churn-conversion-memo
Skill0 starsChanged 6 months agoNo licenceView on GitHub
Produce a board memo dissecting customer churn, signup-to-paid conversion, AND upsell barriers into named, fixable mechanisms with per-mechanism monthly ARR impact — given a sample of customer support interactions + accounts data + per-plan-tier LTV. Each mechanism names a specific file, screen, error string, policy clause, or feature gap (no abstract category labels). Output is a three-bucket decision table (Conversion / Churn / Upsell) plus aggregate impact claims (conversion-rate uplift, churn-rate drop, upsell-driven MRR uplift) plus a 3-bullet "what to ship in the next sprint" recommendation. The methodology was iterated through 30+ turns of trial and error on Sonetel's customer-service data; this skill captures what worked and what catastrophically failed. Read METHODOLOGY.md before producing any output.
In this repository18 other files it gives its agents.
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The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
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