly dependent on it. There are important complexities within the human character that no computer can comprehend and there's more to marketing than math. It takes a level of creativity that only humans possess to interpret big data and make it meaningful.

    Big Data Alone Doesn't Mean Bigger Sales

    Research from my company, Adobe, shows that companies that embrace creative marketing are 3.5 times more likely to see their annual sales revenue grow by 10 percent or more compared with companies that exclusively rely on big data. Buyers' behavior isn't always rational. People make strange decisions that defy neat algorithmic understanding. Often, customers are not simply looking for the highest-quality product for the lowest possible price. Indeed, the burgeoning field of behavioral economics is revealing on an almost daily basis how irrational consumers can be—and how seemingly irrelevant factors can influence purchasing decisions. Savvy marketing adapts to these nuances.

    Take, for instance, an ad commissioned by The Economist. It marketed an online-only subscription to the magazine for $59, a print-only subscription for $125, and a combined print-and-web subscription for... $125. Those offerings defy logic. Why let subscribers pay the same amount for print-only as for both print and Web? But this pricing scheme was designed to make the combination more appealing. It worked. Subscriptions increased, and The Economist secured its position as one of the few profitable newsweeklies.

    Choice Results

    There are similar quirks when it comes to choice. A strictly rational marketing approach would call for providing customers with as many options as possible in order to maximize the chance that they'll see something they like, but there is such a thing as too much choice. People can feel overwhelmed—and decline to buy anything at all.

    One study compared a group of customers selecting from 24 types of gourmet jams with a group choosing from just six. The former were 90 percent less likely to make a purchase. Marketing based solely on big data may fail to account for such behavior.

    Selling Intimacy

    Finally, big data can never replace the very human work of establishing an individual, emotional connection with a customer.

    A landmark 1993 Harvard Business Review article coined a term for this connection: "customer intimacy." Researchers looked at a protocol at Home Depot that encouraged clerks to spend as long as necessary with guests to make sure they found the right product, even if the sale in the end was minimal. Home Depot doesn't just sell products. By spending so much time with each customer, the retailer is also selling the information and service its shoppers are demanding. That kind of personalized attention engendered fierce loyalty to the Home Depot brand—loyalty that translates to profit. Tellingly, when Home Depot replaced knowledgeable employees with part-timers, it hurt sales—and Home Depot is trying to fix the problem by, guess what, having clerks once again spend more time with customers.)

    Marketing In 2025

    Even in a world of supercomputers and powerful software, human creativity still has a crucial role to play in marketing. The advertising strategies of the future will incorporate the best of both worlds, coupling sophisticated technology with old-fashioned intuition. Such an approach may require companies to fundamentally rethink core parts of their business.

    Take, for instance, the design of brick-and-mortar stores. Despite the rising popularity of e-commerce, customers still value the physical retail experience. Eighty-five percent actually prefer brick-and-mortar-stores to online retail. People don't want to choose between online and offline shopping. They want both. Research indicates that 72 percent of millennials research and shop their options online before going to a store or the mall.

    There's a real hunger for a quality in-store experience. Yet most retail businesses don't deliver one. For years, they've stuck with the same uninspired flyers and television commercials—as well as the same old layout, with row after row of barely organized products. Creative firms could enhance the in-store experience to drive sales. For instance, they could develop a smartphone app that allows users to navigate stores and compare features of specific products. That's the best of both worlds.

    The marketing of the future will couple the best of big data with the best of human intuition. That's the formula for establishing long-term customer loyalty in an ever-more competitive marketplace.

    Source: ReadWrite

  • The role of big data in financial inclusion

    Data-driven insights can be invaluable for businesses – and improve financial inclusion in a world where 2.5 billion people still have no access to financial services

    data drivenRelevance and personalisation are delivered by data-driven insights; this is one of the new truths of business. As a mathematician and statistician this appeals to my inner geek; I enjoy numbers, patterns and formulas. For many years it wasn’t a “cool” thing to admit but now in the 21st century the geeks have inherited the earth and technology has penetrated every part of our lives. Thanks to the ubiquity of technology those same numbers, patterns and formulas that I learnt to love are opening new doors and delivering data insights that are changing the face of business and commerce.

    Patterns and predictions can influence every part of our lives. What particularly interests me is how insights can deliver impact and influence in real world applications. I see the power of data every day, because we use it to inform our own business decisions at MasterCard. We use it as a route to identify areas for growth, address concerns, to understand our audiences and to drive social good on a global scale.

