4 Ways social media posts can provide competitive intelligence

Over the past decade, social media has transformed the way businesses promote themselves by opening up new, direct lines of communication with current and potential customers. Unlike traditional advertising, social media is interactive and immediate, and the content is often more diverse and in-depth than what you’d find in an ad.

With all that in mind, keeping track of what your competitors are posting can provide valuable insights. If you’re starting a social media monitoring process, here are four types of posts you’ll definitely want to capture:

• Industry relationships: It’s always useful to know who’s rubbing elbows with your biggest competitors, and many businesses use their social media accounts to actively promote their relationships with other businesses. Keep an eye on who your competition is retweeting, reposting, and tagging on social media, particularly on platforms like Facebook and Twitter, for a glimpse at what companies they’re talking to and, potentially, partnering with.

• Events & webinars: Conferences and expos great opportunities to find out what other businesses are offering, and knowing which events your competition will be attending can give you an edge in terms of deciding which events are worth your time and money. Many businesses use social media to advertise the events they plan to attend, as well as the events and webinars they plan to host in the near future.

   

• Customer complaints: It’s easy to find out what your competitors’ view as their strengths, just check out their advertising campaigns. But very few companies are upfront about their products’ weaknesses. To find out what’s not working for them, look for customer complaints and questions directed at the competition’s social media accounts. Many customers turn to Twitter for an immediate response when they have a customer service issue, and those public posts are a great source of insight into the problems the company is dealing with, as well as how they’re handling the complaints.

• Sponsored or employee-generated content:Native advertising, or ads that blend in with the publication’s non-sponsored content, have blown up over the past decade. Companies are jumping at the chance to engage potential customers through sponsored or employee-authored articles, and the content they produce is often full of useful tidbits. Watch your competition’s social media accounts for posts promoting articles or guest blogs written by employees of the company. Chances are, even if it doesn’t look like an advertisement, it still promotes the business’s products and perspectives.

Source: CI Radar

Business Intelligence outdated? Niets is minder waar zo bewees het Heliview congres ‘Decision making by smart technologies’ dat afgelopen dinsdag in de Brabanthallen in Den Bosch werd georganiseerd.

200 Klantorganisaties luisterden naar presentaties van o.a. Rick van der Lans, Peter Jager, Frank de Nijs en Arent van ‘t Spijker. Naast het bekende geluid was er ook veel nieuws te beluisteren in Den Bosch.

Nieuwe technologieën maken heel veel meer mogelijk. Social media en, moderne, big data technologie stellen organisaties in staat veel meer waarde uit data te halen. Hoe organisaties dat moeten doen is veelal nog een uitdaging. Toepassing van de technologie is geen doel op zich zelf. Het gaat erom toegevoegde waarde voor organisaties te produceren. Of door optimalisatie van processen. Dan wel door het beter bedienen van de klant door productontwikkeling. In extremis kan data zelfs de motor achter nieuwe business concepten of –modellen zijn. Voorwaarde is wel een heldere bedrijfsvisie (al dan niet geproduceerd met intelligent gebruik van data en informatie). Belangrijk om te voorkomen dat we ongericht miljoenen stuk slaan op nieuwe technologie.

Voor de aanwezigen was het gehoorde geluid soms bekend, maar soms ook een confrontatie met zichzelf. Een ding is zeker: De rol van data en informatie bij het intelligent zaken doen is nog niet uitgespeeld. Business Intelligence leeft.

30 JANUARI 2015

For a site that only requires 140 characters to get your message across, there is a lot of confusion about how to effectively use Twitter to monitor competitors, and how to analyze that information to create an awesome social media strategy.

Competitive Intelligence and Twitter:

Why even use Twitter?

Almost every major brand has jumped aboard the Twitter wagon, but just showing up is not enough. You’ve seen them in your newsfeed, possibly from a company, or perhaps from your Mom who just signed up to the service: the dreaded Pointless Tweet.  Pointless Tweets are messages that do not provide value to your reader. Repeating your marketing message or Tweeting inspirational quotes is not a way to engage your customers.

Used properly, Twitter provides a platform for you to engage with customers and potential customers, provide instant customer service, and establish your company as an industry leader.

How to find competitors through Twitter.

To search for competitors, and discover who they engage with, simply type their names into the Twitter search. You can see the competitors’ Tweets by clicking ‘People’, and learn who is Tweeting about them by clicking ‘Tweets’.

Want to get fancy?  Twitter also has an Advanced Search feature that lets you track by location, or search for Tweets with links from a specific user, or even for Tweets that are only positive or negative.

What information should I track?

1. Number of followers
2. How frequently your competitor Tweets
3. The engagement impact of each Tweet – How many retweets, replies, and favorites
4. How often your competitor responds to customer inquiries
5. What times of day they are Tweeting

This should be tracked daily in an excel spreadsheet, or you can take the simple, automated route and sign up for Rivalfox’s upcoming beta release. Over time, the data will provide you with valuable insights, e.g. which type of Tweets create the most engagement and whether the time of Tweets correlates to more followers. Armed with this information, you can fine-tune your own awesome Twitter strategy.

Creating Awesome Strategy

When it comes to Twitter strategy, you do not need to reinvent the wheel.  Utilize what works for your competitors and change what doesn’t.  The most important goal is to share thrilling creative content, provide excellent customer service, and to engage potential customers by reaching out directly to their screens.

Source: rivalfox.com, 13 oktober 2015

Het ‘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

Moet de afdeling personeelszaken ook opletten wat de medewerkers allemaal zeggen en registreren op sociale media als LinkedIn, Facebook en Twitter? Moet dat opgeslagen worden voor de eeuwigheid? Het kunstmatig intelligente softwareprogramma Crunchr blijft daar nu nog allemaal ver vandaan, stelt oprichter Dirk Jonker van Focus Orange, de eigenaar van Crunchr, meerdere malen nadrukkelijk.

Net als bij AH moet de werknemer er volgens Jonker ook beter van worden als hij bijvoorbeeld data over zijn arbeidsverleden ter beschikking stelt. ‘We moeten de werknemer in ruil daarvoor ook een dienst kunnen aanbieden. Daarnaast is transparantie over wat je doet, en waarom, het belangrijkste. Maar voorlopig moeten bedrijven eerst de data gaan gebruiken die ze al hebben.’

Anoniem onderzoek

Focus Orange adviseert ook regelmatig bij het ontwerpen van cao’s. ‘Daar zetten we Crunchr ook in. Het blijkt namelijk dat ondernemingen vaak onvoldoende weten wat het personeel echt wil. Als je dat meet, kun je een pakket voorstellen dat effectief en aantrekkelijk is.'

Consortium

In mei heeft Jonker het People Analytics Consortium opgericht om het vakgebied verder door te ontwikkelen. De TU Delft, het Centrum voor Wiskunde en Informatie (VU/UvA), Randstad, Wolters Kluwer en ASML nemen hieraan deel. Binnen het consortium worden technieken ontwikkeld — nadrukkelijk niet met data van de betrokkenen bedrijven — om nieuwe vraagstukken te kunnen beantwoorden. Welke vragen moet een bedrijf stellen om patronen te kunnen herkennen en wat zijn de beste technieken om de vragen te laten beantwoorden?

