Segments and metrics for outputs
Segments and metrics are the components that make up the outputs of our data analysis.
Segments: Overall and by major segment (product, geo, demo, etc)
Critical segments to figure out how to consistently separate:
- How does the customer behave in the funnel/business process?
- What pattern do they follow, according to the metrics we have identified as critical to the success of the business from our 18.104.22.168 Data Analysis - Acquitention Heuristic and discussions with the company?
- Easy segments are those with columns in the data.
- Product, geography, etc - but these will rarely be interesting or important other than as support knowledge.
- Pareto segments
- Show the 80/20, and the 96/4, and the 99/1. How do these segments differ from each other in terms of LTV, CAC, funnel, activity patterns, etc?
- RFM segments
- 22.214.171.124.20.10 Data Analysis - Segments - RFM
- And RFM by segment - i.e. do an RFM segmentation within other segments which seem important so that we can identify the champions and losers there
Metrics: Revenue and engagement (customer count, LTV, interactions, etc)
Metrics can be different for different kinds of companies. E.g. A non-subscription consumer company will care about AOV because basket size is important, but this won't change much for a subscription SaaS company so they will not care about it.
To get the total number of outputs, take the number of segments and multiply by number of metrics. E.g.
- Overall segment
- Product segments (5 products)
- Geo segments (10 geos)
- Gross retention
- Quick Ratio
- Net churn
- Revenue metric
- Customer count metric
- LTV metric
- AOV metric
= total of
16 segments * 7 metrics = 112 outputs, each with numeric grid and graph
Side note re. RFM segmentation
It would be a very fun and good experiment to include an RFM segmentation into the above outputs. It would add ~10 new segments so the number of outputs would be
26 segments * 7 metrics = 182 outputs.