Expansion
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Feature Discovery
Points subscribers locked in a single feature category toward a second and third, the breadth move that raises retention without nagging.
+6.6 percentage points of retention from one to three feature categories
feature-category usage data across hundreds of organizations
Consistent effect across multiple independent deployments.
How we grade evidence →Threshold trigger · Edition 1 · June 2026
What is it?
Subscribers who live in a single feature category are one workflow change away from leaving. To them, the product is exactly one tool, replaceable by any other tool that does that one thing. Moving from one feature category to three carries 6.6 percentage points of additional retention, because breadth turns a tool into a system.
This tactic finds subscribers locked in one category and points the way to a second, then a third, chosen from what similar subscribers adopted next and offered inside the product at moments adjacent to the work they are already doing.
When it fires
The threshold is sustained single-category usage: a subscriber active in the product but confined to one feature category past the learning period. The suggestion renders in-app, framed from the subscriber’s existing work, with the second category presented as a natural extension rather than a separate product to learn.
Pacing is the discipline. One category at a time, one suggestion per session, and silence after a dismissal. The corpus phrase is the operating instruction: point the way without nagging.
What the evidence shows
The breadth effect, 6.6 percentage points of retention from one category to three, holds across feature-category usage data from hundreds of organizations. It is one of the most actionable retention correlations in the dataset because the lever sits in the product’s hands: discovery costs nothing but placement.
The churn-reason data explains why the lever exists at all: 19 percent of churn cites a missing feature, and a meaningful share of those features already exist. The subscriber never found them. Discovery is partly a retention play and partly a correction for value the product already shipped but never put in front of anyone.
How it runs
In production, the tactic maps each org’s features into categories from usage data, tracks per-subscriber category breadth, and resolves the next-best category per subscriber from the adoption paths of retained subscribers with similar usage. Suggestions render in-app at contextually adjacent moments.
Guardrails keep discovery polite: hard frequency caps, an immediate stop at three categories (the measured effect plateaus there, and the suggestion budget is better spent elsewhere), and cooldowns after dismissal so the same suggestion never becomes wallpaper.
Run this for your business
Want to run Feature Discovery for your business? Connect the Churnkey MCP to your favorite AI agent. It reads your own usage and billing data and recommends the growth and retention plays most likely to move your LTV—starting with whether this one fits.
npm install -g @churnkey/mcpChurnkey's retention products run on the same dataset behind this tactic.
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Put the evidence to work.
The same dataset behind these tactics powers Churnkey's retention products. See what it finds in your subscription data.
