Great customer engagement requires the right mix of art and science. There are days when you’re crafting creative campaigns that inspire action. Then there are times when you put on your analytical hat and crunch data.
The best marketers and product managers transition between the two in a fluid loop, rather than looking at them as discrete tasks. In order to speed up the feedback loop, last year we launched MoEngage Analytics. This brought insights into your users and the ability to act on them, to one place.
Now, we want you to take the analysis a step further and apply it to user segmentation.
User Segmentation, as we know it, is flawed.
For a process that’s critical for providing a relevant and personalized user experience, segmentation is still done manually. It’s typically based on hunches and guesswork and doesn’t take advantage of the vast amounts of user behavior data you collect.
This manual rule-based segmentation also runs the risk of adding some users to multiple overlapping segments, which can potentially lead to chaos as your user base grows. It might also leave out some of your users who might not fall into any of the segments – leading to lost opportunities.
The predictive segments module complements the existing segmentation method with behavioral data. It takes into account the Recency, Frequency, and Monetary value (also called RFM analysis) to create user segments.
Here’s RFM Analysis in a nutshell:
As marketers and product managers, you know aspects of your customers better than most algorithms. An RFM analysis is by no means a replacement for the qualitative insights that you’ve acquired over time.
You’ll still be able to define segments based on rules from the 'Segmentation' module. What we’re recommending is a hybrid approach where you analyze the broad user base with Predictive Segments. And when you need to go a lot deeper, you can create nuanced segments that are rich and highly targeted.
Basically, you get the best of both worlds.
Here are actionable resources we've curated for you!