Exploiting Data: Don’t Forget To Ask ‘Should We?’
Exploiting data - which door should I open

Written by: Jon Stafford

I have over 13 years experience in creating effective BI that turns data into insight and value. I’ve worked in many industries, combining my extensive business knowledge and background in solution development to deliver successful BI strategies, solutions, training and coaching.
Exploiting data: Don’t forget to ask ‘should we?’

If you’re an organisation that sells products or services, you’ve probably already looked at exploiting your data in some way. Maybe you’re analysing customer demographics to better inform your campaign targeting. Or looking at customers’ buying histories to identify upsell or cross-sell opportunities. Or examining your website analytics to improve its effectiveness.

And It’s Not Hard To Do

Every year it gets easier and easier to exploit information to gain value. Data gets more plentiful and arrives faster (think Big Data and cloud storage). Analysis and reporting tools get better, cheaper and easier to use (think natural language queries and visual data discovery). Also, the analytics at your disposal get more and more powerful (think forecasting, machine learning and AI).

Doing complex analytics on vast amounts of granular data, used to be the preserve of the big players. For example, only multi-million turnover retailers could afford the people, software and hardware needed to extract customer-level insights from their data. But now every business, no matter how small, has easy and affordable access to these enterprise-level capabilities.

But Don’t Get Carried Away

So it’s never been easier to indulge your every data exploitation whim. It’s common to see meetings where a frenzy of brainstorming results in a host of exotic ways to wring every last drop of money-making juice out of the available data. But the one thing that never seems to be discussed as much as it should be is how these money-making schemes will be viewed by the customers who are supposedly ‘benefiting’ from them.

Beneficial, Dubious or Just Plain Creepy?

Approaching things solely from your organisation’s viewpoint is a blinkered approach. It can too often lead to a nasty taste for your customers. In my view, the effects of data exploitation on the customer lie somewhere on a continuum with ‘beneficial’ at one end, ‘dubious’ in the middle, and ‘just plain creepy’ at the other end. Some examples:

‘Beneficial’ data exploitation:

  • ‘Customers who bought this also bought …’, ‘People who looked at this item also looked at…’, and similar. People are happy with the source of this information (purchase histories) and it can give them confidence in their buying decision.  

‘Dubious’ data exploitation:

  • Location-based and habit-based marketing, such as offers that pop up on your phone when you pass a particular shop. Or that are sent to you on a Saturday morning when you typically go shopping. People understand that organisations can get information on their location and can track their habits. There is (or should be) a way for them to opt-out of this kind of marketing, if they find it annoying.

‘Just plain creepy’ data exploitation:

  • Searching for something on line and then seeing adverts for that same thing appearing on every web page you visit for days afterwards. This smacks of blatant profiteering to people. Someone is selling your browsing history with no easy way for you to stop it other than becoming an expert in browser privacy settings and VPNs.

Whilst all of the above can lead to an uplift in sales (and so be ‘beneficial’ to the organisation exploiting the data). Those in the ‘dubious’ and ‘just plain creepy’ categories can also cause you to lose customers that you offend (and who then vent their frustration on social media leading to bad publicity).

Even data exploitation in the ‘beneficial’ category can also backfire if it’s left to run unchecked. A situation that becomes more likely as machine learning algorithms and AI are increasingly left to their own devices. Not everyone appreciates a personally-targeted offer for a potentially embarrassing product. Just because they bought similar items to someone else who also bought that product, however much the offer makes logical sense to the algorithm suggesting it.

The ‘Should We?’ Test

To make sure that your data exploitation always lies in the (mutually) ‘beneficial’ category, always make sure you apply the ‘Should We?’ test when looking at ways to exploit your data. Ask the following three questions for each initiative you come up with:

  • Will it be viewed positively by the target customers? Check that it’s also something they would find beneficial, and not something that would potentially annoy them or worse, creep them out.
  • Is it an acceptable use of data? Whilst GDPR has brought this consideration to the forefront, it’s also something you should have been thinking about anyway. What will your customers consider the source of the data you’re using to be, and will they see this as an acceptable use of that data? Using their purchase history – fine. Tracking their location – hmmm. Exploiting their search history – perhaps not.
  • Is it ethical? Does it suit our brand? Does it match the way we want to do business? This is a more subjective test – ultimately does the initiative ‘feel right’ to you? The things you need to consider when answering this question are unique to your particular organisation of course. You’ll often instinctively know when something isn’t right. The trick is to remember to ask this question in the first place (instead of forgetting to in your enthusiasm for how much money you’ll make).

If the answer to all of the above questions is ‘Yes’, then the initiative has passed the ‘Should We?’ test – go ahead and exploit that data! If not, then either amend the idea or abandon it. You might not get any uplift in sales but you won’t lose any customers or suffer any bad publicity either.

A truly intelligent business doesn’t just find ways that it can exploit data to gain value. It also always remembers to check whether it should.

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