Everyone knows Wally, the little man from Martin Handford’s puzzle book series. He’s instantly recognisable with his glasses, hat, striped jumper and walking stick. But you just can’t find him! Sometimes he’s hidden in a cake factory, other times in a flower garden, a circus, or even in Hollywood. OK, there are some people who spot Wally immediately, but most of the time you’ll end up staring at the drawing for quite a while.
It’s exactly the same with companies looking for their most valuable or promising customers. All the data you need to find Wally is already in the CRM system or the web shop software, but you just can’t pick him out. This is either because there’s just so much data, or because your resources are not linked and you have to search in all sorts of locations.
Fortunately, there is an algorithm that can handle the search work for you, sorting and ranking customers much faster than a human can. By the way, did you know that there is actually an algorithm that can find Wally in a split second?
Better results thanks to the RFM model
The analysis of your customer data is based on the RFM (recency, frequency and monetary) model. When did someone make a purchase? How often did someone buy something? How much was spent?
This analysis generates a list which not only finds your Wally, but also shows which customers are ‘sleeping’ and which are in danger of being lost. This will help you make Wally a personalised offer. If you are a commercial director, you can also use your marketing and sales capacity more efficiently. After all, the insights help you see which customers you should consider lost. There is probably no point in spending any more time marketing to them.
You can even go one step further in analytics by predicting demand. For example, if you know that customers who buy product x now will show interest in product y in the future, you can tailor your message accordingly and recommend that product directly.
Become a data-driven company
McKinsey shows that this way of working can help your organisation make the shift to a data-driven way of working. Insights emerge from customer data that, among other things, help to:
- Improve customer experiences.
- Minimise customer losses.
- Increase customer loyalty.
- Optimise customer acquisition.
- Increase customer spending.
Want to learn more? Download our e-book, ‘How to work step by step to increase customer satisfaction and growth with customer analytics’. Or drop us a line!
/1.%20Visuals/Website/Icons/x-twitter.svg){kind=icon}
