How to assess the value of big data
One room, four speakers and a lot to digest. Content, that is. But MOA knows how to put their customers first. At times, the MOA Summit might as well be a food summit. Casa 400 hosted a fruitful afternoon and, after an inspiring network lunch, Rene van der Laan (SAS), Olav Lijnbach (GfK), Vincent Pijpers (Tweakers) and Martin Strub Hidalgo (Google) took the floor in their quest to tell us about where we stand with big data, why we should tame it and… how we should handle it.
A rapidly changing field
Big data has created a gap between what we do and what we could do. In his introduction full of facts, Rene exemplifies. To get a feeling of the amount of data the world produces on a daily basis, think of a big wall of iPads, 20 meters high, from Amsterdam to San Francisco. And very little of this data is effectively used. At the same time, insights of Google show we’re still speeding up.
Whereas the Netherlands had a smartphone coverage of 27% in 2010, that percentage increased to 77% in 2014 – making the country ‘mobile first’. The average Dutchman has at least three Internet-connected devices at home and is expected to have 15 of them within the next 10 years. Moreover, adoption rates of new technology are speeding up; meanwhile devices are more interconnected. According to René, the Internet of Things has brought us to a moment in time where machines and sensors generate more data than their human users do.
The next hype: value
However, the rise of data is becoming common knowledge. That’s why Rene opts for the next hype. We should add the fourth ‘v’ to the ‘volume, velocity, variety’ vocabulary of big data we’re already used to. And actually, that fourth ‘v’ should be the basis for everything we do with big data: the value of it. That is, when relevant consumer value is generated through endless fields of binary input.
There is no doubt that there is a need for consistency to obtain this value. Martin shows us how Google’s analysis of a customer journey revealed no less than 39 steps from orientation to purchase. ‘Front-end’ websites have become more understandable for customers. Ironically however, the omni-channel, multi-device orientation of the customer gives companies a harder time to understand what they are doing. We already know that orientation towards products and checking order statuses usually happens on smaller mobile screens. Adding items to the online basket is a typical tablet activity, while the real deals still take place on desktops mainly.
During the launch of the iPhone 4, 12% of all searches for this product were on mobile, whereas that percentage went up to a staggering 51% at the time of the iPhone 6 introduction. Bigger screens have moved us away from our older desktop screens. More screens (e.g. smartwatches) will make the customer journey even harder to analyze. Consistent campaigns need to understand the omni-channel customer journey. They clearly monitor both online and offline touch points. The challenge of current companies is to clearly keep track, thereby never ‘disrupting’ a customer journey by presenting additional, irrelevant offers. When you have visited the BMW showroom to take a look at the brand-new 4-series, it is confusing to receive a ‘customized’ e-mail promotion for accessories on your current 3-series. With big data comes an increased need to approach your customers consistently, thereby providing added value to them.
All this makes big data more a matter of organizational changes rather than one of only technical change. Of all costs related to the use of big data, Rene argues, reorganizing your company will be by far the largest. Big data changes both strategy setting and your company’s design within and across departments.
The road to your target is no longer to be set from the start. A vast amount of data will need consulting to adapt your route while you are on the move. Get used to getting directions while walking and dare to throw away your map with a predefined, straightforward route. As Olav illustrates, companies will change rapidly. That applies to technology, but also to people. Marketers have changed from (m)admen to math men. Guessing based on gut feeling is replaced by relying on accurate predictions.
Big data changes the way your company is organized. Firstly, within departments. For example, the role of big data will change consumer research. Olav states that census research will take over research on limited samples. Still, big data is just data – not information per se. We should be aware of the ‘what’ – and not so much the ‘why’ – identity of big data. As Martin illustrates, leading brands know the ‘why’ of their customers. Qualitative insight generation will be a rewarding complementary activity next to handling massive amounts of data. In that sense, market research is more than big data. At the same time the opposite is true.
Second, namely, change occurs between departments. Martin indicates the need for a smoother collaboration between your services, marketing and research departments. Fragmented, vertical organizations are simply not ready to leverage the big data potential. As Rene also showed, we should move to horizontal platforms (‘ecosystems’) to connect ‘silos’ – in an attempt to dispose of separate, vertical systems for each discipline. For example, the customer decision hub of SAS is a platform where internal information (rules, insights) and external actions (all inbound and outbound customer contact) come together, in an attempt to ‘orchestrate’ all marketing actions consistently. You cannot disrupt customers at the ‘front end’ of your actions. And you don’t want your ‘back end’ (e.g. offline campaigns, searches, displays, and digital analytics) to lead independent lives either.
How to transfer this message?
Change is often a complicated message for your organization. Vincent shows how he mixed his psychological and IT background to ease internal thinking at Tweakers. And Tweakers, in the middle of growing from a garage start-up to a full-fledged professional company, gained some experience in that area. By approaching big data as a phenomenon as complex as the human brain, Vincent brings up perfect analogies. First, just like the brain, big data is about input, processing, storage and retrieval of a particular part of data. Neither the human brain nor the systems we have built, bring pure, objective information. Online monitoring tools may differ in their settings of reporting on a sample of visitors rather than on a census, covering all the people who ever visited your web pages.
Second, Vincent underlines the urge of connecting data by the analogy of our human senses. In recognizing objects, the human brain profits from linking ‘data points’ of seeing, feeling, hearing and smelling an apple. Merging data from ‘digital senses’ such as Google Analytics, Webtrekk and customer databases comparably give a better customer representation. The sum of senses is bigger than each of the senses in a separate silo.
Have we moved beyond the hype of big data?
Opinions may differ. Olav states that big data will only be productive 5 to 10 years from now. Using the Gartner Hype cycle, he argues that we’ve just passed the ‘peak of inflated expectations’.
But the fact of today is that even though 360° CRM systems have been here for decades, there is hardly one big retailer or bank that can give an exhaustive customer overview that is consistent throughout all channels. Whereas HEMA (analyzing your shopping basket) and Tweakers just have started leveraging the potential of big data, the German national football team embraced big data as their 12th man, improving the number of seconds of ball possession per player. Wherever we are, the answer to the big data hype is definitely not only technological. Horizontal ecosystems should become an increasing part of daily organizational life in this changing era. In the end it is about value – not data