    Of course, we are not alone; the use of data to inform business decisions is nothing new. What is different today is that ubiquitous technology I referenced earlier. Whether it’s looking for the best deal on your next holiday at home on your tablet, buying a coffee with your smartphone or simply keeping abreast of news in other parts of the world via Twitter or Reddit, the world has changed and this has opened an opportunity to connect with people and businesses globally, in a far more relevant way. This new deeper understanding is driving growth – and more businesses need to seize the moment and be part of the new data driven opportunity.

    Post-recession there has been a seismic shift in consumer spending habits and for businesses; this has amplified competition across all sectors. Insights into consumer spending, derived from data, are increasingly valued by businesses in order to understand their competitors, differentiate themselves and ultimately drive growth. We no longer live in a world where business can create products, services, destinations and experiences that are driven by an identification of demand. Make it matter to me, make it work for me, make it for me is the mind set of our global consumer and in return we not only need to listen but we need to help make this a reality. While it is valuable to look at broad demographics, to see an audience by geography or age group; in today’s world we are all a “segment of one” or smaller and to engage me you have to listen, learn, personalise and reinvent; while balancing this with my right to privacy.

    We can do that at MasterCard; every day our network handles payments for two billion cardholders and tens of millions of merchants in over 210 countries around the world. These transactions or payments generate real-time data on a global scale, available faster than regular government statistics. The data we see is not personally identifiable data but by understanding what it tells us, we can create highly sophisticated macro-economic indicators that can inform business, retailers, governments and more can be used to help inform their business decisions.

    One of the fastest growing areas of our business is how we use aggregated data on spending patterns found in the payments we process to help our partners and customers build more relevant and tailored solutions, products and experiences. For businesses, who excel at capturing and analysing their own customer data, our macro-level data insights applied to their data can illustrate what happens next, outside of their company, across the market. By sharing these insights, we can give them an edge over their competitors, helping them to provide the optimum experience for their customers.

    A good example is some research MasterCard recently commissioned into travel trends. As a globally connected business, we wanted to look at how consumers spend across 135 cities around the world. MasterCard’s fourth annual Global Destination Cities Index found that London was the world’s top travel destination for consumer spending, with a projected 18.7 million international visitors in 2014 which equates to an £11bn injection into London from the tourism industry alone. Our report showed us the countries from where these visitors were coming, identified the travel corridors that link cities and much more. This kind of information is valuable to a huge range of businesses as well as local authorities and governments who can plan for an influx of tourists within a city as well as enabling business and government to plan for the year ahead.

    Alongside this we can also look at how global events give us an opportunity to identify insights. This year the World Cup took Brazil by storm and we were able to draw some particularly interesting trends and analysis. In advance of the big events many expected all spending to rise, particularly for items connected to the event. Instead, through MasterCard’s data and analytics we were able to see a spike in spending in Brazil on groceries and a drop in spending on luxury goods. This kind of economic insight can be of significant value to brands with larger single items costs, by enabling them to better plan promotional windows.

    Data-driven insights are also vital for our own commercial success and they inform how we drive growth. MasterCard’s vision of a world beyond cash is well publicised and the benefits for business, government and all of us are endless our data and insights help us understand the way we pay and the barriers around the shift from cash and paper to not only deliver against this vision but also to enhance the customer experience. One of the many things we have identified is that while there are unifying themes, like safety and security and ease and speed there are also local considerations that we are able to take into consideration when building next generation products and solutions around the world.

    Insights generated from data demonstrate an opportunity to grow our business among small and medium-sized enterprises (SMEs) too. Globally, some 80% of SMEs don’t have access to full banking services, even in developed economies. Our simplify commerce platform can help an SME set up from scratch in 15 minutes, enabling them to start selling over the internet and accept card payments. Without big data on the needs of SMEs we may never have identified this opportunity for growth.

    Data-driven insights are not just valuable for business; it also has an exciting role in driving social good. As president of international markets, I manage around 60% of MasterCard’s business worldwide, so the expansion of e-commerce, m-commerce and innovative payment technology driving growth in emerging markets is extremely important to me. We use data-driven insights to understand how people spend money around the world and identify where people are falling through the gaps in the financial system. For example, we know that 2.5 billion people are still without access to financial services – this is unacceptable.

    There is a perception that this is just a developing world issue, but that is not the case. Our data shows us there are 93 million people in Europe that don’t have access to basic bank accounts or financial products. Take Italy for example, where 25% of Italians are underserved and don’t have access to basic electronic payment. Families are left crippled in a world without access to sustainable financial services whether it is savings, social credits or insurance. Our data insights help us determine how we support people that are experiencing different aspects of financial exclusion in every market that we operate. We can work to engage these marginalised audiences in each market, helping people improve their lives by providing opportunities for access to essential financial services. Governments in these countries can also use this information to build better financial systems and encourage people to become more included in these systems. This marks a step forward for good in the way big data is used.

