Editor’s note: Patrick Glenisson, manager/marketing analytics at Belgium’s KBC bank, is a guest blogger for Big Data for News Publishers this week.

When I attended last year’s edition of a Big Data in Retail Financial Services conference, everyone — speakers and participants alike — seemed to agree on one thing: We collectively hated the term “Big Data.”

Why? It’s too technical. It’s often misused by vendors. It creates confusion (especially with senior managers). It inflates expectations. And is often reminiscent of other failed “Big” IT implementations (data-warehousing, business intelligence, CRM …). 

Moreover, for banks, collecting, storing and using customer data is not a new thing: Credit risk scoring is common practice since the ‘90s. And most banks have undertaken one or multiple customer segmentation exercises, and have been building up a portfolio or marketing response models through their in-house analytics units. 

So, nothing new under the sun, right?

An opportunity to grab 

Yet a lot has change during the past five years or so: 

  • Customers are more than ever in the driver’s seat, visibly impacting loyalty and churn rates.  
  • Government regulation is impacting margins across a variety of sectors.
  • Schumpeter doesn’t sleep lately as non-traditional players keep disrupting business models.
  • And finally, the economic downturn weighs on consumer spending behaviour.

Also banks cannot expect anymore to gain (or keep) market share by just pushing a good product in the market. Rather, the battle for every customer is raging. More than ever before, this requires us to bring customer-centric thinking in the way we change, plan, and operate our business. 

The idea of customer centricity is not new either. Ten years ago (and since then continously), high-end consultancy firms were preaching this as the new normal. It took an economic turndown and some dramatic examples of digital disruption to create a burning platform for customer-led strategies at board level.

This is where customer analytics or business intelligence professionals have a unique opportunity to stand up. Without rushing to build a 360° customer database (another loaded term), they should aim at answering a list of key questions that can bring clarity in the link between customer data and business results.

Often the underlying raw data is available in-house. Sometimes it’s scattered across silos, sometimes its quality varies, sometimes it requires cross-silo thinking, but .... it’s there.

The toothbrush test

Over the past three years, my team at KBC and I have been working on leveraging what we had. In the years before that, we had done a great job of building a customer data mart, along with a portfolio of models that predict various cross- and up-sell opportunities at customer level.

But we were stalling on:

  1. The systematic adoption of these models by marketers.

  2. The integration of customer intelligence in sales support tools.

In other words, we had been trying to push our analytical products rather than considering our (internal) customers’ real needs. Push marketing, but on the inside. Trapped in silo-thinking, we too had forgotten the ultimate customer-centric mantra: “Be relevant.” 

This is where Google inspired me with its so-called the “toothbrush test.”

In one article describing Google’s product strategy, its head of M&A said: “We ask ourselves, ‘Is this something people use once or twice a day and does it solve a problem?’ That sounds like a toothbrush, at least for those of us who want to keep our teeth.” 

And that’s exactly what data professionals should be asking themselves: How can data, big or not, be made relevant for employees and customers on a daily basis? 

Linking marketing and sales

Evolutions in the business intelligence landscape (see, for example, Gartner Magic Quadrant for BI & Analytics 2014) now provide cost-effective options to rapidly deploy dashboards and interactive views on considerable sizes of data.  

We used one of these solutions to unlock the value of our data and data products to a broader audience of users — all while keeping in mind that answering business questions requires regular interactions and several iterations. Hence, an agile approach. 

For example, a regional director came to us with the hypothesis that the sales targets for his region were not in accordance with local customer potential. Our task was to confirm or reject this thesis, and provide him with insight into how his region’s potential compares to that of his peers. 

We linked our full customer database containing dozens of socio-demographic indicators, with our list of predictive scores and a list of sales performance indicators at regional level.  

In less than a week, we prototyped several interactive views through a visual analytics dashboard, published it on a central portal, and iterated two times with him over the results. By the Friday of that week, the results were on the agenda of performance review meeting.

He found the ammo he was looking for — the data supported his gut feeling. 

This approach also allowed us to standardise frequently asked queries and generate self-service dashboards — hereby freeing up time from our analysts that were in heavy demand. 

This, in turn, has created opportunities to start looking at business questions requiring more advanced data crunching, but also to spend more time guiding our internal customers through proper interpretation of customer data and analytical results.

It was a first step in a journey that is still ongoing within KBC. 

Think big, start small

Is this Big Data? Yes and no.

No, because it’s a far cry from the digital-driven business models displayed by the Amazons, LinkedIns, or Twitters of this world. But, as customer centricity is becoming an imperative, it is crucial to start gaining a company-wide understanding of the business impact of tiny groups of customers, and ultimately of every individual customer. 

Data, big or small, plays a critical role in developing this understanding. Just make sure that you start looking at what you already have.