I'm a data analyst. The Business unit I'm assigned for needs to segment users based on power vs non-power users so they can target each segment with proper treatments.
- Segment users (power vs non-power) based on their spending level (revenue generated to the company) on a small sample (20k users)
- Find the best treatment (marketing campaign, PN, etc) for each segment by experimentation
The focus of this thread is on the 1st goal (segmenting users), but it will be good to keep in mind the final goal of having these segments.
What I've done so far, in sequence
Pick 5 features for segmentations. These are the most important business metrics, i.e. transaction amount, transaction frequency, recency.
Clean the data, perform scaling (Min-Max Scaler, 0-1 range), and remove one highly correlated feature
Do Elbow analysis, pretty clear that best number of cluster is 3
Perform K Means clustering and assign a cluster for each user (PCA Viz is attached)
Check how "good" the segments are, i.e. checking the size of each segment and difference of transaction amount per segment
Define some thresholds based on the segment result above, so these segments can be applied to the population based on these thresholds, i.e. power user = users transacted more than 100$ and did >= 3 transactions in a month
The output of this segmentation is fixed thresholds, is this a best practice?
How do I know these segments will be useful to the business? i.e. they will improve revenue once we found the best treatment for them
Basically I'm not sure how to link the K Means clustering exercise to improve the business metrics.