    The value of data-driven insights as a significant source of competitive differentiation to MasterCard will come as no surprise. But, I believe big data has the potential to transform people’s lives for good globally and the capability to drive social change, supporting transformational innovation and growth in developing countries. There is a need to drive financial inclusion across the world and data can play a part in making a real difference, the opportunity is there for the taking we just need to embrace the change.

    Ann Cairns is president of international markets at MasterCard

    Source: The Guardian, January 20, 2015

  • The virtue of doubt: succesful analysts generate deliverables that are being acted upon by decision-makers

    When considering how to select, educate, train and professionally develop the market intelligence officer and analyst of the future, the first thing that came to my mind was an aphorism: “begin with the end in mind”. When attempting to answer this essay’s competition question, properly defining the end may well be imperative prior to beginning. My first step towards an answer thus starts with a question: how to define the end in intelligence?

    twijfel

    The desired end state in intelligence for the purpose of answering our question is defined as:

    “Intelligence is to significantly contribute to the successful definition and execution of a company’s strategy”

    For intelligence deliverables to contribute in this way, the first requirement the deliverables need to meet is that they have to be used by decision-makers. The latter are being defined as the principals of the intelligence work that themselves operate outside the intelligence community. When is intelligence work being acted upon? In my experience, when the work is of convincing quality in at least two dimensions. Excellence is required both in content and in persuasiveness of delivery. This allows us to draw an interim conclusion. The analyst of the future needs to be selected, educated, trained and professionally developed both to deliver work of great content as well as of strong persuasiveness towards the relevant decision-makers. So far, this is nothing new. The requirements to deliver great content in a persuasive manner are timeless. The addictive beauty of intelligence work is hidden in a single word: it is about the future. What analyst can meet these requirements in the future? For this, we need to look forward.

    Intelligence permanently updates the company’s future market environment maps

    Prior to diving into what the future may look like, it has a merit to briefly review the role of intelligence. A

    simple equation may be helpful:

    Threat = Competency + Intent + Surprise

    In this equation, threat is analyzed in terms of how a competitor may hurt our company’s interests. Competencies are defined as the competitor’s (in-) tangible assets. Think of number and size of competitor factories or brands they own. The intent is to summarize the objective of the competitor’s leadership. What plans do they really have to grow their business, given the assets they control today and may have access to tomorrow? The term surprise indicates the power of surprise a competitor may use. What competitor surprises could our intelligence analysts imagine and convince our company’s leadership to prepare for, before they materialize? This equation not only holds true for a single competitor but may also be applied to markets, suppliers, countries etc. A market intelligence function concurrently solves multiple threat equations at any one time. I believe this to be the core role of intelligence in corporate strategy definition and execution. Intelligence is to monitor and forecast all threats a company may face by adding up all relevant threat equations. In so doing, intelligence builds a holistic and permanently actualized picture of the market environment. This picture metaphorically serves as a map for decision-makers to real-time navigate in the market environment and where needed both to change course and adjust the sails. In so doing, intelligence is fully integrated in planning cycles like the plan-do-check-act loop. Given this role, a successful analyst not only permanently updates the map but also ensures the corporate ship is navigated based

    on the latest map.

    In summary, I see two key requirements for the analyst of the future: he knows how the relevant market environment looks in the future and he knows how to persuade the future executive to integrate his intelligence deliverables as input in her decision-making.

    The future may be unpredictable but people do not change

    In this section we are moving back to the future. We face two questions. The first question is: how do we anticipate the future market environment to look like? The second question concerns the professional needs of the future decision-maker. These questions are interlinked. Answering the question what the future market environment for a business will look like will by implication lead to attributes that a successful business leader will need. These attributes will thus determine how she is best served her intelligence needs.

    I feel tempted to describe the generic future market environment. Words like rapidly changing, globally connected and volatile come to my mind. These terms, however, may look wise but are neither actionable nor specific enough for the individual company to be useful. Moreover: I am an analyst not a futurologist. Suffice it to say that the successful analyst of the future needs to offer foresight on the future market environment regardless of what future emerges. Similarly, the successful analyst of the future needs to serve and persuade the future decision-maker regardless of who she happens to be. The latter analyst’ task may well be easier than the task to forecast the future market environment. Different market environments may bring different leaders to

    executive positions, but people as such do not fundamentally change. Basic human psychological needs like recognition, association or power are not affected by changing currency exchange rates or shifting economic and political power. For an analyst to become persuasive, grasping the psychology of executive power is imperative, regardless of this morning’s level of the Dow Jones Index.

