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Big Data is what the market calls a large volume of data.
In the 1990s, large stores of data (Data Warehouses)
were alread...
02
The social media present a new challenge: these
days, companies have more external data on their
consumers than interna...
03
4Ps of Insights: a whole new
framework to deal with
Social Media Big Data
For decades marketers have used the famous 4 ...
Places
In the 20th century researchers used the zip code to
classify men and women. We picked Places in the plural
to diff...
YOU ARE WHAT YOU MENTION
Companies are also going beyond these basic
matching procedures and are using sophisticated
stati...
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Social Big Data

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How the analysis of Social Big Data has radically changed the way in which we monitor social networks and deal with business intelligence

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Transcript of "Social Big Data"

  1. 1. Big Data is what the market calls a large volume of data. In the 1990s, large stores of data (Data Warehouses) were already used by companies like Walmart to identify consumer habits from their choices at the point of sale. This was how it was discovered that parents who bought beer were also buying disposable nappies, allowing retailers to devise tied promotions. In the last decade, the Mayor of New York, Michael Bloomberg, also implemented a data warehouse for the city. The aim was to improve decisions by the public authorities. For example, which properties – some 900,000 in the city – to inspect to prevent the occurrence of large fires. The first step was to identify the different data sources that generate information about the city: on rented properties, medical emergencies, complaints about noise in the neighbourhood, crime reporting, even the sighting of rats. This data was unstructured and in different silos (different databases according to the municipal department dealing with the problem). It first went through a process of structuring to make manipulation possible. Then a statistical model was created to handle the data. Once the model was implemented, every new claim received by the city about a fire danger was checked using the algorithm of the new model. The result: the rate of reports dealt with that actually 01 | What Big Data is 02 | What has changed with the Social Media 03 | 4Ps of Insight: a framework to get started 04 | How to use the 4Ps of Insights right now 04 | Big Data applications What Big Data is represented an imminent danger jumped from 30% to 70%. Decisive forecasting enabled New York City to make considerable resource savings, since it was able to avoid sending out fire crews to false alarms. A similar problem also related to New York’s exploding manhole covers. The same problem has plagued other cities in the world such as Rio de Janeiro, and in the Big Apple it was also treated with Big Data. To find out which manholes were on the point of blowing, a large volume of different data such as charts of gas, telephone, electricity ducts, as well as traffic in the immediate vicinity was cross-referenced. Just like NYC, the American retailer Target also transformed its big data into insights, e.g. when it predicted whether its female consumers were pregnant a few months before giving birth. The process involved the analysis of a large volume of data on products consumed and buying habits, enabling Target to have a more personalised communication with the customer. There is even a story that one irate father called Target’s Marketing Department complaining that his teenage daughter had begun to receive offers of baby products by email. Some time later he discovered that his daughter actually was pregnant (and he didn’t know). In both cases, New York City and Target, unstructured data formed a large volume of data (big data) that, after passing through statistical analyses, enabled important insights to be identified into both current and future consumer habits. 01 SOCIAL BIG DATA How the analysis of Social Big Data has radically changed the way in which we monitor social networks and deal with business intelligence SOCIAL BIG DATA August 2013
  2. 2. 02 The social media present a new challenge: these days, companies have more external data on their consumers than internal data. There are more than 1 billion Facebook users worldwide. Twitter, with 200 million active users, generates over 400 million tweets daily. The volume of this information grows by the day. From 2008 to date, we at E.life have stored 600 million data points, or units of data, posts, comments and shares from Twitter and Facebook. More than half of this data has been stored since 2012. Just to mention some figures, there are more than 30 million Brazilian users (and they’re growing exponentially). The challenge is not only to have access to this data, but also to analyse the information differently. The first change is: the software is much more user-friendly and allows us to see everything, i.e. N=all. When looking at a large volume of data it is possible to use sophisticated statistical techniques and