Data-Driven Marketing

Making Analytics Data Actionable: It's the customer, stupid!


A post from Webtrekk Country Manager Benelux

Fred Pellenaars

 

Remember Pareto? Eighty percent of your sales come from twenty percent of your customers. You’re in business largely because  of a fraction of your customer base: your best customers.

But who are they? And what to tell them when and where?

The foundation for making these decisions is customer segmentation. Not all visitors have purchased identical amounts. Some have ordered more recently, some have ordered more often, some have ordered more, period.

Consequently, not all visitors should be contacted with the same effort and expense. And the methodology for figuring out which ones to contact is RFM: Recency, Frequency and Monetary value.

RFM methodology in a nutshell

RFM uses primarily sales data to segment a pool of visitors/customers based on their purchasing behaviour. The resulting customer segments are neatly ordered from most valuable to least valuable. This makes it straightforward to identify best customers.

The logic behind RFM is quite simple:

  • Customers who have purchased from you recently are more likely to buy from you again than customers who you haven’t seen for a while.
  • Customers who buy from you more often are more likely to buy again than customers who buy infrequently.
  • Customers who spend more are more likely to buy again than customers who spend less.


Using web analytics data

Based on your own defined RFM model, you segment in order to identify groups of visitors with the same buying behaviour. The goal is to give each group the right message through the right channel at the right time.

 


In this example based on Webtrekk's Digital Intelligence Suite, you see that by using RFM modelling based on user-centric digital analytics data, you are able to set up a specific campaign for a specific group of visitors (in this case, a banner campaign for “First and low value buyers ”).

Instead of focusing on page views or funnels, you are now actually focussing on your clients' behaviour and using your data the way it is supposed to be used – on the user level.

Check!

RFM modelling has been acknowledged as a successful model to segment groups of customers and predict buying behaviour. You are totally flexible in the way you set up your marketing campaigns – a big spender at an online socks shop won't be a big spender for an airline – and you can target accordingly.

This enables you to increase efficiency and maximise your budget. It goes without saying that you should check the uplift in sales is as the outcome of your efforts. If you can't see whether your marketing efforts make a difference, why even bother, right?

Conclusion

As a customer segmenting tool, RFM is a great methodology.

But if you use RFM exclusively as a basis to contact your best customers, your lowest ranking customers will never hear from you (churn!). Your highest ranking customers will suffer from info overload.

So you have to develop a customer/visitor contact strategy so that everyone gets the right message and attention.

You should basically value the RFM segmentation technique for what it is: One of the first steps to make analytics data really actionable and improve your decision-making. But a first good step.

You can connect with Fred on LinkedIn or Twitter!

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