    In summary, we thus need to educate and train the analyst both for any future market environment that may emerge and for serving any decision-maker. Before starting with an analyst’s education, we clearly first need to select those analysts that naturally are best suited for such future role. Let us now consider some analyst’ selection criteria.

    Market intelligence is a smart people’s business and offers a rewarding career

    In my experience, intelligence is rarely a numbers war. Throwing loads of people at an intelligence puzzle is not by definition conducive to solving it rapidly and elegantly. Reginald V. Jones - head of British Scientific Intelligence during World War II – corroborates my view when he writes how an intelligence team is to be set up [Jones, 1978]:

    “The size of the staffs […] should be kept as numerically small as possible, and that quality was much the most important factor”

    His counterpart in WW II British Naval Intelligence, Admiral John Godfrey even more passionately advocates the relevance of the quality of the analyst [Macintyre, 2010]:

    “It is quite useless, and in fact dangerous to employ people of medium intelligence. Only men with first-class brains should be allowed to touch this stuff. If the right sort of people cannot be found, better keep them out altogether.”

    When selecting first class intelligence officers, what quality do I believe we need to look for? In line with common human resource practices, I distinguish functional and behavioral competences. A critical functional competence in selecting an analyst I believe is intellect. Being smart, however, is not enough. When selecting an analyst, I also look for research skills, knowledge of business administration, language skills and increasingly for consultancy skills. An analyst that has no feel how to connect to a decision-maker may be highly useful in a team, but the smaller the team, the more every team member has to able to act independently. An analyst needs to be thinking on her feet, i.e. being always prepared, even when she unexpectedly happens to share the elevator with their decision-maker customer. In job interviews I ask candidate analysts their elevator pitch. The candidate better has to score an instant sell to stay in the recruitment process.

    In behavioral competences, I think of qualities like curiosity, frustration tolerance, persuasiveness, humility, perseverance, courage, discreetness and innovativeness. There are two more distinctive qualities in a candidate I look for. The first is the degree of xenocentric thinking that the candidate demonstrates. In analysis, putting the other party central is critical when aiming to understand their current steps and predicting their next steps.

    The final required quality is the candidate’s passion for intelligence work – embracing the puzzle game as a mean to have a rewarding career. I intentionally write career, not job. Intelligence is one of these crafts where experience matters more than proportionally. In inte

    lligence the 10,000 hour rule certainly applies: no mastery without extensive practice. As long as artificial intelligence cannot replace analysts, we need to select analysts that are willing to dedicate their lives to the cause. In return an analyst should not only be entitled to an intellectually rewarding profession but also to

    a decent financial package. The best employers have developed a dual career ladder, where becoming a thought leader is equally conducive to receiving higher job grades as becoming a leader of ever larger numbers of staff. There is a catch though. I try to avoid selecting Mammon’s mercenaries. If your pay matters more than your puzzle, try Wall Street.

    Educate and train to doubt, not to know

    There is a critical difference between chess and cheese. In chess, rules are fixed and adversaries change. In the cheese business, rules – paradigms if you wish – are fluid and adversaries tend to remain the same. Even when today’s paradigm is that cheese is mainly sold through supermarkets, tomorrow’s rule may be that it is sold through e-commerce. As a cheese producer, you are often still up against the same competitors, only the channel has changed. Why does this matter to how to educate
    analysts?

    If cheese would be like chess, the focus in analyst education would be on a one-off learning of business’ paradigms and related functional tools. Provided we have selected the best and brightest minds as analysts, education would only require a relatively modest effort. Listening skills and reasonably regular class attendance would already take our analyst a long way.

    Tools and traits to learn in intelligence include classics like the intelligence project cycle. The cycle consists of brief reception, project definition, collection, analysis, reporting and filing and finally customer debrief. For each of the phases of the cycle, both functional and behavioral analyst skills need to be educated. Think of project management skills, of OSINT- and/or HUMINT-collection courses, of courses in analysis tools, in the psychology of bias and in strategy, of a course in slide writing and story-lining, of a course in consultancy skills including personal effectiveness and last but not least of a mandatory training in ethics and applicable compliance. The above list is not exhaustive and should be tailored to the job needs of the future analyst rather than the other way around.

    The real effort to educate and train a future analyst would have to be spent on training by doing, with the analyst in training over time independently taking on ever more complex problems. In so doing, the analyst in training gradually builds a mental library of multidimensional intelligence problems and related solutions. In a sharp mind, problems and their solutions are unconsciously filed as patterns. The 10,000+ hour experienced analyst has an instant grasp of a complex new problem, because he intuitively recognizes the new problem as matching a mentally stored pattern. Moreover, the pattern informs him on the script that is likely to enroll for this type of problem and thus the most appropriate course of action to take. Problem solving through pattern recognition built upon experience indeed is a source of power in chess [Klein, 1999].