‘Er worden bijvoorbeeld 40.000 lappen tekst in het systeem gestopt. Dat moet dan technisch ontleed worden. We trainen ons algoritme bijvoorbeeld op de hele correspondentie rond het Enron-schandaal. Dat is allemaal openbaar.’

Naast deze vrij ingewikkelde techniek kan Crunchr ook worden ingezet om inconsistenties in bedrijfsdata te vinden. Bijvoorbeeld of iemand teveel verdient voor zijn functie. Bij grote bedrijven die in alle delen van de wereld werken, is zo’n 'uitschieter’ geen uitzondering. ‘Daar komen soms dingen uit die je met het blote oog niet ziet’, zegt Jonker. ‘Wij kunnen in zeven seconden 40.000 records doorakkeren. En met de uitschieters die we vinden kunnen we ons systeem weer kalibreren.’

Opvolging

Bij opvolgingsplanning maakt personeelszaken voor senior management en kritieke posities een plan wie deze personen kunnen opvolgen als de positie vacant komt. Voorselectie ligt gevoelig, het kost veel tijd en de plannen zijn beperkt houdbaar. Zodra een belangrijk persoon vertrekt, is de helft van de kandidaten ook al weer in een andere positie, of inmiddels ongeschikt gebleken.

Crunchr gebruikt de opvolgingsplannen als input voor het algoritme, dat zelf moet leren wat iemand tot een goede opvolger maakt. Als er in de toekomt een positie vrijkomt, kijkt het netwerk in het hele bedrijf naar opvolgers. Het bedrijf is flexibel en niet meer afhankelijk van verouderde plannen. Iedereen komt in beeld en als een bedrijf meer vrouwen in de top wil, dan kan het daar op sturen. Door dit eindeloos systematisch te oefenen is Crunchr getraind.

Een grote internationale onderneming maakt al snel decentraal een paar duizend plannen, die het regionaal bespreekt en waar personeelszaken op corporate niveau op stuurt. Een getraind algoritme geeft binnen enkele seconden elk plan een realiteitsscore. Een pas afgestudeerde Nederlander opvolger laten zijn voor een senior managementpositie in de VS is bijvoorbeeld onwaarschijnlijk.

Risico's mitigeren

Met de bekende ‘grafentheorie’ uit de wiskunde laat Crunchr zien of binnen het management iedereen denkt een aantal opvolgers te hebben, maar als dit veelal dezelfde toppotentials zijn, is het risico niet afgezwakt. Daarnaast kan het bedrijf de opvolgingsplannen gebruiken voor het voorspellen van ‘global mobility’. Hoe bewegen toekomstige leiders over de wereld, waar zitten leiders nu en waarheen verhuizen ze. Beide inzichten zijn volgens Jonker bijzonder waardevol voor de top van het bedrijf.

FD, 3 oktober 2016

How algorithms mislead the human brain in social media - Part 1

Consider Andy, who is worried about contracting COVID-19. Unable to read all the articles he sees on it, he relies on trusted friends for tips. When one opines on Facebook that pandemic fears are overblown, Andy dismisses the idea at first. But then the hotel where he works closes its doors, and with his job at risk, Andy starts wondering how serious the threat from the new virus really is. No one he knows has died, after all. A colleague posts an article about the COVID “scare” having been created by Big Pharma in collusion with corrupt politicians, which jibes with Andy's distrust of government. His Web search quickly takes him to articles claiming that COVID-19 is no worse than the flu. Andy joins an online group of people who have been or fear being laid off and soon finds himself asking, like many of them, “What pandemic?” When he learns that several of his new friends are planning to attend a rally demanding an end to lockdowns, he decides to join them. Almost no one at the massive protest, including him, wears a mask. When his sister asks about the rally, Andy shares the conviction that has now become part of his identity: COVID is a hoax.

This example illustrates a minefield of cognitive biases. We prefer information from people we trust, our in-group. We pay attention to and are more likely to share information about risks—for Andy, the risk of losing his job. We search for and remember things that fit well with what we already know and understand. These biases are products of our evolutionary past, and for tens of thousands of years, they served us well. People who behaved in accordance with them—for example, by staying away from the overgrown pond bank where someone said there was a viper—were more likely to survive than those who did not.

Modern technologies are amplifying these biases in harmful ways, however. Search engines direct Andy to sites that inflame his suspicions, and social media connects him with like-minded people, feeding his fears. Making matters worse, bots—automated social media accounts that impersonate humans—enable misguided or malevolent actors to take advantage of his vulnerabilities.

Compounding the problem is the proliferation of online information. Viewing and producing blogs, videos, tweets and other units of information called memes has become so cheap and easy that the information marketplace is inundated. Unable to process all this material, we let our cognitive biases decide what we should pay attention to. These mental shortcuts influence which information we search for, comprehend, remember and repeat to a harmful extent.

The need to understand these cognitive vulnerabilities and how algorithms use or manipulate them has become urgent. At the University of Warwick in England and at Indiana University Bloomington's Observatory on Social Media (OSoMe, pronounced “awesome”), our teams are using cognitive experiments, simulations, data mining and artificial intelligence to comprehend the cognitive vulnerabilities of social media users. Insights from psychological studies on the evolution of information conducted at Warwick inform the computer models developed at Indiana, and vice versa. We are also developing analytical and machine-learning aids to fight social media manipulation. Some of these tools are already being used by journalists, civil-society organizations and individuals to detect inauthentic actors, map the spread of false narratives and foster news literacy.

Information Overload

The glut of information has generated intense competition for people's attention. As Nobel Prize–winning economist and psychologist Herbert A. Simon noted, “What information consumes is rather obvious: it consumes the attention of its recipients.” One of the first consequences of the so-called attention economy is the loss of high-quality information. The OSoMe team demonstrated this result with a set of simple simulations. It represented users of social media such as Andy, called agents, as nodes in a network of online acquaintances. At each time step in the simulation, an agent may either create a meme or reshare one that he or she sees in a news feed. To mimic limited attention, agents are allowed to view only a certain number of items near the top of their news feeds.

Running this simulation over many time steps, Lilian Weng of OSoMe found that as agents' attention became increasingly limited, the propagation of memes came to reflect the power-law distribution of actual social media: the probability that a meme would be shared a given number of times was roughly an inverse power of that number. For example, the likelihood of a meme being shared three times was approximately nine times less than that of its being shared once.

This winner-take-all popularity pattern of memes, in which most are barely noticed while a few spread widely, could not be explained by some of them being more catchy or somehow more valuable: the memes in this simulated world had no intrinsic quality. Virality resulted purely from the statistical consequences of information proliferation in a social network of agents with limited attention. Even when agents preferentially shared memes of higher quality, researcher Xiaoyan Qiu, then at OSoMe, observed little improvement in the overall quality of those shared the most. Our models revealed that even when we want to see and share high-quality information, our inability to view everything in our news feeds inevitably leads us to share things that are partly or completely untrue.

