How do I implement the RFM Model?
With RFM analyses, you can precisely determine the value of each customer. In the previous blog, we segmented your customers based on transactional data. Now, let's work with those different segments. Which approach will you choose for a 'Champion' and which for a 'New Customer'?
Once the segments are clear, you can devise a marketing approach for each segment. Below are detailed approaches for five of the eleven segments from my previous blog.
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To make your campaigns even more tailored to the customer and thus even more successful, you can use additional customer data: data stored in your CDP, CRM, or email package. Think, for example, of interests, preferences, and the geo-location of your customer. In which region are your 'Champions' located, which communication channel do your 'New Customers' prefer, and what life situation are your 'Hibernating' customers in?
Measure your RFM results
Measure to know. Measure the success of your marketing actions on these segments. Ensure a good baseline measurement. How is the ROI of your campaigns? Is the RFM score increasing thanks to your efforts? Are you actually extracting more value from your customers? How is the desired migration per customer segment progressing? Have you compared to the previous period, fewer 'Hibernating' customers and more 'Champions'? Have you managed to win back 'Can't lose them' customers?
Ensure that you can monitor these numbers on your marketing dashboard and share them with your team. Creating a dashboard is not difficult. It can be done within BlueConic or in an Analytics tool. However, determining what to include is key. Start small and tailor what you show to the audience of your dashboard: managers want KPIs, while data marketers want to see segments and growth.
With such a dashboard, you make visible to all your colleagues what you and your team are working hard on. This contributes to the team spirit, but even better is that you can get feedback and tips based on the data you show.
For example, the two-dimensional figure from the previous blog gives you a nice overview and is useful for your fellow marketers. But also, a table like the one below provides a pleasant overview.
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The limitations of RFM
I'm really excited about RFM, but there are limitations too. In the previous blog, I showed you that the calculations can be quite complex without the use of software or a tool. Fortunately, in the most advanced Customer Data Platforms, such as BlueConic, you can automatically and in real-time link transactional data to customer profiles.
Another limitation is that RFM is not useful when you offer only 1 product or when most of your customers are one-time buyers. It's also not suitable for determining who prospects are; after all, you're looking at historical transactional data.
More effective marketing activities
Regardless of these limitations: with RFM, you learn which customers deserve more attention, where the opportunities lie for cross-selling and up-selling, and how to best deploy your marketing activities. Start small, for example by focusing on one or two segments, and then expand your RFM activities per segment. Do you have any questions? Feel free to contact me at niels.deveth@gxsoftware.com!
If you want to extract more predictive value from your transactional data, the Customer Lifetime Value (CLV) model is suitable. Check out my next blog!
