Is your business drowning in data?

Posted: March 7, 2016

Is Your Business Drowning in Data? – by Jacques Burger, CEO of M&C Saatchi Abel Gauteng

We live in an age where information is readily available…and without doubt, there is lots of it. Indeed, it is estimated that in 2011 alone, 1.8 zettabytes of information was created, which is the equivalent of 200 billion HD movies –and this figures doubles every two years. But if information is multiplying and so easy to access, does it still represent a true competitive advantage in business today – and are we not perhaps investing a huge amount of money and effort Jacques Burgerinto collecting data that is irrelevant when it comes to doing business better?

Some business leaders argue that all this data leads to decision paralysis rather than insight and business savvy. So maybe author Malcolm Gladwell is right when he argues that the world needs less information – not more?

How Much is Enough?

Maybe a good starting point is to understand how much data you really need as a business. Today, the potential for data capture is infinite and it often seems that the data strategy of companies is shaped by wanting to know everything about their customers – as opposed to wanting to better understand them.

In my view, understanding the drivers and motivators that inform the relationship that your customers have with your offer/ brand is vital in being able to ask the relevant questions. So, if you could ask only five questions of your customers, what would they be?

Is knowledge of where your customers live and work important? Or whether they have children? Or to quantify their buying power? Our obsession with data has led to a mushrooming of information that is largely irrelevant to many of the businesses that spend millions collecting it! Savvy companies are far more focused and discerning around how they collect data – which means that the data becomes far more manageable – and far more powerful.

Data Tunnel Vision

Once you decide on what data to collect, the next challenge is to understand and then get rid of what I call Data Tunnel Vision. Take this scenario into consideration. An innovative organisation recently collected a rich set of data around their customers in order to determine their value to the organisation. They ranked the clients based on the value of the investments they had with the company, the number of products they held with the particular company and the number of years they’ve been a client.

Based on this information, they then developed a model to tier these clients and service them accordingly. Naturally, the most valuable clients received the platinum standard level of service – tailored events, personal service, added value benefits etc. – while the bottom tiered ones received the basic package – call center access and a birthday card once a year. What the company failed to interpret from the data was that some of which could potentially be the most valuable clients in the future to them, were now grouped at the lowest service level in the organisation because of the particular segmentation.

These clients were on average 2-3 times wealthier than their platinum grouped clients and they held bigger investment product portfolios than most of those in the platinum group – just not with this particular company. So whilst the data reflected their current relationship with the business, it was not reflecting their potential relationship.

Sadly, the data collected by companies is reviewed and interpreted in a company centric way vs. a customer centric way – which often results in flawed and harmful interpretations. The data weighting on the current customer versus the potential customer creates a risk of developing a strategy for the business that excludes potential growth opportunities and obscures organisational weaknesses.

It’s like the shoe shop that collects customer data that shows its male customers love buying pink lace-ups. So the business advertises the pink shoes more aggressively and stocks more of them – and the data shows that their customers love it. However, there is a market out there 50 times the size of their customer base that would never consider frequenting that shop because these customers all wear brown and black shoes, which this outlet doesn’t stock – because the data indicated that people don’t buy them.

Ultimately, data cannot make business decisions for us. It cannot write a business strategy – and in the space of innovation in particular, we need to know when to listen to data, what data to listen to and what data to ignore. As leaders of business, we remain the decision makers.