Cognitive biases greatly worsen the problem. In a set of groundbreaking studies in 1932, psychologist Frederic Bartlett told volunteers a Native American legend about a young man who hears war cries and, pursuing them, enters a dreamlike battle that eventually leads to his real death. Bartlett asked the volunteers, who were non-Native, to recall the rather confusing story at increasing intervals, from minutes to years later. He found that as time passed, the rememberers tended to distort the tale's culturally unfamiliar parts such that they were either lost to memory or transformed into more familiar things. We now know that our minds do this all the time: they adjust our understanding of new information so that it fits in with what we already know. One consequence of this so-called confirmation bias is that people often seek out, recall and understand information that best confirms what they already believe.

This tendency is extremely difficult to correct. Experiments consistently show that even when people encounter balanced information containing views from differing perspectives, they tend to find supporting evidence for what they already believe. And when people with divergent beliefs about emotionally charged issues such as climate change are shown the same information on these topics, they become even more committed to their original positions.

Making matters worse, search engines and social media platforms provide personalized recommendations based on the vast amounts of data they have about users' past preferences. They prioritize information in our feeds that we are most likely to agree with—no matter how fringe—and shield us from information that might change our minds. This makes us easy targets for polarization. Nir Grinberg and his co-workers at Northeastern University recently showed that conservatives in the U.S. are more receptive to misinformation. But our own analysis of consumption of low-quality information on Twitter shows that the vulnerability applies to both sides of the political spectrum, and no one can fully avoid it. Even our ability to detect online manipulation is affected by our political bias, though not symmetrically: Republican users are more likely to mistake bots promoting conservative ideas for humans, whereas Democrats are more likely to mistake conservative human users for bots.

Social Herding

In New York City in August 2019, people began running away from what sounded like gunshots. Others followed, some shouting, “Shooter!” Only later did they learn that the blasts came from a backfiring motorcycle. In such a situation, it may pay to run first and ask questions later. In the absence of clear signals, our brains use information about the crowd to infer appropriate actions, similar to the behavior of schooling fish and flocking birds.

Such social conformity is pervasive. In a fascinating 2006 study involving 14,000 Web-based volunteers, Matthew Salganik, then at Columbia University, and his colleagues found that when people can see what music others are downloading, they end up downloading similar songs. Moreover, when people were isolated into “social” groups, in which they could see the preferences of others in their circle but had no information about outsiders, the choices of individual groups rapidly diverged. But the preferences of “nonsocial” groups, where no one knew about others' choices, stayed relatively stable. In other words, social groups create a pressure toward conformity so powerful that it can overcome individual preferences, and by amplifying random early differences, it can cause segregated groups to diverge to extremes.

Social media follows a similar dynamic. We confuse popularity with quality and end up copying the behavior we observe. Experiments on Twitter by Bjarke Mønsted and his colleagues at the Technical University of Denmark and the University of Southern California indicate that information is transmitted via “complex contagion”: when we are repeatedly exposed to an idea, typically from many sources, we are more likely to adopt and reshare it. This social bias is further amplified by what psychologists call the “mere exposure” effect: when people are repeatedly exposed to the same stimuli, such as certain faces, they grow to like those stimuli more than those they have encountered less often.

Such biases translate into an irresistible urge to pay attention to information that is going viral—if everybody else is talking about it, it must be important. In addition to showing us items that conform with our views, social media platforms such as Facebook, Twitter, YouTube and Instagram place popular content at the top of our screens and show us how many people have liked and shared something. Few of us realize that these cues do not provide independent assessments of quality.

In fact, programmers who design the algorithms for ranking memes on social media assume that the “wisdom of crowds” will quickly identify high-quality items; they use popularity as a proxy for quality. Our analysis of vast amounts of anonymous data about clicks shows that all platforms—social media, search engines and news sites—preferentially serve up information from a narrow subset of popular sources.

To understand why, we modeled how they combine signals for quality and popularity in their rankings. In this model, agents with limited attention—those who see only a given number of items at the top of their news feeds—are also more likely to click on memes ranked higher by the platform. Each item has intrinsic quality, as well as a level of popularity determined by how many times it has been clicked on. Another variable tracks the extent to which the ranking relies on popularity rather than quality. Simulations of this model reveal that such algorithmic bias typically suppresses the quality of memes even in the absence of human bias. Even when we want to share the best information, the algorithms end up misleading us.

Want to continue reading? You can find part 2 of this article herehere

Authors: Filippo Menczer

Source: Scientific American

How algorithms mislead the human brain in social media - Part 2

If you haven't read part 1 of this article yet, be sure to check it out here.

Echo Chambers

Most of us do not believe we follow the herd. But our confirmation bias leads us to follow others who are like us, a dynamic that is sometimes referred to as homophily—a tendency for like-minded people to connect with one another. Social media amplifies homophily by allowing users to alter their social network structures through following, unfriending, and so on. The result is that people become segregated into large, dense and increasingly misinformed communities commonly described as echo chambers.

At OSoMe, we explored the emergence of online echo chambers through another simulation, EchoDemo. In this model, each agent has a political opinion represented by a number ranging from −1 (say, liberal) to +1 (conservative). These inclinations are reflected in agents' posts. Agents are also influenced by the opinions they see in their news feeds, and they can unfollow users with dissimilar opinions. Starting with random initial networks and opinions, we found that the combination of social influence and unfollowing greatly accelerates the formation of polarized and segregated communities.

Indeed, the political echo chambers on Twitter are so extreme that individual users' political leanings can be predicted with high accuracy: you have the same opinions as the majority of your connections. This chambered structure efficiently spreads information within a community while insulating that community from other groups. In 2014 our research group was targeted by a disinformation campaign claiming that we were part of a politically motivated effort to suppress free speech. This false charge spread virally mostly in the conservative echo chamber, whereas debunking articles by fact-checkers were found mainly in the liberal community. Sadly, such segregation of fake news items from their fact-check reports is the norm.

Social media can also increase our negativity. In a recent laboratory study, Robert Jagiello, also at Warwick, found that socially shared information not only bolsters our biases but also becomes more resilient to correction. He investigated how information is passed from person to person in a so-called social diffusion chain. In the experiment, the first person in the chain read a set of articles about either nuclear power or food additives. The articles were designed to be balanced, containing as much positive information (for example, about less carbon pollution or longer-lasting food) as negative information (such as risk of meltdown or possible harm to health).

The first person in the social diffusion chain told the next person about the articles, the second told the third, and so on. We observed an overall increase in the amount of negative information as it passed along the chain—known as the social amplification of risk. Moreover, work by Danielle J. Navarro and her colleagues at the University of New South Wales in Australia found that information in social diffusion chains is most susceptible to distortion by individuals with the most extreme biases.

Even worse, social diffusion also makes negative information more “sticky.” When Jagiello subsequently exposed people in the social diffusion chains to the original, balanced information—that is, the news that the first person in the chain had seen—the balanced information did little to reduce individuals' negative attitudes. The information that had passed through people not only had become more negative but also was more resistant to updating.

