How to run a simple RFM analysis using Excel
Here’s a step-by-step tutorial to slicing-and-dicing your structured transaction data to uncover new customer segmentations, using just Microsoft Excel
I won’t bore you with the benefits of an RFM analysis.
You’re here because you understand the benefits of running an RFM analysis to weed out new customer segmentations you didn’t know existed. You want to drive greater personalization in your campaigns with these segmentations.
The following is a simple way of running an RFM analysis using structured customer transaction data from your CRM (or otherwise) and Microsoft Excel.
You’ll definitely want to use a more robust data tool if your datasets are large, but this is at least one way (of many) to build some very compelling lists for campaigns or an asset to use to build buy-in for marketing technology spend.
E-Commerce types sitting on a lot of customer transaction data will find value in this step-by-step guide to running an RFM analysis that has the potential to unlock very valuable (and neglected) customer segments.
Here’s how to do it.
1. Gather Customer Transaction Data
You will need transactional data sets tied to unique customer identifiers (email address or customer numbers), typically pulled from CRM and/or POS (point of sale) solutions; the data contained in this transactional data should be relational to unique customer identifiers, for example, connected to a customer ID or email address.
If you are starting with transactional data that can be tied to whatever unique customer identifier is used at your organization, you can easily build your own RFM model to backbone automated engagement scoring or run it to build some intriguing lists for campaigns.
Let us start with the possibility you are pulling customer transaction data that needs to be prepared for manipulation.
