Are You Missing Important Signals in Your Data?
RFM analysis is a marketing technique used to segment customers based on their purchasing behavior. It helps businesses identify their most valuable customers and tailor marketing strategies to maximize customer retention, engagement, and profitability. RFM stands for Recency, Frequency, and Monetary value, which respectively represent the time since a customer’s last interaction, the number of interactions, and the total money spent.
Despite its popularity, RFM analysis has limitations, as it oversimplifies customer behavior by only considering three dimensions. This may lead to overlooking other factors affecting customer engagement and missing opportunities to target customers more effectively.
For example, some marketing campaigns assume that if I’m a fifty-something man purchasing a product for a child, I must be a grandfather. Other misguided marketing campaigns have tried to sell me shampoo. I’m not a grandfather, and I don’t have any hair to shampoo!
If you’re looking for hyper-individualization, it’s important to consider as many diverse signals as possible. To improve the signal-to-noise ratio in your feature lists, here are the top five feature engineering ideas you can try:
- Clumpiness: does the customer purchase small amounts regularly or infrequently purchase many items?
- Similarity: how different is an individual’s behavior from their peers?
- Stability: how has the individual’s behavior changed over time?
- Timing: how regular is the timing of events and transactions?
- Diversity: how diverse are the customer’s product purchases, and do some products dominate?
Experimenting with these features and serving them in production can take a lot of time and energy. We’ve built an open-source feature engineering library that makes it easy to create these five signal types and more! Click here to access our open-source SDK, with worked examples in Python.