    Chess, however, is not cheese. The fundamental difference may be summarized in a single word: doubt. The cheese analyst for sure needs to take the education and training summarized above. It is imperative that all business strategy tools need to be mastered, next to intelligence-specific skills. The 10,000 hour rule certainly applies as experience makes a proven difference [Mandel, 2014]. That is all similar and true for chess and cheese.

    There is a difference though. In contrast to the expert fire fighters and the master chess players discussed by Klein, the cheese business as we know it has neither Laws of Physics nor fixed rules as its basis. The most fundamental asset that education and training may deliver a future analyst in the cheese business9 is that single intelligence virtue: doubt.

    This is where foxes and hedgehogs enter. The ancient Greek poet Archilochus has written a line that has inspired today’s forecasting professionals such as Silver and Tetlock [Silver, 2013]:

    “The fox knows many little things, but the hedgehog knows one big thing”

    Silver uses this line to distinguish between poor and above-average forecasters. We already discussed above that the analyst of the future should be selected, educated and trained to be successful in predicting market or competitor future moves in any future, serving any future decision-maker.

    It seems a limited stretch to apply the apparent critical behavioral skills of an above-average forecaster to a successful future intelligence analyst. Silver coins his poor forecasters hedgehogs:

    “Hedgehogs […] believe in Big Ideas – in governing principles about the world that behave as though they were physical laws and undergird virtually every interaction in society”

    In cheese intelligence, a Big Idea could be that cheese is yellow – with the analyst conveniently overlooking that white mozzarella cheese is amongst the fastest growing cheese types. Another Big Idea could be to only look for competitors in traditional sales channels.

    The above-average forecasters are positioned as foxes [Silver, 2013]:

    “Foxes […] believe in a plethora of little ideas and in taking a multitude of approaches toward a problem. They tend to be […] tolerant of nuance, uncertainty, complexity and dissenting opinion.”

    The fox embraces her multitude of small sources and views. Doing so calms her fundamental doubt about how fast paradigms in her business over time shift. She realizes that such shifts may render her library of historically obtained patterns dangerously obsolete and by implication render her script-related historically successful courses of actions no longer adequate. That is good thinking! It raises a question though. How to educate and train a smart fox?

    The question is whether a smart fox may result from education and training in the first place. Behavioral economist and Nobel Prize laureate Daniel Kahneman is skeptical of the role of education in topics such as this where psychology seems to matter [Kahneman, 2011]:

    “Teaching psychology is mostly a waste of time”

    Still, the final result of our efforts to select, educate, train and develop analysts remains unchanged. The future analysts need to deliver intelligence that is to significantly contribute to the successful definition and execution of a company’s strategy. We have identified that proper selection, education and training may to a large degree contribute to build that very analyst. We cannot be sure yet, whether these efforts have seeded enough doubt in the analyst’s mind to embrace foxiness. The latter thus is our key attention point in us developing our analysts.

    Developing analysts’ self-confidence makes them radiate and receive trust

    We have so far defined the role of intelligence. Intelligence outputs seamlessly need to integrate into decision-making processes. We subsequently reviewed the selection and curriculum of our future analysts. What is left to do? We now still need to ensure that things move beyond thinking and into execution and to ensure a well-trained analyst remains foxy. It is the intelligence educator’s and practitioner’s responsibility to ensure both that the intelligence output from all analysts – junior and senior - is indeed being fruitfully used by the corporate decision-making for whom it is intended – and that the analysts become and remain foxy.

    Both educators and senior intelligence practitioners should develop the analysts of the future to make the ultimate connection: building trust with the company’s management as a catalyst and safe-guard to ensure our intelligence deliverables are considered critical input in their decision-making. We need to repeat it: intelligence is a people’s business. A decision-maker first needs to trust the analyst prior to trusting the analyst’s work. I have seen foxiness contribute to credibility. More than once I witnessed management being relieved that intelligence took a profoundly non-dogmatic, non-partisan position, highlighting pros and cons and taking multiple perspectives. Objectivity is a rare currency in top management echelons. Harvest as intelligence staff its value and coach future analysts to do the same.

    For a foxy analyst to receive trust it is critical that she first trusts herself. Senior managers tend to be equipped with a remarkable sense for discovering their advisors uncertainty. Even when they may not say it, they may still - perhaps even unconsciously - think: if you don’t trust yourself, how can I trust your judgement on this intelligence topic?

    Moreover, uncertainty is hard to mask. It reveals itself in subconscious body language, in tone-of-voice and in the very phrases used to present an intelligence deliverable. To prevent personal uncertainty to reduce or annihilate the impact of what factually is a good intelligence brief, developing an analyst requires to develop his self-confidence; his trust in his own judgement. This requires on-the-job coaching, praising every piece of good work and constructively suggesting improvements to less-than-perfect deliverables.

