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3 items tagged "marketing strategy"

  • 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 Nike And Under Armour Became Big Data Businesses

    960x0Like the Yankees vs the Mets, Arsenal vs Tottenham, or Michigan vs Ohio State, Nike and Under Armour are some of the biggest rivals in sports.
     
    But the ways in which they compete — and will ultimately win or lose — are changing.
     
    Nike and Under Armour are both companies selling physical sports apparel and accessories products, yet both are investing heavily in apps, wearables, and big data.  Both are looking to go beyond physical products and create lifestyle brands athletes don’t want to run without.
     
    Nike
     
    Nike is the world leader in multiple athletic shoe categories and holds an overall leadership position in the global sports apparel market. It also boasts a strong commitment to technology, in design, manufacturing, marketing, and retailing.
     
    It has 13 different lines, in more than 180 countries, but how it segments and serves those markets is its real differentiator. Nike calls it “category offense,” and divides the world into sporting endeavors rather than just geography. The theory is that people who play golf, for example, have more in common than people who simply happen to live near one another.
     
    And that philosophy has worked, with sales reportedly rising more than 70% since the company shifted to this strategy in 2008. This retail and marketing strategy is largely driven by big data.
     
    Another place the company has invested big in data is with wearables and technology.  Although it discontinued its own FuelBand fitness wearable in 2014, Nike continues to integrate with many other brands of wearables including Apple which has recently announced the Apple Watch Nike+.How Nike And Under Armour Became Big Data Businesses
     
    But the company clearly has big plans for its big data as well. In a 2015 call with investors about Nike’s partnership with the NBA, Nike CEO Mark Parker said, “I’ve talked with commissioner Adam Silver about our role enriching the fan experience. What can we do to digitally connect the fan to the action they see on the court? How can we learn more about the athlete, real-time?”
     
    Under Armour
     
    Upstart Under Armour is betting heavily that big data will help it overtake Nike. The company has recently invested $710 million in acquiring three fitness app companies, including MyFitnessPal, and their combined community of more than 120 million athletes — and their data.
     
    While it’s clear that both Under Armour and Nike see themselves as lifestyle brands more than simply apparel brands, the question is how this shift will play out.
     
    Under Armour CEO Kevin Plank has explained that, along with a partnership with a wearables company, these acquisitions will drive a strategy that puts Under Armour directly in the path of where big data is headed: wearable tech that goes way beyond watches
     
    In the not-too-distant future, wearables won’t just refer to bracelets or sensors you clip on your shoes, but rather apparel with sensors built in that can report more data more accurately about your movements, your performance, your route and location, and more.
     
    “At the end of the day we kept coming back to the same thing. This will help drive our core business,” Plank said in a call with investors. “Brands that do not evolve and offer the consumer something more than a product will be hard-pressed to compete in 2015 and beyond.”
     
    The company plans to provide a full suite of activity and nutritional tracking and expertise in order to help athletes improve, with the assumption that athletes who are improving buy more gear.
     
    If it has any chance of unseating Nike, Under Armour has to innovate, and that seems to be exactly where this company is planning to go. But it will have to connect its data to its innovations lab and ultimately to the products it sells for this investment to pay off.
     
     
    Source: forbes.com, November 15, 2016

  • The Power of Descriptive Analytics: How Your Business Can Thrive in a Data-Driven World

    The Power of Descriptive Analytics: How Your Business Can Thrive in a Data-Driven World

    Organizations are recognizing that data can be a powerful business asset, and are investing in data analytics to provide this valuable tool. According to research, more than 95% of all organizations today incorporate data initiatives into their business strategies. However, most businesses falter when it comes to effective and efficient uses of data. Descriptive analytics, the most common type of data analytics, is used by savvy businesses to help figure out the “what” at the core of their data.

    Descriptive analytics is the foundational data analysis tool that can simplify and reveal the basic meaning entrenched in data sets, and it is transforming the commercial world. Descriptive analytics can be used for everything from recognizing consumer trends to determining effective annual budgets. 

    In this article, we will examine what descriptive analytics is and how it works, including the three main types of descriptive analytics. We will then reveal strategies for using descriptive analytics to make better decisions across all sectors. 

    What Is Descriptive Analytics?

    The most simple form of data analytics, descriptive analytics is most often employed to uncover simple answers about data. Questions such as “what happened” or “what is this about” are answered efficiently through descriptive analytics, making it a powerful tool for revealing trends, patterns, and errors. Descriptive analytics shares a simple description of the data on hand. 

    Raw data needs to be processed to be used effectively; first, it must go through the descriptive analytics process. This process can be used with current or past data and is often set up to show a business’s progression toward set objectives. Descriptive analytics can provide valuable data and insights for business owners, which can allow them to make better decisions for establishing a future path of success, even against the looming threat of a recession. 

    Descriptive analytics can keep track of business metrics as well as key performance indicators (or KPIs), such as the number of products purchased over a certain period or the amount of new and repeat customers since a particular date. It can track monthly revenue increases and decreases, providing useful insights as a starting point toward actions. 

