Most analytics boils down to one simple action that anyone can understand.

It’s not technically advanced. In fact, it’s something we as individuals do every single day - comparing 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?” or "Should I drive or bus to work?" We are all used to approaching things in this way. You’ve been practicing it all your life, and you can apply it to data analytics.

When approaching a problem, look for two things to compare – in doing so, you’ll usually find a good analytics project to help you make a better decision. 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 three common marketing challenges and the different comparisons that can be used to inform a quality analytics project to solve them. This approach to analytics works just as well in logistics, manufacturing, service delivery and many other business sectors.

1. 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…

2. 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…

3. 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 straightforward 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 – once you’ve conquered that fear, you’ll be on your way to implementing proven, repeatable success in your organisation.