All businesses want to better understand their customers: their needs, their behaviour, and the frustrations they face when dealing with an organisation.
Customer insight comes from compiling and combining data, from a range of different sources, to paint a picture (see fig 1.) But for analysis of this data to actually be considered insightful it has to be useful in some way. Certainly, analysis that reveals something interesting about a customer might tickle the brain, but what value is it really adding? True customer insight must go beyond showing something interesting, to showing how a business can actually use that information to better connect its brand with its customers. Insight should help drive the company’s organisational thinking and decision making.
From a reasonably simple statement — the desire to understand customers and gain insight — unfolds the complexity of delivering it. The creation of customer insight should not be left to a small, tactical initiative undertaken by a team working in isolation from the business.
Instead, it requires an alignment between multiple departments of a business — IT, marketing, analysis, product, operations and customer services — across its organisational goals and investment in people, technology, systems and processes.
Analysis and insight are not the same thing
While the words are often used interchangeably; analysis and insight aren’t the same thing. To illustrate the difference between them, consider a direct marketing campaign running two offers:1. At a basic level, the response rate for each offer can be measured. Offer A resulted in a 6% response and offer B in 8%
2. In the champion/challenger approach, offer B would become the champion as it had a higher response rate (assuming everything else was equal). Analysis would suggest, therefore, that any new offer would be tested against offer B
3. However, if we take the analysis a stage or two further, a customer profile of the responders might show that very different ‘types’ of customers responded to offers A and B — and that both these groups had different characteristics to non-responders
4. Therefore, a greater response rate for both offers A and B could be achieved in a future campaign if the offers were more tightly targeted towards customers possessing the characteristics of the original responders to offers A and B
The ‘insight’ comes from modelling what the ROI would have been had the knowledge gained been applied to the original campaign. That is, applying what you know now and arming marketing with the facts to confidently continue with offers A and B — and perhaps identify another appropriate offer C for non-responder.
This post is sourced from The Datamine Guide To Creating Customer Insight, which you can download for free to the right. The Guide introduces what is meant by customer insight and lays out the building blocks for creating it, the skills required to create an effective insights team — and details some of the commonly used analysis techniques for uncovering insights in data.