Predictive RFM?
Does the RFM model using event probability data make sense?
Proper customer segmentation for any communication should be the first step in building a scenario. It determines the time, the channel and, most important, the content of the message. One of the basic segmentation models used by businesses is RFM(E) which tries to provide information on which stage of the interaction (funnel) the customer is at.?On the other hand, there are predictive models available that answer specific questions related to the probability of purchasing or performing a certain task (subscribing to the newsletter) and churn.
Will the combination of these two segmentation approaches into one analytics called Predictive RFM is really possible?
Let's start with the input data and mapping the classic RFM dimensions to the new ones resulting from the prediction.
R - Recency -> Propensity to buy prediction (predictions window infinite)
F - Frequency -> Number PV prediction (prediction window 90 days)
M - Monetary -> Monetary prediction (prediction window 90 days)
In the next step, let's divide the users based on the scoring from the prediction into equal segments. As in the classic model, we get 27 segments that I suggest grouping into 7 larger sets and focusing on:
Skip the line -> UX
Frequent buyers -> Loyalty Club
Buyers -> Price & Product
Hunters -> Price
Viewers -> Product
Random users -> Marketing Consents
Discount kings -> Discount & Promotion
Don’t forget, each business is different and in each of them, you can look for different segmentation criteria.
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(Un)lucky number
this is how many segments we have divided users in the predictive RFM analysis
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Use (This) Case
An old Chinese adage says...
"Not every user is a customer, not every visit is a transaction."
~Yì píng pí jiu
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Business Value Services Director,?Dominik Krolikowski
Business and Marketing Engineering.
1 年Many people in retail use RFM. And that practise can ease the transition to using predictive RFM with CDPs.