    As a food company, we at FrieslandCampina see building self-confidence in our future leaders as part of a broader human resource initiative called “nourishing leadership”. First we aim to select well-educated talent. Once selected, we offer a challenging and inspiring on-the-job and formal training and development setting. In our company senior leaders are held accountable for creating a climate in which our talents for the future thrive, regardless of whether their career path leads them to people leadership or to thought leadership. Self-confidence builds with exposure. So we let the analysts deliver their good work to higher management by themselves as soon as they are up to it. When they know they get the chance to practice senior management advisory whilst their boss will back them up and support them in the meeting where needed, they also build flying hours in executive consulting. Success and experience breeds more self-confidence. This usually leads to a virtuous development cycle.

    To nurture the best connections between analyst and managements I believe starts with managing expectations at both interfaces: that of the (junior) analyst and that of senior management. Senior management needs to understand that good intelligence work can reduce but not remove uncertainty. Analysts needs to accept that when their intelligence output and possibly their recommendations for courses of actions are ignored by line managers it doesn’t mean their work was of poor quality or even worse that rejection reflects distrust in their capabilities.

    In my experience, inevitable and occasional rejection of work is easier to accept when the larger perspective is kept in mind. No matter how wonderful intelligence is as a profession, the role of intelligence in the greater company should not be exaggerated, not even by its most passionate practitioners. Intelligence analysts are only those employed to analyze and predict the business environment with a truly open and doubtful mind, bringing home the key messages to senior decision-makers with a keen and emphatic view of their customer’s agendas and feasible management choices.

    Executives anyway very well know that at all times the buck stops, when it stops, at their management table. Today’s and tomorrow’s best intelligence analysts, however, ensure that very table does not stand in their company’s offices.

     

    Author: Erik Elgersma

    Source: Linkedin posts

  • Top 10 trends BI

    Kijk hier eens voor de mooie trends.
     
    Bron: BI landschap, 24 december 2014
  • Voorspelmodellen voor efficiëntere en effectievere campagnes

    voorspelmodel grafiek

     

    voorspelmodellenWie benader je om conversie te verhogen of churn te verlagen? Over welke kanalen verdeel je je marketingbudget? Ontwikkel je een dure brochure of ga je voor een goedkoper kanaal als e-mail? Vragen die je als marketeer stelt bij het opstellen van een nieuwe campagne. Een voorspelmodel geeft antwoordenDe vijf W’s van de marketing

    Bij het opstellen van een nieuwe campagne wordt vaak gebruik gemaakt van de vijf W’s:

    • Waarom wil ik deze campagne voeren?
    • Wie wil ik benaderen?
    • Wat moet ik ze aanbieden?
    • Waar moet ik ze benaderen?
    • Wanneer moet ik ze benaderen?

    In de praktijk blijft het lastig om op al deze vragen een goed en onderbouwd antwoord te geven. Het waarom is vaak wel duidelijk. Het gaat vaak om het realiseren van verkoop of het voorkomen van churn. Maar wie je het beste kan benaderen om conversie te verhogen of churn te verlagen, met welke boodschap en via welk kanaal is niet altijd helder. Zonde, want je wil je campagne zo effectief en efficiënt mogelijk inrichten.

    De praktijk

    Neem een bedrijf dat producten en diensten verkoopt aan consumenten op basis van een abonnement of contract. Denk aan een leaseauto, telefoonabonnement, energiecontract of een verzekering. Op een gegeven moment loopt het contract af en rijst de vraag: gaat de klant het contract (stilzwijgend) verlengen, of loopt hij naar de concurrent?

    Nu kan je iedere klant, waarvan het contract afloopt benaderen met de vraag of ze alsjeblieft klant blijven. Uit het verleden is gebleken dat maar 20% van deze klanten ook daadwerkelijk opzegt. Gevolg is dat je:

    • onnodige kosten maakt. 100.000 brochures of telefoontjes kosten aanzienlijk meer dan 20.000;
    • slapende honden wakker gaat maken, met de kans dat je klanten denken “Hé, ik kan mijn contract beëindigen, laat ik eens kijken of er ergens anders een leuke aanbieding is”;
    • je klanten lastig gaat vallen met onnodige communicatie en je voortaan in de spam inbox beland.

    Om dit te voorkomen wil je weten welke klanten een verhoogde kans hebben om hun contract op te zeggen. In deze situatie is het zeer verstandig om een voorspelmodel in te zetten en hiermee je targetgroep te bepalen.