identify patterns of behaviour that could not be identified just by looking at last month’s buzz. One important change is that the market has undergone datafication, i.e. data-fying a phenomenon is to put it in a quantifiable format so that it can be tabulated and analysed. Today the more technological shields we use the more we ‘datafy’ our day-to-day lives. A simple Twitter bio or a single day of check-ins on Facebook provide more data today than mankind ever thought possible in past decades. Another change is the end of data silos. Just like New York City had to put in a single data warehouse information that belonged to different municipal departments, companies also have the same challenge. Withoutasingledatawarehousethatgathersconsumer information scattered across all the software used by the company, it will be impossible to gain insight into their behaviour. At this point, social networks have a great competitive advantage: consumer data about various dimensions of their behaviour can already be found in one place: what do they drink? where do they go? what do they watch on TV? What do they buy? It’s all there on the social networks. What has changed with the Social Media What has changed? Datafication = datafying a phenomenon is to put it in a quantifiable format so that it is tabulated and analysed. Datafication is different from Digitalisation. Digitalisation N=all. Today it is possible to analyse everything and massive amounts of data. Data sample No more data silos. Data from different sources in one enormous data store. Data silos Information can be re-used. The cost of storage in servers in the cloud allows you to save a large volume of data and reuse it, even if the data has been collected for some other purpose. Data used only once and then discarded ‘What’ is better than ‘Why’ for Big Data. Often we will not get the explanations for everything but we will have more certainty about what we are talking about thanks to the huge volume of data analysed. Why The end of having just one version of the truth. The data tell stories and the stories depend on the questions asked. In other words, get used to having many versions of the truth according to the questions you ask. A single version of the truth THEN NOW
  3. 3. 03 4Ps of Insights: a whole new framework to deal with Social Media Big Data For decades marketers have used the famous 4 Ps of Marketing to develop campaigns and communication strategies. No matter what kind of business, one can start a marketing strategy asking basic questions about the 4 Ps: Product, Price, Place and Promotion. Market and Opinion researchers, though, never had an equivalent theoretical Framework as simple as that; until now. At E.Life we are proud of outside-the-box thinking. That means we are also willing to make mistakes and learning a lot during the process until we can offer the best solution for our clients. After almost 10 years delivering Insightful reports exclusively based on social media data, we thought it was about time we attempted to summarise our work in the simplest way possible. We came up with what we think could be a thorough framework to start any Intelligence Project based on social media and general digital data. We call it: The four P´s of Insights. Preferences Bio Brand Mentions Intention/Action Apps RTs Places Location Check-ins People Bio Brand Mentions Intention/Action Personality Traits Pricing Price or cost Mentions Brand Mentions KEY INSIGHT INDICATIORS Preferences Pricing Ecommerce has become pervasive. Hordes of people shop at Ebay, Amazon or Zara or they get loads of information before going to a real store. Product prices float accompanying new trends by the hour. Social Big Data and traditional datawarehouses can set these prices by the minute. Hence we chose our second P, pricing instead of price to emphasize the current dynamics nature of how much money a company can charge for a product. Possible questions that can be answered with Social Big Data: • What is the real-time perception of whisky drinkers on the price of the drink in social networks? And how has this price perception varied historically in the last two years • What is the perception of consumers about the prices of airline tickets, by carrier/brand?? In a multi-choiced world people cannot be pinned down by classic demographics any more. We are back to being tribes of hunter-gatherers looking for food, fun, beliefs, and 1000 other things. The Social Media allow us for the first time to cluster people in Tribes, ranging from a few hundred people who like Catena Zapata wine to hundreds of millions that adore McDonalds and everything in between. We picked Preferences as our first P because it translates so well the new era of Likes. Possible questions that can be answered with Social Big Data: • What do consumers who define themselves as parents on Twitter drink at weekends? Beer or Wine? • Mothers who go to football matches usually shop at which supermarkets and support which teams? In a study of data on 59,000 parents on Twitter in 2013 in Brazil, we found that those who define themselves as a father in their Bio usually drink Heineken beer and most often support Santos or Flamengo.