A 2015 study by OSoMe researchers Emilio Ferrara and Zeyao Yang analyzed empirical data about such “emotional contagion” on Twitter and found that people overexposed to negative content tend to then share negative posts, whereas those overexposed to positive content tend to share more positive posts. Because negative content spreads faster than positive content, it is easy to manipulate emotions by creating narratives that trigger negative responses such as fear and anxiety. Ferrara, now at the University of Southern California, and his colleagues at the Bruno Kessler Foundation in Italy have shown that during Spain's 2017 referendum on Catalan independence, social bots were leveraged to retweet violent and inflammatory narratives, increasing their exposure and exacerbating social conflict.

Rise of the Bots

Information quality is further impaired by social bots, which can exploit all our cognitive loopholes. Bots are easy to create. Social media platforms provide so-called application programming interfaces that make it fairly trivial for a single actor to set up and control thousands of bots. But amplifying a message, even with just a few early upvotes by bots on social media platforms such as Reddit, can have a huge impact on the subsequent popularity of a post.

At OSoMe, we have developed machine-learning algorithms to detect social bots. One of these, Botometer, is a public tool that extracts 1,200 features from a given Twitter account to characterize its profile, friends, social network structure, temporal activity patterns, language and other features. The program compares these characteristics with those of tens of thousands of previously identified bots to give the Twitter account a score for its likely use of automation.

In 2017 we estimated that up to 15 percent of active Twitter accounts were bots—and that they had played a key role in the spread of misinformation during the 2016 U.S. election period. Within seconds of a fake news article being posted—such as one claiming the Clinton campaign was involved in occult rituals—it would be tweeted by many bots, and humans, beguiled by the apparent popularity of the content, would retweet it.

Bots also influence us by pretending to represent people from our in-group. A bot only has to follow, like and retweet someone in an online community to quickly infiltrate it. OSoMe researcher Xiaodan Lou developed another model in which some of the agents are bots that infiltrate a social network and share deceptively engaging low-quality content—think of clickbait. One parameter in the model describes the probability that an authentic agent will follow bots—which, for the purposes of this model, we define as agents that generate memes of zero quality and retweet only one another. Our simulations show that these bots can effectively suppress the entire ecosystem's information quality by infiltrating only a small fraction of the network. Bots can also accelerate the formation of echo chambers by suggesting other inauthentic accounts to be followed, a technique known as creating “follow trains.”

Some manipulators play both sides of a divide through separate fake news sites and bots, driving political polarization or monetization by ads. At OSoMe, we recently uncovered a network of inauthentic accounts on Twitter that were all coordinated by the same entity. Some pretended to be pro-Trump supporters of the Make America Great Again campaign, whereas others posed as Trump “resisters”; all asked for political donations. Such operations amplify content that preys on confirmation biases and accelerate the formation of polarized echo chambers.

Curbing Online Manipulation

Understanding our cognitive biases and how algorithms and bots exploit them allows us to better guard against manipulation. OSoMe has produced a number of tools to help people understand their own vulnerabilities, as well as the weaknesses of social media platforms. One is a mobile app called Fakey that helps users learn how to spot misinformation. The game simulates a social media news feed, showing actual articles from low- and high-credibility sources. Users must decide what they can or should not share and what to fact-check. Analysis of data from Fakey confirms the prevalence of online social herding: users are more likely to share low-credibility articles when they believe that many other people have shared them.

Another program available to the public, called Hoaxy, shows how any extant meme spreads through Twitter. In this visualization, nodes represent actual Twitter accounts, and links depict how retweets, quotes, mentions and replies propagate the meme from account to account. Each node has a color representing its score from Botometer, which allows users to see the scale at which bots amplify misinformation. These tools have been used by investigative journalists to uncover the roots of misinformation campaigns, such as one pushing the “pizzagate” conspiracy in the U.S. They also helped to detect bot-driven voter-suppression efforts during the 2018 U.S. midterm election. Manipulation is getting harder to spot, however, as machine-learning algorithms become better at emulating human behavior.

Apart from spreading fake news, misinformation campaigns can also divert attention from other, more serious problems. To combat such manipulation, we have recently developed a software tool called BotSlayer. It extracts hashtags, links, accounts and other features that co-occur in tweets about topics a user wishes to study. For each entity, BotSlayer tracks the tweets, the accounts posting them and their bot scores to flag entities that are trending and probably being amplified by bots or coordinated accounts. The goal is to enable reporters, civil-society organizations and political candidates to spot and track inauthentic influence campaigns in real time.

These programmatic tools are important aids, but institutional changes are also necessary to curb the proliferation of fake news. Education can help, although it is unlikely to encompass all the topics on which people are misled. Some governments and social media platforms are also trying to clamp down on online manipulation and fake news. But who decides what is fake or manipulative and what is not? Information can come with warning labels such as the ones Facebook and Twitter have started providing, but can the people who apply those labels be trusted? The risk that such measures could deliberately or inadvertently suppress free speech, which is vital for robust democracies, is real. The dominance of social media platforms with global reach and close ties with governments further complicates the possibilities.

One of the best ideas may be to make it more difficult to create and share low-quality information. This could involve adding friction by forcing people to pay to share or receive information. Payment could be in the form of time, mental work such as puzzles, or microscopic fees for subscriptions or usage. Automated posting should be treated like advertising. Some platforms are already using friction in the form of CAPTCHAs and phone confirmation to access accounts. Twitter has placed limits on automated posting. These efforts could be expanded to gradually shift online sharing incentives toward information that is valuable to consumers.

Free communication is not free. By decreasing the cost of information, we have decreased its value and invited its adulteration. To restore the health of our information ecosystem, we must understand the vulnerabilities of our overwhelmed minds and how the economics of information can be leveraged to protect us from being misled.

Authors: Filippo Menczer

Source: Scientific American

Data is at the very core of the business models of the future – and this means wrenching change for some organizations.

We tend to think of our information systems as a foundation layer that support the “real” business of the organization – for example, by providing the information executives need to steer the business and make the right decisions.

But information is rapidly becoming much more than that: it’s turning into an essential component of the products and services we sell.

Information-augmented products

In an age of social media transparency, products “speak for themselves”– if you have a great product, your customers will tell their friends. If you have a terrible product, they’ll tell the world. Your marketing and sales teams have less room for maneuver, because prospects can easily ask existing customers if your product lives up to the promises.

And customer expectations have risen. We all now expect to be treated as VIPs, with a “luxury” experience. When we make a purchase, we expect to be recognized. We expect our suppliers to know what we’ve bought in the past. And we expect personalized product recommendations, based on our profile, the purchases of other people like us, and the overall context of what’s happening right now.

This type of customer experience doesn’t just require information systems; the information is an element of the experience itself, part of what we’re purchasing, and what differentiates products and services in the market.

New ways of selling

New technologies like 3D printing and the internet of things are allowing companies to rethink existing products.

