The fact is most analytics boils down to one simple thing that anyone can understand.

It’s not technically advanced. In fact, it’s an action we as individuals do every single day.

Compare two things.

All personal and business decisions are, to some extent, based on a comparison of two or more options, starting from “do I want eggs or cereal for breakfast?” – we are all used to approaching things in this way. You’ve been practicing this all your life, and you can apply it to data analytics.

 

When approaching a problem, look for two things to compare and you’ll usually find a good analytics project to help you make a better decision. You’ll be surprised how well this works and how it’s applicable to pretty much everything. Even building advanced statistical models is mostly about trying to find good comparisons – for instance, churn modelling attempts to compare the similarity of people in your customer base to those customers that have churned in the past.

Let’s look at some of the common types of analytical comparisons you might use in marketing which can inform a quality analytics project and guide your analyst team on what to focus on. This approach to analytics works just as well in logistics, manufacturing, service delivery, and pretty much anything you can think of.

  • What’s going on in our customer base?

Compare profitable customers to unprofitable customers, churners to non-churners, loyalty programme customers to non-loyalty, segment movers to those who stayed put, customers who used to be high value to ones that still are, and the list goes on…

  • How can we get more ROI from our marketing spend?

Compare campaigns that had better than expected results to ones that underperformed, responders to non-responders, sales patterns during discount days to those on non-discount to see what products are more price sensitive, and more…

  • How can we drive acquisition?

Compare good customers to prospect lists to identify those worth your effort, response rates for one kind of campaign versus another, customers who abandoned a sign up process to those who didn’t, and so on…

Although there are varying degrees of complexity involved in each individual business challenge, I’ve found that comparison is something you can always fall back on to get a better understanding of what’s going on. It’s simple, understandable, and clear to everyone.

A key step towards empowering yourself to leverage data analytics is overcoming the misconception that data is too complicated for the average business professional to grapple with.