Big Data: Why more data is better for brand loyalty and customer experience

We’ve recently started talking to a brand which has around 700,000 customers in its database. They have collected lots of behavioural data, by which I mean transactional data – recency, frequency and value (or RFM) – and response data. This response data is all about what happens when the customer is sent a piece of communication, in this case an email. What they do, when they do it, where it leads. Say the database contains 30 fields. That’s 21 *million* pieces of information, all tied together to create a big fuzzy room we can in effect walk around, try to make sense of, and manipulate to achieve commercial goals. 21,000,000.

Everyone talks about Big Data as if it were some kind of technological nirvana. The reality is you can gather data from a whole lot of sources and stick it all together more or less by hand, if you need to. In practise, Big Data is shorthand for the notion that if only you could mine, interpret and extrapolate all the data you could get you’d have some kind of joined up living solution to customer engagement, almost a mindmeld between your brand and a collective representation of your customer base in its entirety. Nice.

The reality is that data is an enabler, something you can make use of – not something that should make your decisions for you.

So how does this pragmatic approach work? There are a number of critical steps to take you  from having on the one hand a commercial goal and on the other some customer data. First, make sense of the data. Customer insights start with understanding what kind of data you have. In our CRM terms this information breaks down into three broad groups:

Demographic – who the customer is
– Gender, age, life stage
– Location
– Income
– Status
– Family make-up
– Education etc.

Behavioural – what they do
– What they have bought
– When
– In response to what
– How much do they spend
– How long is their ‘customer lifetime’
– What channels do they use
– When do they respond most

You can see already that by combining some of this information you can infer quite a lot about the way you might want to talk to some of your customers. It is obvious that you can start to create segmentation based on demographic and behavioural data. However, this approach to segmentation may help you to be efficient (behavioural) and accurate (demographic) in who you talk to, but it often does not tell you what to talk to them about.

Taking the classic example of customers of a prize-based fantasy football league, segmenting by these two dimensions might lead you an easy segmentation based on whether the customer buys one or twenty teams (behavioural) and jump to conclusions about their financial status (demographic).

3D segmentation adds a new aspect, motivation, to the mix. If you can divine what motivates your customers then you can speak to them using motivation-based segmentation and that may actually provide the cut through that’s required in a highly competitive environment.

Motivation – why they do it
– Need state
– Environmental factors

This dimension can change based on changes in the other two dimensions; for example changes in family make-up or life stage may radically alter someone’s drivers for engaging with your brand.

In the case of the fantasy football league, by looking not at behaviour or demographics (which didn’t appear to correlate) but by motivation, through the simple expedient of a brainstorm with everyone we could find near the meeting room we reached an insight we could test – first by checking the correlation with the behavioural data, second by sending a brief questionnaire to a standard sample. The insight was that customers bought principally because they were either motivated by passion for the game (bought a single team) or by the desire to win the prize pot (bought twenty teams).

By using this simple insight we created two segments serving two types of (relevant) content. These were then split into time-based sets based on where the customer was in the product lifecycle (new joiners, mid-season etc.) so we had six or seven simple segments.

Revenue went up 93% in 90 days. The client was The Sun.

The job of data is not to confuse or confound. The job of data is to allow you to extract simple insights that allow you to run singleminded campaigns that tap into your customers’ motivations so that they want to engage with you. As we start to think beyond the age of CRM and focus on rapid growth, it is imperative that Big Data doesn’t become an encumbrance. Data should be there to provide insight so you can get on with the engagement – because how you engage with your customers is the only thing that will drive your success.