Products can be more easily customized and personalized for every customer. Pricing can be more variable to address new customer niches. And products can be turned into services, with customers paying on a per-usage basis.

Again, information isn’t just supporting the manufacturing and sale of the product – it’s part of what makes it a “product” in the first place.

Information as a product

In many industries, the information collected by business is now more valuable than the products being sold – indeed, it’s the foundation for most of the free consumer internet. Traditional industries are now realizing that the data stored in their systems, once suitably augmented or anonymized, can be sold directly. See this article on the Digitalist magazine, The Hidden Treasure Inside Your Business, for more information about the four main information business models.

A culture change for “traditional IT”

Traditional IT systems were about efficiency, effectiveness, and integrity. These new context-based experiences and more sophisticated products use information to generate growth, innovation, and market differentiation. But these changes lead to a difficult cultural challenge inside the organization.

Today’s customer-facing business and product teams don’t just need reliable information infrastructures. They need to be able to experiment, using information to test new product options and ways of selling. This requires not only much more flexibility and agility than in the past, but also new ways of working, new forms of IT organization, and new sharing of responsibilities.

The majority of today’s CIOs grew up in an era of “IT industrialization,” with the implementation of company-wide ERP systems. But what made them successful in the past won’t necessarily help them win in the new digital era.

Gartner believes that the role of the “CIO” has already split into two distinct functions: Chief Infrastructure Officers whose job is to “keep the lights on”; and Chief Innovation Offers, who collaborate closely with the business to build the business models of the future.

IT has to help lead

Today’s business leaders know that digital is the future, but typically only have a hazy idea of the possibilities. They know technology is important, but often don’t have a concrete plan for moving forward: 90% of CEOs believe the digital economy will have a major impact on their industry. But only 25% have a plan in place, and less than 15% are funding and executing a digital transformation plan.

Business people want help from IT to explain what’s possible. Today, only 7% of executives say that IT leads their organization’s attempts to identify opportunities to innovate, 35% believe that it should. After decades of complaints from CIOs that businesses aren’t being strategic enough about technology, this is a fantastic new opportunity.

Design Thinking and prototyping

Today’s CIOs have to step up to digital innovation. The problem is that it can be very hard to understand — history is packed with examples of business leaders that just didn’t “get” the new big thing.  Instead of vague notions of “disruption,” IT can help by explaining to business people how to add information into a company’s future product experiences.

The best way to do this is through methodologies such as Design Thinking, and agile prototyping using technologies should as Build.me, a cloud platform that allows pioneers to create and test the viability of new applications with staff and customers long before any actual coding.

Conclusion

The bottom line is that digital innovation is less about the technology, and more about the transformation — but IT has an essential role to play in demonstrating what’s possible, and needs to step up to new leadership roles.

Source: timoelliot.com, November 14, 2016

Social intelligence: social media data as a means for market intelligence

To be successful as researchers, we need to know our customers. We seek to identify our customers’ needs, desires, and opinions about our products, brands, and competitors. In doing so, we understand better how to tailor our products and services to meet our customers’ needs.

Traditional market research identifies subgroups within a target audience through direct engagement, in-person or through email engagements. Researchers can explicitly define and ensure an individual’s association to a given audience. It’s an effective approach, but it can be expensive, time-consuming, and difficult to execute.

Where traditional market research and social media meet

At Microsoft, the Research + Insights (R+I) team focuses on answering the same business questions as sought through traditional market research. However, instead of using standard methods, we utilize the vast set of available data in social. Over the past year, we asked questions like: What if we could leverage public social media feeds to understand what specific audiences people belong to? What messages might be most likely to have a positive impact? What if we could align social market research methods to traditional market research methods to drive a greater understanding of the customer in more dynamic ways?

Microsoft’s R+I team has developed a method for identifying and grouping social media users based on their online behavior and comments. This is a “listening-based” approach that can be automated to sift through massive amounts of social media traffic and metadata to create business-relevant segments and to find relevant insights.

How a “listening-based” approach to social intelligence works

For most social users, their profile encompasses their varying identities. A single profile could perhaps represent a marketing professional, a mother, and someone who likes to travel and dine out. Within this profile, that individual is apt to identify her interests and offer commentary on each of them. A Twitter profile bio, for instance, may contain the line: “Professional marketer, mom of two kids, can’t wait for next trip to France.” Chances are, this individual makes social posts relevant to each of those three personae, as well as a variety of other topics.

The advantage of our approach is that our audience members self-identify, then engage in natural and organic conversations in a way that can’t be replicated through traditional market research. By utilizing biographical word tagging in social media, we strive to achieve a minimum content inclusion accuracy of 80 percent for audience inclusion and conversation relevancy. Meaning when we analyze 100 profiles in the social media grouping, at least 80% of those profiles are relevant to the grouping. For our Topic inclusion, we apply a similar methodology. This ensures our data is clean before we analyze it at the aggregate level.

We can expand on the biographical word tagging, using the frequency and relevancy of terms mentioned, to bucket groups for increased accuracy. For example, let’s take software developers. By assigning points based on mentions of developer languages (1 point), developer tools (2 points), developer conferences (3 points), and developer domains (5 points), we can identify group members and segment groups by amateur or professional developers, with those amassing the most points seen as “most active” developers. The methodology above is just an example of how weighted behavior across specific audiences can be leveraged to create meaningful groupings then used to distill business-relevant insights.

Below is an example output analyzing what is resonating from self-identified Microsoft Employees, Partners vs. the general population and themes in hybrid work discussion.

We can perform similar group identification among commercial, educational, and consumer customers, as well as Microsoft-centered customers (such as partners or Xbox fans). We can also infer group identification without direct self-identification. We know, for instance, that 70 percent of millennials follow Dan Price, the CEO of Gravity Payments. So, if we come across an individual who also follows Dan Price, in addition to other social behavioral markers scored similarly to the developer example above, we can infer that he or she likely is a millennial. This is a broad example, but you get the idea.

Customer privacy is at the core of what we do. When we are analyzing user conversations we are doing so at an aggregate level and not on the individual level to ensure personal identifiable information (aka PII) is not reported against. It is built into our best practices to scrub author names and users’ names from the records when we report against these audience segments.  

What’s next for social insights?

The most important piece is using our initial data to understand common characteristics in our audiences and to isolate common social traits within each audience. With that, we can broaden our groups by understanding the relevancy of behavior by the audience and then scoring (or weighting) those behaviors.

Once we’re able to group a certain number of identified behaviors, our understanding of the audience becomes more accurate. We’ll also be able to include individuals who don’t self-identify as part of our target audience. If they follow patterns similar to people who do self-identify, then we can include them within a group. At the end of the day, all of this is done to better understand how to address the needs of our customers in order to continue to empower them to achieve more.

Authors: Allie Webster & Justin Schoen

Source: Greenbook

The United Nations Chief Information Technology Officer spoke with TechRepublic about the future of cybersecurity, social media, and how to fix the internet and build global technology for social good.

Artificial intelligence, said United Nations chief information technology officer Atefeh Riazi, might be the last innovation humans create.

