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Big data, new model

Big data, new model

By Frank Wolfram, Germany CTO, Geometry Global 

Massive quantities of data are accumulating on the Internet. This trend is a blessing for the implementation of marketing for two reasons. First, the insight gained from big data helps get closer to the communication ideal of right message, right time and right audience.

Secondly – and this is still not being discussed enough – it will increase the level of automation in the industry and thus boost efficiency. Such efficiency will drive optimised communications aimed at consumers along the shopper decision-making process.

But, while efficiency is important, the overall process and thinking needs to be considered on an equal footing. How is the data to be used? What should the consideration be at each stage of the process?

Geometry Global uses the "purchase decision journey" model which helps us to map the individual steps towards the purchase decision, starting with the need and ending with the actual purchase, identifying the key touch points for influencing consumer behaviour. This leads us to the right message, right time and right audience, exemplifying how big data can come into play.

Yet in an environment of increasingly fragmented communication channels (car buyers use an average of 18 different sources today, for example), we must bring the various data sources together in order to make the most of them. We start with online media data, which provides information on the path users take to arrive at their digital purchase, alongside what their intention is. We analyse searches, correlate them to web analysis data and build a complete picture of what users do along the purchase decision journey.

This then allows us to optimise the journey they take when experiencing a brand; for example, a homepage should welcome the user with exactly the content they were searching for. Web analysis data allows content to be optimised depending on which device is being used. Other data sources can then be further integrated, such as offline media data (like TV or print advertisements) which allows the superimposing of data over time to understand how they influence online consumer behaviour.

E-tailers like Amazon are leading the way with this data use. Capitalising on this information, Amazon is in a position where it can draw a comprehensive data picture of every individual customer, including where they were acquired, their consumption habits and their value. This makes it possible not only to optimise content, but also to target them with marketing.

The nature of this digital landscape, and the skills required to activate its opportunities, create a natural tendency for big data to be viewed from a technical standpoint. But anyone within analytics will have realised that big data isn’t just a technical issue; the correlations and emotive background data are, in fact, more important. The basic marketing questions of "who?", "what?", "when?" and "where?" haven't changed and should still focus on people.

The promise of big data is to be able to precisely answer the traditional marketing questions through automated behaviour analysis. After all, as long as human beings are being targeted, there will be no communication without emotion. Whether or not that communication will be produced solely by human beings in the future is a different question.

In this realm we are already seeing the development of increased automation of communication. For example, with the most significant update of its algorithm since 2001, Google is now able to understand the intention of a search. In other words, in response to the question "where to get the best pizza?" it knows to integrate the user's current location.

This personalisation is a simple yet effective indicator of the power of big data. Companies are now beginning to balance both the rational power of data and the emotive power of stories. So while the technology behind this is important to get right, this is only to further the end goal: engage with customers on a human level by thoroughly understanding their behaviour. This can only be achieved by knowing them and their behaviour and the patterns inherent in their online lives. Through this understanding, we can tailor communications that target like never before and engage precisely, activating behaviour on a scale never previously seen.

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