--- title: "Right-Plan Recommendations — Pricing Growth Tactic" description: "Reads each subscriber’s usage against vertical benchmarks and recommends the closest-fit plan at signup, upgrade, and save." canonical: "https://churnkey.co/growth/library/right-plan-recommendations" category: "growth" related: - /growth/library/index.html.md - /growth/library/methodology.html.md - /tools/churn-rate-calculator.html.md last_updated: "2026-06-09" --- # Right-Plan Recommendations Reads each subscriber’s usage against vertical benchmarks and recommends the closest-fit plan at signup, upgrade, and save. - **Stage:** Pricing - **Evidence grade:** validated (replicated across many companies) - **Evidence:** 88 percent churn for subscribers who land in the $50–$100 price dead zone - **Trigger type:** Event - **Channels:** in-app, cancel modal ## What is it? A meaningful share of churn has nothing to do with the product and everything to do with the plan. The pricing analysis behind this tactic found a dead zone, the $50–$100 price band, where 88 percent of subscribers eventually churn, measured across 628,000 subscribers. Roughly 82 percent of company pricing also sits suboptimally against peers with similar growth profiles. This tactic keeps each subscriber off the wrong plan. It reads the usage profile (per feature, per event type, per activity level), crosses it with the vertical benchmark and the plan catalog, and recommends the closest-fit tier wherever a plan decision is being made. The recommendation runs in three places. On the pricing page, the closest-fit tier renders first with the standard ladder as fallback. At the in-product upgrade moment, it shapes the proposed tier. And in the save moment, a right-sized smaller tier becomes the downgrade offer. The same logic serves all three: match the plan to observed usage, not to whatever the default presentation happens to push. ## When it fires The trigger is the decision point itself, not a schedule. A pricing-page visit, an upgrade moment, or a cancel-flow entry each asks the tactic for the closest-fit tier, and the tactic answers from the subscriber’s current usage profile and the company’s benchmark. No decision point, no recommendation. The save moment matters most. Downward plan migrations run at an 83 percent churn rate when no downgrade path exists. So when usage has drifted well below the current plan, the tactic puts the right-sized tier on the table at the moment the subscriber is deciding whether to leave entirely. ## What the evidence shows The dead zone is the headline: subscribers landing in the $50–$100 price band churn at 88 percent, measured across 628,000 subscribers. Plan structure, not product quality, does the damage in that band. And roughly 82 percent of company pricing sits suboptimally against similar-vertical peers, which means most subscribers choose from a ladder that was never tuned to them. The downgrade evidence completes the picture: mid-tier-to-micro-tier migrations carry an 83 percent baseline churn rate when no downgrade path exists, and a right-sized offer in the save moment is the path. These are large, consistent structural patterns validated across the platform’s pricing data. They are strong enough to act on, though they come from pricing-structure analysis rather than a controlled test. ## How it runs In production, the tactic maintains a per-subscriber profile from usage data, crosses it with the plan catalog and the vertical benchmark, and resolves a closest-fit tier on demand at each decision point. On the pricing page the recommended tier renders first. In the Cancel Flow it renders as the downgrade offer. Acceptance applies the plan switch in the billing provider as a single transaction. Guardrails keep the match honest: no recommendation from a thin usage profile, one recommendation per decision point per session, and the closest-fit tier gets recommended even when it is cheaper than the current plan. The match is to observed usage, never to revenue. ## Run it with the Churnkey MCP This tactic runs via the Churnkey MCP. Connect it to your favorite AI agent and it reads your own usage and billing data, then recommends and runs the plays most likely to move your LTV — starting with whether this one fits. **Trigger:** A subscriber reaches a plan decision point (a pricing-page visit, an in-product upgrade moment, or a cancel-flow entry) and the closest-fit tier computed from their usage profile differs from the plan the default presentation would surface. **Surface:** In-app on the pricing page (closest-fit tier rendered first, standard ladder as fallback) and at the upgrade moment. In the Cancel Flow, as the right-sized downgrade offer. **Guardrails:** - Usage-data floor: never recommend from a thin usage profile. Require a minimum observation window and event coverage before trusting the closest-fit computation. - Rate limit: one plan recommendation per surface per session. Never stack a plan-match recommendation on top of another active offer. - Eligibility: live signup session or active subscription with no open payment failure. In the Cancel Flow, the downgrade offer follows the flow’s own pacing. - Recommendation integrity: recommend the closest-fit tier even when it is cheaper than the current plan. The match is to observed usage, never to revenue. **Evidence:** The $50–$100 price dead zone carries 88 percent churn across 628,000 subscribers. Roughly 82 percent of company pricing sits suboptimally against similar-vertical peers. Downward plan migrations run at an 83 percent baseline churn rate when no downgrade path exists. Validated at scale across the platform’s pricing analysis. **How to run it:** 1. Build a per-subscriber usage profile (per feature, per event type, per activity level) and keep it current. 2. Load the plan catalog (tiers, limits, prices) and the vertical benchmark for typical plan distribution among similar companies. 3. Compute the closest-fit tier per subscriber by crossing the usage profile with the catalog and benchmark. Recompute when usage shifts materially. 4. At each decision point (pricing page, upgrade moment, Cancel Flow), render the closest-fit tier first, with the standard ladder as fallback. 5. On acceptance, apply the plan switch in the billing provider as a single transaction. In the save moment, treat an accepted downgrade as a retained subscriber. 6. Track plan-match acceptance, 90-day retention of matched subscribers, and dead-zone concentration over time. Tune the fit computation against those outcomes. Install the MCP server: ```bash npm install -g @churnkey/mcp ``` The full reference is in the [Churnkey MCP docs](https://docs.churnkey.co/data-integrations/mcp). ## Related - [Growth Tactics Library](/growth/library/index.html.md) — The full register of graded growth plays. - [How Tactics Are Graded](/growth/library/methodology.html.md) — The four-grade evidence ladder behind this grade. - [Churn rate calculator](/tools/churn-rate-calculator.html.md) — Related calculator.