    Het model: bepaal je high risk klanten

    Met een voorspelmodel schat je de kans dat een klant waar het contract afloopt, deze ook daadwerkelijk opzegt. Je gebruikt zoveel mogelijk beschikbare informatie als (historische) klant, marketing-, sales- en/of webdata als input voor het model.

    Allereerst identificeer je de klanten die in het verleden uit contract liepen en kijkt of zij hun contract hebben beëindigd.
    Daarna bepaal je met het model welke (klant)kenmerken samenhangen met het uiteindelijk wel of niet opzeggen. Concreet zijn dit persoonskenmerken als geslacht, inkomen, het aantal jaren dat iemand al klant is en of er ook andere producten worden afgenomen.

    Vervolgens geeft het model alle geselecteerde kenmerken een gewicht mee. Deze gewichten worden geprojecteerd op de klanten waar de aankomende tijd het contract van afloopt. Zo krijg je per klant de kans dat hij je gaat verlaten in een bepaalde periode.

    Creëer inzichten voor content en kanaal

    Nu bekend is wat de high risk klanten zijn, kan je de targetgroep voor de campagne afbakenen. Je weet dus Wie je moet benaderen. Met een geavanceerder duurmodel kan je ook bepalen Wanneer je iemand moet benaderen.

    De uitkomsten van het model zet je vervolgens in om je targetgroep in te delen in verschillende risicogroepen. Zo bepaal je per groep met Welk marketingkanaal je ze benadert. Klanten met het hoogste risico om ze kwijt te raken wil je persoonlijk benaderen met een goede aanbieding. Klanten met een lager risico kan je dan via een goedkoper kanaal benaderen zoals e-mail. Op deze manier zet je je marketingbudget gericht in.

    Wat wordt de boodschap van je campagne? Dat bepaal je door de kenmerken van je targetgroep in kaart te brengen. Gaat het om jonge of oude mensen? Zijn het relatief vaak vrouwen? Zijn ze al lang klant? Deze informatie is bruikbaar voor de bepaling van de content van je campagne. Doordat je de boodschap gericht afstemt op je doelgroep, worden je campagneresultaten beter!

    De voordelen van voorspelmodellen

    Of het nu gaat om campagnes gericht op prospects, upsell of churn, door gebruik te maken van voorspelmodellen worden je campagnes efficiënter en effectiever. Ze helpen je bij de invulling van de W’s en resulteert uiteindelijk weer in een hogere Return on Investment. Voorspelmodellen:

    • helpen je bij het vaststellen van je targetgroep (Wie)
    • bieden handvaten voor effectieve content van je campagne (Wat)
    • leiden tot een efficiëntere inzet van je marketingbudget via de marketingkanalen (Waar)
    • kunnen inschatten op welk moment je iemand moet benaderen (Wanneer)

     

    Door: Olivier Tanis

    Bron: 2Organize.nl

  • Wat maakt een company profile een krachtige tool?

    Company profileWat is een company profile of bedrijfsprofiel nu eigenlijk en waar wordt het door bedrijven voor gebruikt? 

    Wat is een company profile?

    In een company profile wordt op een systematische wijze een analyse gemaakt van een bedrijf. Onderwerpen die hierbij aan de orde komen zijn:

    • Algemene feiten van het bedrijf
    • Historie van het bedrijf
    • Strategie
    • Identiteit
    • Doelen
    • Competenties
    • Product portfolio
    • Organisatiestructuur
    • Financials

    Maar waar wordt het nu door bedrijven voor gebruikt?

    In het bedrijfsprofiel worden zaken in kaart gebracht die u als organisatie zelf kunt beïnvloeden zoals bijv: bedrijfsstrategie, concurrentiestratie, R&D strategie, product portfolio of imago. Verschillende afdelingen binnen organisaties maken gebruik van company profiles voor verschillende doelen:

    Afdelingen Doel
    Strategie

    Ontwikkelen organisatiestrategie, concurrentiestratie of product portfolio; Beoodelen bedrijfsovername

    Marketing Bepalen imago, positionering
    Sales Voeren van acquisitiegesprekken; voorbereiden proposaltrajecten; realiseren cross-selling bij klanten
    R&D Leveranciersanalyse, ontwkkelen R&D strategie door inzicht in technologie van concurrentie
    Inkoop Leveranciersanalyse
    Finance

    Beoordelen financiele betrouwbaarheid

    Ervaring heeft uitgewezen dat het gebruik van company profiles de effectiviteit van bijv een acquisitiegesprek, cross-selling, bedrijfsovername, leveranciersselectie vergroot en verkleint de kans op verkeerde keuze voor leveranciers. Wilt u meer weten of een voorbeeld te downloaden klik dan hier.