"The next innovations," said the cabinet-level diplomat during a recent interview at her office at UN headquarters in New York, "will come through artificial intelligence."

From then on, said Riazi, "it will be the AI innovating. We need to think about our role as technologists and we need to think about the ramifications—positive and negative—and we need to transform ourselves as innovators."

Appointed by Secretary General Ban Ki-moon as CITO and Assistant Secretary-General of the Office of Information and Communications Technology in 2013, Riazi is also an innovator in her own right in the global security community.

Riazi was born in Iran, and is a veteran of the information technology industry. She has a degree in electrical engineering from Stony Brook University in New York, spent over 20 years working in IT roles in the public and private sectors, and was the New York City Housing Authority's Chief Information Officer from 2009 to 2013. She has also served as the executive director of CIOs Without Borders, a non-profit organization dedicated to using technology for the good of society—especially to support healthcare projects in the developing world.

Riazi and her UN staff meet with diplomats and world leaders, NGOs, and executives at private companies like Google and Facebook to craft technology policy that impacts governments and businesses around the world.

TechRepublic's in-depth interview with her covered a broad range of important technology policy issues, including the digital divide, e-waste, cybersecurity, social media, and, of course, artificial intelligence.

The Digital Divide

TechRepublic: Access to information is essential in modern life. Can you explain how running IT for the New York City Housing Authority helps low income people?

UN CITO: When I was at New York City Housing, I came in as a CIO. The chairman had been a CIO and within six months most of the leadership left. He looked at me. I looked at him. The board looked at me. I knew to be nervous, and they said, "you're in. You're the next acting general manager of New York City Housing." I said, "Okay."

New York City Housing is a $3 billion organization providing support to about 500,000 residents. You have the Section 8 program, you have the public housing, and a billion and a half of construction. I came out of IT and I had to help manage and run New York City Housing at a very difficult time.

When you look at the city of New York, the digital divide among the youth and among the poor is very high. We have a digital divide right in this great city. Today I have two eight year olds and their homework. A lot of [their] research is done online. But in other areas of the city, you have kids that don't have access to computers, don't have access to the internet, cannot afford it. They can't find jobs because they don't have access to the internet. They can't do as well in school. A lot of them are single family, maybe grandparents raising them.

How do we provide them that access? How do we close the gap so they can compete with other classmates who have access to knowledge and information?

In Finland, they passed a law stating that internet access is a birthright. If it's a birthright, then let's give it to people right here in New York and elsewhere in the world.

All of the simple things that we have and we offer our children, if we could [provide internet access] as a public service, we begin to close the income gap, help people learn skills, and make them more viable for jobs.

E-waste

TechRepublic: Can you help us understand the role of electronic waste (e-waste) on women and girls in developing countries?

UN CITO: E-waste is the mercury and lead. Mercury and lead contributes to 5% of global waste. They contribute to 70% of hazardous materials. You have computers, servers, storage, and cell phones. We have no plans on recycling these. This is polluting the air and the water in China and India. Dioxin, if you burn electronics you get dioxin, which is like agent orange. The question to the tech sector is, okay, you created this wonderful world of technology, but you have no plans in addressing these big issues of environmental hazard.

The impact of electronic waste is tremendous because women's body looks at mercury as calcium. It brings it in, it puts it in the bones and then when you're pregnant, guess what? It thinks, oh, "I got some calcium. Here it is."

Newborns have mercury and lead in their blood, and disease. It's just contributing to so many children, so many women getting sick and because women pass it on to the next generation, [children] are impacted.

Where is the responsibility of the tech sector to say, "I will protect the women. I will protect the children. I will take out the lead and mercury. I will help contribute to recycling of my materials."

The Deep Web

TechRepublic: While there are many privacy benefits to the Deep Web, it's no secret that criminal activity flourishes on underground sites. I know this is the perpetual question, but is this criminal behavior that has always existed and now we can see it a little better, or does the Deep Web perpetuate and increase criminal behavior?

UN CITO: I wish I had enough insight to answer correctly, but I can give it from my perspective. The scope has changed tremendously. If you look at slavery and the number of people trafficked, there's 200 million people trafficked now. You look at the numbers and you look at how much the slaves were sold [in the past]. I think the slaves were sold for [hundreds] of... today's dollars. Today, you can buy a girl for $300 through the Deep Web.

Here's the thing. To the child trafficking, human trafficking has exploded because we're a global world. We can sell and buy globally. Before, the criminals couldn't do it globally. They couldn't move the people as fast.

TechRepublic: If we're putting this in very cynical market terms, the market for humans has grown due to the Deep Web?

UN CITO: Yes. The market has grown for sex trafficking, or for organs, or for just basic labor. There are many reasons where this has happened. We're seeing tremendous growth in criminal activity. It's very difficult to find criminals. Drug trafficking is easier. Commerce is easier in the Deep Web. All of that is going up.

Humans and 99% are good but you've got the 1%, and I think we have a plan to react to the criminal activities. At the UN we are beginning to build the cyber-expertise to become a catalyst. Not to resolve these issues, because I look at the internet as an infant that we have created, this species we've created which is growing and it's evolving. It's going through "terrible twos" right now. We have a choice to try to manage it, censor it, or shut it down, which we see in some countries. Or we have a choice to build its antibody. Make sure that it becomes strong.

We [can] create the "Light Web," and I think we can only do it through the use of all the amazing technology people globally want to [use to] do good. As a social group, we can create positive algorithms for social good.

Encryption and cybersecurity

TechRepublic: In the digital world, the notion of sovereignty is shifting. What is the UN's role in terms of cybersecurity?

UN CITO: It's shifting, exactly, because government rule over a civil society in a cyber-world doesn't exist. Do you think that criminals care that the UN or governments have a policy, or a rule? Countries and criminals will begin to attack each other.

From our perspective, our mission is really peace and security, development of human rights. The UN has a number of responsibilities. We have peacekeeping, human rights, development, and sustainable development. We look at cybersecurity, and we say that peace in the cyber-world is very different because countries are starting to attack each other, and starting to attack each [other's] industrial systems. Often attacks are asymmetrical. Peace to me is very different than peace to you.

We talk about cybersecurity. Okay, then what do we do? This is the world we've created through the internet. What do we do to bring peace to this world? What does anyone do?

I think that we spend a lot of money on cybersecurity globally. Public and private money, and we are not successful, really. Intrusions happen every day. Intellectual property is lost. Privacy, the way we knew it, has changed completely. There's a new way of thinking about privacy, and what's confidential.

We worry about industrial systems like our electric grid. We worry about our member states' industrial systems, intrusions into electricity, into water, and sanitation—things that impact human life.

Our peacekeepers are out in the field. We have helicopters. We have planes. A big worry of ours is an intrusion into a plane or helicopter, where you think the fuel gauge is full but it's empty. Or through a GPS. If your GPS is impacted, and you think you're here but you're actually there.

Where is the role of encryption? Encryption is amoral. It could be used for good. It could be used for bad. It's hard to have an opinion on encryption, for me at least, without realizing that the same thing I endorse for everyone, others endorse for criminals. Do we have the sophistication, the capabilities to limit that technology only for the good? I don't think we do.