  4. 4. Places In the 20th century researchers used the zip code to classify men and women. We picked Places in the plural to differ from this approach: people are on the move doing facebook checkins, taking photographs and tagging them in Instagram, tweeting their location and so on. The new demographic analysis must consider the digital geography of a consumer. Possible questions that can be answered with Social Big Data: • Where will the digital data go from Rio de Janeiro and São Paulo from check-ins on social networks? This has already been answered in an E.life study in 2012. • What is the difference in men’s and women’s behaviour in Mexico when they visit points of sale? People By stating their preferences online, consumers are leaving an enormous behaviour footprint. Again demographics from the 20th century do not suffice for characterizing the new citizen. Personality traits can be inferred from preferences and the other way around, helping companies to understand the fundamentals of the ever-changing consumer. We picked People as our fourth and last P, to make it clear that companies are dealing with very well-informed, complex and sophisticated human beings, who do not fit into old fashioned social-demographics anymore. Possible questions that can be answered with Social Big Data: • What food and drink consumption habits do vegan mothers have? • What is the difference between men and women in relation to media consumption on TV? And what is the difference between doctors and lawyers in media consumption on TV? How can you use the 4Ps of Insights right now? Start your research by asking questions in your domain for each P. Say you are a Shopping Mall. For the Preferences dimension, how about: which brands, hobbies, activities, TV shows are consumers that come to your Mall sharing online? For the Pricing dimension: Are people associating expensive/cheap and other price-related adjectives to any stores in your mall? Are there any trends you should be aware of to fine-tune your pricing? For the Place dimension: who is checking- in on your mall? Where else are they checking-in? And finally, for the People dimension: what personality-traits map into what preferences? Learn more about the 4Ps of Insights at www.4PsOfInsights.com Big Data applications SOCIAL TARGETING: TAKE ONE-TO-ONE MARKETING TO THE NEXT LEVEL Twitter has 250 million users worldwide, Facebook has 4 times more. Most consumers also carry their mobile phones and post photos on Instagram or inform their location via Foursquare check-ins, and most of this data ends up again on those two platforms. What if an insurance company knew that a given person who once visited their web site has just bought a car? Or a pet food company just learned that you tweeted a lot about your cat? Definitely a great way to generate new sales or branding, right? With Social Targeting this is now possible. Social targeting is a way to find out more about your site visitors by inviting them to use their Twitter or Facebook user name to log on to your web site. Both platforms allow developers to ask permission to see likes, bios and other data that can then be aggregated to find the best fit for each visitor. Say you are a Pharmaceutical company and the person who just logged in is a doctor (when we last looked we found 15,000 people who identify themselves as doctors on one of our monitoring projects). Or an ecommerce that learned you are a big scuba diving fan. In both cases you can use e-mail or messaging services from one of the two platforms to create a unique offer to your visitor and hopefully increase your conversion rate. 04
  5. 5. YOU ARE WHAT YOU MENTION Companies are also going beyond these basic matching procedures and are using sophisticated statistical processing to infer what a given consumer with a “mention story” might be interested in. One popular technique is clustering, where it is possible to group people according to their similarities. You may have the hipsters cluster mentioning Mad Men and Daft Punk or the game fanatic cluster mentioning top on-line games. A new visitor can automatically be classified under one of these clusters and successful past offers to individuals belonging to it may also work for this new person. Another technique is called association rules, where the algorithm infers things like: if a consumer is male and has mentioned an airline and a car brand, there is a high probability that he will respond to a credit card add. Precision increases with the amount of data a web site can gather. Amazon is the best example in the ecommerce realm and has been doing this for decades now. SOCIAL FOR REAL Socialtargetingcanalsobetakenintothefieldviasmart phones. At E.life we have developed a Shopping Mall check-in app with a twist: every time a user checks-in on a store he collects points to get discounts or freebies from that venue. Before he does it though, the app asks permission to get his likes or his Twitter info. Now the marketing folks are looking at the clusters generated by each store to improve their offers. The next logical step will be to create on-the-fly promotions as soon as a user does a check-in even including combined stores in the Mall. There is no doubt that we are watching a revival of the one-to-one marketing projects preached in pre-internet times. But now, with the ubiquitous mobile web and social platforms the outlook looks far brighter. Does your company web site have a social login? Maybe it is a good idea to have it. It is time to get to know each of your visitors more personally. More at: www.4psofinsights.com 05

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