    Bron: RK-Intelligence.nl, Ruud Koopmans, 4 November 2016

  • What Can Retailers Do To Elude Extinction?

    ExtinctHere's what you didn't learn in school about the disruption affecting retail today. A recent article by consultant Chris H. Petersen, "Seven disruptive trends that will kill the bigstock-Extinct-150-79929610-copy'dinosaurs of retail'" discussed the fate of "25 retail dinosaurs that vanished in the last 25 years" which was the subject of an Entrepreneur article. Those retailers included giants such as Circuit City, Comp USA, Blockbuster, Borders, and Tower Records, companies which literally dominated their category or channel. Others named in the article were retail innovators in their own right until new disruptors outgunned them. The point is that neither longevity, size, or specialization guarantee retail survival today. So how can today's retailers avoid being extinguished by current disruptive innovations?

    Disruptive innovation refers to any enhanced or completely new technology that replaces and disrupts an existing technology, rendering it obsolete. (Picture how we went from the Model T to the KIA; from giant mainframes to personal computers; or from fixed-line telephones to cellphones/smartphones).

    Disruptive innovation is described by Harvard Business professor Clayton Christensen as a process by which a product or service takes root initially in simple applications at the bottom of a market and then relentlessly moves up market, eventually displacing established competitors.

    Today's major disruptive retail trends have led to the rise of the consumer, the rise of technology to help retailers best serve the consumer while wrestling with competitive forces, and the demise of "the old way of doing business."

    I. The Consumer.

    Evolving, innovative, disruptive technology has led to consumer-dominated behavior that reaches across many channels. As we know, today's consumer now shops any time and everywhere using a variety of helping tools.

    The consumer is capable of having a personal, seamless experience across their entire shopping journey to explore, evaluate and purchase, tempered by how retailers DO business, provide service, deal with their competition, etc.

    * The consumer journey starts online, although stores remain a destination for experience.

    What can retailers do? The successful retailer of the future needs to not only master online and offline, but how to connect with the consumer across many touch points, especially social media.

    * Mobile juggernaut. The latest stats show that there are now more cell phones in use than people on this planet. Smartphones now exceed 4.5 billion. Mobile is the majority and will be the preferred screen for shopping.

    What can retailers do? Retail survivors must optimize for mobile engagement, and also broadcast offers and connect with consumers wherever they are. The store of the future will not only have beacons to connect, but to track traffic via mobile as well.

    * Stock availability / Virtual aisle / Endless shelf. More than 50 percent of consumers expect to shop online and see if stock is available in store.

    Omni channel consumers now fully realize that stores can't begin to stock every model, style and color. While consumers can see hundreds if not thousands of products in store, they know that there are millions online.

    What can retailers do? The survivors are literally creating a seamless experience between online, store and mobile apps so the consumer can "have it their way" anywhere, anytime.

    * Consumer experience still rules. Consumer experience still needs to come down to senses: Tactile, visual, and psychological.

    What can retailers do? Virtual dressing rooms, better in-store experiences, and adoption of new disruptive technology to address and satisfy these issues.

    * Personalization of products and services.

    What can retailers do? New survivors are emerging with "mass personalization" opportunities to custom tailor your clothes or curate your personal wardrobe assortment and send it to you.

    * Social Connections and the influence of the opinions of others. Social has become a primary source of research and validation on what to buy. Today's consumers are 14 times more likely to believe the advice of a friend than an ad.

    What can retailers do? Today's major brands are giving much more attention to and spending more dollars on social media than traditional media.

    II. Technology

    Disruptors share the common purpose to create businesses, products and services that are better -- usually less expensive and always more creative, useful, impactful -- and scalable.

    What can retailers do? Put into use as soon as possible disruptive technology solutions such as price and assortment intelligence, behavioral economics, customer experience analytics, predictive analytics, and more to help understand, meet, and outgun the competition and service the customer.

    A Note on Predictive Analytics.

    Dr. Christensen subscribes to predictive analytics as, "the ability to look at data from the past in order to succeed in new ways the future." Predictive analytics solutions, the capability to forecast consumer purchase trends in order to sell the most products at the best prices at any given time are coming on strong.

    Bottom Line For Your Bottom Line

    There's never been a time of more disruptive change in retail. Retailers who are the most adaptable to change -- and not the strongest nor more intelligent of the species -- will be the ones to survive.

    It's a case of keeping yourself on top of the tsunami of change through the mastery of today's and tomorrow's new disruptive technologies.

    *Thanks to Chris H. Petersen, PhD, CEO of Integrated Marketing Solutions, a strategic consultant who specializes in retail, leadership, marketing, and measurement.

    Source: upstreamcommerce.com, February 8, 2015

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