TechRepublic: What is the plan for cybersecurity?

UN CITO: Well, I've been waiting. I think that is something for all the member states to come together and talk about cybersecurity.

But what is the plan of us as homosapiens, now we are connected sapiens and very soon we are a combination of carbon and silicon? As super intelligent beings, what is the plan? This is not being talked about. We hope that through the creation of digital Blue Helmet we'd begin a conversation and we'd begin to ask people to contribute positively to what we believe is ethically right. But then again, what we believe is ethically right somebody else may believe is ethically wrong.

Social Media

TechRepublic: The UN recently held a conference on social media and terrorism, particularly related to Daesh [ISIS]. What was the discussion about? What takeaways came from that conference?

UN CITO: Well, we got together as a lot of information and communication professionals, and academics to talk about the big issue of social media and terrorism with Daesh and ISIL. I think this type of dialog is really critical because if we don't talk about these issues, we can't come up with policy recommendations. I think there's a lot of really good discussion about human rights on the internet. "Thou shalt do no harm."

But we know that whatever policies we come up with, Daesh would be the last group that cares whether you have policies or not. There's deeper discussion about how does youth get attracted to radicalism? You have 50% unemployment of youth. You have major income disparity. I think if we can't begin to address the basic social issues, we're going to have more and more youth attracted to this radicalism. There was good discussion and dialog that we need to address those issues.

There's some discussion about how do we create the positive message? People, especially youth, want to do something positive. They want to participate. They want to be part of a bigger thing. How do we encourage them? When they look at the negative message, how do you bring in a positive message? Can governments to do something about that?

Look at the private sector. When there was a Tylenol scare or Toyota speeding on its own, when you went online and you searched for Tylenol, you didn't get all the bad stories about Tylenol. You went into the sites that Tylenol wanted you to go. Search is so powerful, and if you can begin to write positive algorithms, that begins to move the youth to positive messaging.

Don't try to use marketing or gimmicks because it's so transparent. People see right through it. Governments have a responsibility to provide a positive information space for their youth. There was a lot of good dialog around that.

On the technology side, I think this is a two year old infant, the internet is amoral, and we can use it for good and use it for bad. You can't shut down the internet. You can't shut down social media. There's a very gray space because, as I said, somebody's freedom fighter is somebody else's terrorist. Is it for Facebook or Twitter to make that decision?

Artificial intelligence

TechRepublic: I know you are quite curious about artificial intelligence. Is there a UN policy with respect to AI?

UN CITO: AI is an amazing thing to talk about, because now you can look at patterns much faster than humans [can]. Do we as technologists have the sophistication of addressing the moral and ethical issues of what's good and bad?

I think this is what scares me when it comes to AI. Let's say we as humans say, "we want people to be happy and with artificial intelligence, we should build systems for people to be happy." What does that mean?

I'm looking at the machine language, and the path we're creating for 10, 20, 30 years from now but not fully understanding the ethical programming that we're putting into the systems. IT people are creating the next world. The ethical programming they do is what is in their head, and so policies are being written in lines of code, in the algorithms.

We look at artificial intelligence and machine learning, and the world we see as technologists 20 years from now is very different than the world we have today. Artificial intelligence is this super, super intelligent species that is not human. Humans have reached our limitation.

That idea poses so many questions. If we create this artificial intelligence that can do 80% of the labor that humans do, what are the changes? Social, cultural, economic. All of these big, big questions have to be talked about.

I'm hoping that's the United Nations, but there's so much political opposition to those conversations. So much political opposition because we are holding on to our physical borders, and we have forgotten that those physical borders are gone. The world is virtual. We sit here as heads of departments and ministers and talk about AI. We discuss the moral, the ethical issues that people are going to confront with AI technology—positive and negative.

Source: TechRepublic

Using social media data to analyze market trends

Market trend analysis is an indispensable tool for companies these days. Social media gives analysts access to data that might otherwise be tough to collect. Rapidly changing business conditions require deep insight, and a market trend analysis report is a critical tool. Aside from future-proofing businesses, trend analysis reports also help companies tune into current dynamics and create better products or services.

There are many tools and data sources trend analysts use to prepare a market analysis report. However, social media data offers the most fertile ground. Today there are over 4.5 billion social media users worldwide. That’s over half the world’s population accessing social media and interacting with content.

Social media data is even more valuable because of the high costs of generating original research from scratch. In essence, social media platforms offer all the data companies need, and cost-effectively. Here are three major insights that market trend analysts can derive from social media data.

Consumer Preferences

Every business lives and dies with its customers, and assessing consumer preferences is a tough task. While existing customers often make their intentions clear with their purchase patterns over time, market trends often shift and push potential future customers away from a brand’s messaging.

“Usually, the first signs of a shift (in market trends) show themselves through social media or engagement metrics,” writes SimilarWeb’s Daniel Schneider in a recent post on market trend analysis. “This crucial rise or fall in traffic, engagement, or variation in demographics is what reveals your competitive advantage.” In this context, competitive advantage refers to a company or brand’s position in the market and its appeal to consumers, relative to how its competitors are perceived in “the conversation.”

Social media engagement data offers a wealth of insight in this regard. For instance, high-level data such as the number of comments or likes, and engagement per hashtag, provide companies insight into which topics niche consumers are interested in. Monitoring the trends in these metrics also reveals broader market shifts.

A company’s engagement rate trend and conversion ratios offer insight into marketing effectiveness over time. In the same way that a decreasing sales conversion rate over time points to a possible disconnect with consumers, so too does a falling follower or subscriber count.

Thanks to rising social awareness, companies are expected to take stands on important issues these days. Monitoring the usage of hashtags related to these issues, keeping an eye on trending topics, and tracking engagement metrics on content that addresses these issues helps companies easily tune into the current market climate.

When compared to conducting surveys or polls, there’s no doubt that social media data removes biases and presents user opinions in a useful manner.

Seasonal Trends

Many industries are subject to seasonal trends, and market analysts need to figure them out. The consequences of predicting an incorrect trend can be catastrophic, thanks to production and procurement schedules tied to seasonal demand. 

A market trend analysis that mines social media demographic data will uncover seasonal trends at multiple levels. At a high level, trend analysts can figure out who their customers are and what their tendencies look like. Platforms such as Facebook’s Ad Manager provide a wealth of information, right down to the type of devices the user prefers and even their political leanings.

Analysts can dig deeper into these data and uncover specific data points that help them segment their customer audience. For instance, customers older than 50 might prefer a product during fall, but a younger audience might prefer it during spring. By providing demographic data, trend analysts can help their companies meet demand intelligently.

Market trend reports informed by such data help companies anticipate trends that might develop in the future. As strategic business advisor Bernard Marr points out, “By practicing market analysis, you can stay on top of which trends are having the most influence and which direction your market is headed — before any major changes take place — leaving you well placed to surpass your competition.”

