Callcredit Blog

Is Analytics revolutionary or reassuring?

Consumer Marketing Data

This is an interesting conundrum and I’ll tell you the answer now, it is sometimes both, but more often it is the latter – reassuring rather than revolutionary.

If I know the answer, why have I posed the question you ask? Well, it’s for the same reason that we often undertake analytical projects! I’ll explain.

Let’s start with a simple definition of Analytics: Data Analytics is the science of examining data with the purpose of drawing conclusions about the information in that data. Analytics is used in many industries to allow companies to make better business decisions and in the sciences to verify or disprove existing models or theories.

It’s a simple definition but summarises what we do reasonably well, we get lots of data, often raw, we manipulate it to get information from it and we use that information to make better (marketing) decisions.

So if we were building a customer response model: we may look at responders and non-responders, create new variables from behavioural data at the time of mailing, append further demographic data, compare the profile of responders to non-responders, identify the key characteristics, build a statistical response model and apply it to the population to undertake selections.

Let’s be honest about this – if this is a customer response model, I already know before I start that the key behavioural drivers will relate to recency, frequency and value and that lifestage and affluence are likely to be key demographic drivers. What a shock when they appear in my model!

Am I oversimplifying, yes probably, and data mining (rather than data analysis) is more about sorting through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships. The thing is how often are those patterns and relationships actually hidden or revolutionary; if you sat down and thought about customer behaviour long enough, you’d probably come up with 80% or more of them.

Hence we come full circle, and the final part of the definition above, Data Analysis is about verifying or disproving existing models or theories. It is not always about coming up with some revolutionary answer, it is often about proving what you thought you knew is right, and providing the evidence to back it up.

Thus when you deliver a piece of analysis and the recipient says “I knew this already”, ask the following questions: If you knew it already what have you done about it? and how is this knowledge already feeding into the marketing you are undertaking? If the answer is, it isn’t, it is probably because they needed the evidence to back up their theories – Data Analysis by definition!

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