Putting Data At The Heart Of Your Business

What separates established businesses that really embrace the power of data from those who don’t is the attitudes of the leadership team, writes Ian Shepherd

Hang on, why is the average always wrong? In a marketing presentation you might hear that ‘the average customer visits us 2.3 times a month’. That’s a comfortingly precise number, and it is easy to move on to a discussion about how you might increase it to 2.4 times a month, without giving too much thought to the underlying data.

But what is an average anyway? What does the figure mean, how should we interpret what an ‘average customer’ really is, and what might this simple overall figure be hiding? Consider two scenarios:

In the first scenario, most customers really do come between two and three times a month. Your business is an occasional visit for almost all your customers. By diving into research and customer feedback about how people use your products, you can chase that increase from 2.3 to a higher figure.

In the second scenario it turns out that 10% of your customers visit you 20 times a month, and the remaining 90% only visit once every three months. That generates exactly the same 2.3 overall average frequency, but obviously reveals a very different story. In this reality, you’ll want to know a lot more about why some customers visit so frequently. Are they different to the majority, or is it that the customers who visit your business so rarely are actually equally avid buyers, but from your competitors?

Getting a bit further under the skin of those simple overall figures is of massive value to business leaders. It is not the average of a set of data that is interesting, but instead the variances and discrepancies to that summary figure. When someone tells you that your average customer spends €100 with you each year, shops three times each year and has been a customer for 2.5 years, they are giving you only a tiny part of the story.

In reality, almost none of your customers behave like that. What’s interesting are the differences

llbetween customers. The great power of modern data science is the ability to use complex algorithms to understand the real richness of data and what it tells you.

Underneath all those averages in presentations is the underlying data. Data is a topic that consistently dominates discussion of the fortunes of consumer businesses around the world. How much of it does a business have? How good is its loyalty programme and what benefits does that programme deliver? Is the business deploying the latest cuttingedge machine learning and artificial intelligence to increase its profits? How customer-centred is the business, and how does it use ‘big data’ to achieve that?

There is a good reason for all that focus on data. The rise of the pure play online business, powered by the latest technology and unencumbered by costly stores or legacy investments, poses a huge challenge for established consumer businesses the world over. And as if all the other advantages these new online businesses had weren’t enough, they are awash with data.

Since it isn’t possible to buy from an online business without providing them with your email address and quite possibly your actual address too, an online business acquires a customer database almost by default. No surprise, then, that they have used that data advantage ruthlessly — building predictive models and generating insights that enable them to squeeze just a bit more spending out of each customer than their mortal competitors can.

What does it take to turn a consumer business into a data-led one? That’s the big question facing many leadership teams, and it can be an imposing and uncomfortable one. Many business leaders in retail have grown up with a sharp focus on supplier relationships, buying and merchandising, but with relatively little real customer data flowing back into decision making.

For many management teams, the ‘data discussion’ can feel like a strange and alien one. As a result, some businesses shy away from investing in