Stop the buzz

In the afternoon of September 12, the MOA Digital Analytics session was set to answer the urge to transform a fuzzy phenomenon into real-life cases. It’s an honor to be guided on this journey by authorities Mark Raben, Director Innovation & Product Strategy at SAP and J. Graeme Noseworthy, Strategic Messaging Director Big Data at IBM.
In a compact yet very interactive setting, Mark kicked off with pointing at the pace with which technology has evolved in the past 20 years. Understandably, his young daughter had a hard time explaining what that red telephone cell in London was used for nowadays: does it provide better network coverage or is it for charging a smartphone? We moved rapidly in technology, very rapidly and without a doubt Mark and his fellows at SAP had their share in that.
Mark then walked us through a wave of big data examples in sports, starting with demonstrating how today’s sports brands merged successfully with the customers’ usage of their running shoes. Their coaching app goes beyond providing you with info on the bare clothing essentials for running. Their virtual coach gives you all kinds of progress indicators, making the brand an actual running partner. And whereas a few years ago we could collect data and analyze it afterwards, the whole show now goes in real time. Online shoes and clothing retailer Zappos adds to this idea by developing shoes with integrated sensors to objectively measure the performance of their product.
SAP seizes the opportunity to apply big data in sports. Many of their projects in this field have two things in common: increasing performance of sports people and fan engagement and thereby also promoting the abilities of SAP. Introducing a highly sensitive subject in the heart of Amsterdam with fresh memories of the World Cup football in Brazil – Mark shows us their high-tech innovations as implemented at the DFB (Deutscher Fußball-Bund).
Backed by the delicate camera technology of Panasonic, their software neatly identifies football players in real time, giving a tremendous amount of statistics about both individual and team performance. Presenting the current football match on a timeline, Joachim Löw (manager of the German national team) could even replay parts of the match on his iPad during the match. Although that’s fascinating in itself, the real value of it goes beyond these descriptive statistics, namely: big data made real-time predictions possible. For example, visually indicating the current area that a defender could cover. Think of it as watching a FIFA video game in the real world.

Next to similar examples in sports ( analyzing every shot on target, Sailing Analytics showing detailed statistics measured by drones), McLaren is a case in itself. As a design-thinking company, SAP started off with the persona of their customer’s customer: VIP guests at Formula 1 events. In an augmented-reality view (a digital layer through your tablet’s lens), those guests saw real-time performances of the high-speed cars, ranging from tire pressure checks to oil temperature changes by milliseconds. The VIPs were literally fueled up to give their own analysis – even to the driver at the meet & greet afterwards.
And still, that’s not all. SAP supported BMW in using big data (e.g. former preference, loyalty to brands, plans ahead) for predicting which gas station a driver probably wants to go to – moving away from the traditional approach where a set of gas stations was simply presented as the driver’s choice to make.
Graeme ‘that big data dude’ is up next. He takes the floor to present the IBM view on things. And as he humorously demonstrates, that does not necessarily mean presenting stuff with hundreds of words per slide. He stays in the context of Formula 1 for a while, showing a video of a pit stop in the 1950s, compared with one of the current ones. With the help of data, human-centered effort decreased tremendously throughout the years.
We shift to the field of ‘C-level’, which shows us that most CMOs feel underprepared for the deluge of data. We have changed gears in moving from analyzing subsets of data to analyzing all of it. However, it is not the technology, but the customer who is central in all success factors for dealing with our data-driven emerging digital economy:
1. Emerge in advanced analytics for deep customer insight
2. Define a rewarding customer experience
3. Effectively execute on the customer’s promise
According to the passionate Graeme, the vast amount of data collected at each point of the consumer’s journey (think of data on how a consumer compares products online or how they move through a store) enables adding value at each step in that journey. That implies more than just personalization (“Hi Mr. Smith”). It stands for creating an experience (e.g. a web environment) that is completely customized to each individual. For an integrated approach, breaking down silos within your organization is key. For those who think to excel on such a strategy: customer centricity and relevance through smart big data application is something your brand buyers assume. In the US, consumers at the airport would even be dissatisfied when not being offered something personalized. And as they – rather than your company – hold the keys to your brand, you’d better join the race.
Catalina Marketing is successful in offering shoppers a uniquely tailored coupon on their checkout bill. At the checkout of a retail store, millions and millions of columns and rows of data are used to find patterns in the current groceries in their bucket, the geographical area shoppers live in, all the groceries they bought before and many more details. And all of that happens in milliseconds.
But let’s not move too rapidly. Most companies would preferably implement a comparable couponing system overnight. The truth is, however, that many companies are still at their very first step: simply integrating data sets across the organization. A step to take very seriously, in this ‘unify to utilize’ era. Graeme advises to think big but to start small. Without a doubt aware of Moore’s Law, he opts for investing in your data infrastructure up to 18 months ahead.
If one thing became clear this afternoon, it is that the exponential growth and smart use of data has brought us remarkable innovations. However, in many examples we should also see a trend in disclosing, smartly applying and combining data – as opposed to seeing it as completely new sources. Mark brought up an ‘industry swop’ as an example: looking for big opportunities in combining data from previously unrelated industries (e.g. banks and food retailers).
For those in market research who foresee a Matrix-alike scenario of machine learning surrounding all of us, Mark and Graeme have a calming message: not only technology, but also tools and talent are required for successful data strategies. It takes one year to build a billion dollar oil tanker – it takes 30 years to raise and educate the captain who can navigate it. And indeed, the nature of and human intervention with collecting data is bound to change. Moreover, hypothesis formulation and sharply predefined analyses on historical data will make room for more pattern finding, explorative algorithms on data that continuously flows in.

Nonetheless, the human role in capitalizing the complementary power of quantitative and qualitative insights is here to stay. The moment a box talks to a device to make an independent decision, something has gone terribly wrong

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