    How Does the Descriptive Analysis Process Work?

    Before data can be analyzed it must be gathered. The descriptive analysis process begins with consolidating data from all its various disparate sources into one singular location. 

    Once it is assembled, the data is cleaned to ensure that it is trustworthy. 

    This cleaning process can involve identifying and eliminating duplicate or incomplete data from the dataset, which removes potential problems when making future decisions based on information stored in these data sets. The data is then organized and analyzed using various tools and software. Some of the more popular descriptive analytics tools include SAP Analytics Cloud, SAS, Tableau, Apache Spark, and Sisense.

    While long, overcomplicated spreadsheets were once the standard for data analysis, the data analytics tools of today offer more intuitive, visually appealing aids for understanding data. Different data analysis software offer options for interactive displays, graphs, and charts that can allow users to easily interact with and visualize data content. 

    Working with Descriptive Analytics

    While other types of data analysis can provide deeper or more action-oriented insights (such as predictive analyses, prescriptive analyses, and diagnostic analyses), descriptive analyses can provide clear, powerful information with widespread implications. 

    By bringing data analytics back to its basic elements and answering simple questions about what information data contains, analysts can make smarter, streamlined decisions with confidence. The descriptions that this type of data analysis can provide can guide overall business decisions based on performance, targets, and trends. 

    Descriptive analyses lend themselves naturally to insightful financial decision-making processes and can help to shape marketing campaigns. Let’s take a look at four ways to utilize descriptive analytics to make better decisions.  

    Identify Trends

    Descriptive analytics are used most commonly across all industries to recognize and analyze trends. For example, media streaming company Netflix relies heavily on data analysis to shape the direction of its growth and evolution. The team at Netflix gathers data about Netflix viewers’ habits and preferences. 

    They then use descriptive analytics software to understand which movies and TV series are most popular at any particular moment. Using this data, they take it one step further to figure out why and how this media is connecting with audiences, and how that information can be applied to media development and choices in the future. 

    Track the Success of Marketing Campaigns

    Descriptive analytics are frequently used to help organizations shape the direction of their marketing campaigns. By uncovering information on new leads, new customer preferences, conversion rates, and marketing spending, descriptive analyses can be used to trace the successes and weaknesses of each marketing campaign over time. 

    These sets of data can be organized into charts that quickly compare multiple campaigns or the same campaign over different time sets. This information has broader implications for good decision-making within an organization. Tracking the progress of individual campaigns can shape future marketing campaigns, which will directly affect the overall viability of the organization. 

    In addition, descriptive analytics can bring traditional and digital marketing campaigns closer together, since data analysis can easily identify trends that include virtual and physical engagement. An analysis that combines social media impressions, the rate of bounced website pages, the number of clicks on a professional Facebook ad, and other signifiers can provide a powerful tool for steering the direction of marketing campaign progress through a series of smart, informed decisions. 

    Monitor Finances

    Any organization can utilize descriptive analytics to keep track of its financial status. Businesses can set up regular data sets organized by value, which descriptive analyses can use to identify patterns and trends. For example, a business can assemble regular weekly data sets drawn from the number of products sold each week. 

    Descriptive analysis software can then provide an accessible and easy-to-understand chart of what this data suggests about the business’s overall financial health. The same process can be applied to monthly, quarterly, and annual revenues, revealing insights into year-over-year growth and stability. 

    Stakeholders and executives can then use this descriptive data to make informed choices about where to allocate funds, which assets to purchase, where and when to invest more in product development, and how to shape target objectives. In this way, descriptive data provides the answers to the “what” questions about finances so that executives and stakeholders can make decisions about the who, where, why, how, and when. 

    Generate Overall Business Performance Insights

    Beyond the already valuable tasks of keeping track of financial well-being and helping to shape marketing campaigns, descriptive data can also help shareholders and executives discover insights about their entire business performance. Descriptive data can reveal new patterns and information about growth rates and churn rates. It can even address unexpected areas, such as employee engagement and productivity. 

    Descriptive analyses can reveal possible future risks to the business, which can motivate executives to make smart adjustments before potential risks become an actual problem. 

    With cybersecurity an ever more pressing issue, descriptive analyses can be a powerful tool in preventing cybercrime. Data breaches in the cloud are only getting worse, and executives can use the descriptive analysis process to identify possible cyberattacks or vulnerability points. 

    Final Thoughts on Descriptive Analytics

    With the data provided by descriptive analyses, stakeholders and business owners can make informed choices about how to keep their organization growing and evolving. Descriptive analyses pare down the analysis process to its simplest, most basic question, “What happened?”

    By doing so, descriptive analytics can provide a strong foundation upon which analysts can build, deepening their understanding of patterns, trends, and future developments. Making good use of this information is an effective way to make better, smarter, more future-oriented decisions for any organization. 

     

    Author: Nahla Davies

    Source: Dataversity

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