Social media data provides companies an easy way to access data that points to major trend changes. Demographic data allows companies to isolate audiences who might form a future customer base and figure out their preferences in advance. In turn, this helps them create production schedules that match that audience’s seasonal preferences.

Market Dynamics

The market a business operates in is subject to a variety of forces. Chief among these is competitor activity. Disruptive products introduced by competitors can seriously harm a company’s earning ability. A famous example of this is Apple eliminating the likes of Palm and Blackberry within a few years after the release of the iPhone.

Monitoring a brand’s social share of voice and comparing that to its competition helps trend analysts figure out who’s occupying the top of users’ minds in the market. Analysts can also correlate these trends to sales volumes and connect product improvements, marketing strategies, and discover broad market trends. These data also help companies build lasting relationships with their customers.

Given the fast pace with which consumer preferences change these days, traditional data-gathering techniques will leave companies playing catch-up. “Because so much of the world is sharing its opinions on every subject at all hours of the day, trends and markets can shift quickly,” says Meltwater’s Mike Simpson. “It is not just the customer of next year or next month that organizations need to consider — but the customer of the next day.”

Whether it’s trends in engagement, demographics, or competitor data, social media data helps analysts gain perspective on how the market is headed.

A Full Picture

Social media platforms offer a treasure trove of user data. Market trend analysts can mine these data continuously to connect business performance and consumer behavior. Social media gives companies a real-time, cost-effective look into their customers’ minds compared to traditional data-gathering methods.

Author: Ralph Tkatchuk

Source: Dataconomy

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 '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

Which types of analytics do business use?

Data analytics in businesses help uncover competitive intelligence, actionable insights and trends. Different types of analytics enable businesses to gain an edge over their competitors. In the “data first” era, data-driven insights and decisions have become the key drivers of business performance. This post reviews the different types of data analytics routinely used in enterprises.

The Raw Data for Different Types of Analytics

Every business collects a vast range of data, namely sales data, supply chain data, customer data, employee (HR) data, transactional data, and much more. The data sources or channels can be numerous—sensors, applications, surveys, emails, or chats. The data type can be structured, semi-structured, or unstructured. Businesses have to rely on data analytics to make sense of huge piles of collected data.

The raw data is collected, cleaned, and prepared, then analyzed for result-oriented outcomes. Data analytics can be applied to different business functions in different ways: predictions for the future; audience behavior trends for marketing; quarterly sales trends and patterns; viewer analytics for websites; customer feedback trends in social media, and so on.

Many types of data analytics are presently used across sectors like healthcare, banking, insurance, fintech, HR, and manufacturing.

The Various Types of Data Analytics

A Career Foundry guide states: “In some ways, data analytics is a bit like a treasure hunt; based on clues and insights from the past, you can work out what your next move should be.”

This guide also breaks up each type of data analytics as a series of questions about data, which makes the surrounding definitions crystal clear. Fortunately, global businesses have four basic types of data analytics available at their disposal for a wide variety of purposes. These four types are categorized as descriptive, diagnostic, predictive, or prescriptive analytics. If you are completely new to data analytics, this blog post is a good place to begin.

The different types of analytics:

• Descriptive analytics is commonly regarded as the simplest type of data analytics, descriptive analytics help explain what happened in the past. This type of analytics is especially helpful in understanding customer preferences and choices or which products or services performed well. Some examples: reports, descriptive statistics, and data dashboard.
• Diagnostic analytics looks at and attempts to analyze the “whys” of past events. In other words, diagnostic analytics investigates why certain things happened the way they did. This type of analytics can be helpful in identifying problems currently present within business operations. Some examples: data mining, data discovery, and correlations.
• Predictive analytics relies on historical data (trends, patterns, logs) to make predictions about the future. This type of data analytics can help in anticipating and planning for future problems, for example, risk assessment, demand forecasting, patient care outcomes. Predictive analytics can also help uncover probable opportunities for business growth and profit. Usually statistical models like decision trees, regression models, or neural networks use past data to predict future outcomes. Some examples include fraud detection, custom recommendations, risk analysis, and inventory forecast.
• Prescriptive analytics, considered the most complicated type of data analytics, will not only make future predictions but also recommend remedial actions for positive outcome; for example, risk mitigation. This type of analytics, requiring high volumes of data, can also be time-consuming and costly. Some examples: lead scoring, investment aids, and content recommendation for social apps. Here are some prescriptive analytics use cases.

Businesses first need to determine which type of data analytics they need for a particular situation before expecting the benefits. 

Use of AI and ML with Data Analytics

In the artificial intelligence (AI) era, one cannot think of any data-driven operation without the presence of AI or machine learning (ML). ML algorithms make use of artificial intelligence to learn how to predict by studying high volumes of past data. On the other hand, hybrid models combine a number of predictive analytics techniques to deliver accurate predictions. After selecting one or more models, businesses have to train the model with available data. The data often comes from a combination of internal and external sources.

In AI-powered predictive analytics platforms, the trained model is used to predict future outcomes. The actionable insights can be used to develop marketing campaigns, set prices for new products, or plan investments.

Social Data Analytics: Use of Social Media

With the rise of social-media channels for online shopping, two other types of data analysis have surfaced alongside traditional data analytics. These are sentiment analysis and customer behavior analysis. Businesses are now able to collect large volumes of customer behavior data in the form of  likes, tweets, or comments. According to this article about social media analytics (SMA), SMA indicates an “approach of collecting data from social media sites” for making enhanced business decisions. This process involves deeper analysis of social data.

Customer behavior analysis: Popular communication channels like emails, chat scripts, video-conferencing logs, and online feedback add to the endless cycle of customer behavior data. Smart business operators collect, store, and routinely analyze this data to better understand their customers—their likes, dislikes, tastes, and preferences.

Sentiment Analysis: This unique type of data analysis is used to measure the collective sentiment of a certain group of audience. Sentiment analysis helps to dive deep into customer behavior. This type of analytics can be particularly useful for marketing or customer service.

An essential step to “measuring social media success” is to align the goals of your social marketing strategy with developed KPIs.

Data Analytics in Action: The Basic Advantages

A common misconception about data analytics is that huge volumes of data are required for every type of analytics. The truth is even simple spreadsheet data in combination with descriptive analytics can lead to valuable insights. Data analytics offer these obvious gains to businesses:

• 360-degree view of and better understanding of customers
• Enhanced customer service
• Improved business performance (revenue, sales, customer base)
• Timely, actionable insights
• Competitive market intelligence
• Improved products and services
• Optimized business operations

Summary on Types of Analytics

According to this Geeks for Geeks article, “It is critical to (build a data analytic infrastructure) that provides a flexible, multi-faceted analytical ecosystem, optimized for efficient ingestion and analysis of large and diverse data sets.”

In addition to all the benefits discussed above, data analytics is currently the most critical driver of a data-first business ecosystem. Data analytics is widely used across sectors, market segments, and various business types and sizes. Data analytics is one core activity that enables a business to make better decisions, drive performance, optimize resources, and understand customers. 

Author: Paramita (Guha) Ghosh

Source: Dataversity