Plan performance analysis

Plan performance analysis measures the health and contribution of each subscription tier in your pricing structure. Rather than looking at aggregate revenue or churn, it breaks these figures down by plan so you can see which tiers attract customers, which retain them, and which generate the most revenue per subscriber.

This analysis is essential because pricing and packaging decisions are among the highest-leverage choices a subscription business makes. A plan that attracts many sign-ups but churns heavily may have a positioning problem: it promises something the product does not deliver at that tier. A plan with excellent retention but low adoption may be priced too high or insufficiently differentiated from the tier below.

Plan analysis also reveals cannibalisation. If a newly launched mid-tier plan is growing but the premium tier is shrinking by the same amount, the business may be trading down rather than expanding the market. Tracking plan upgrade rate alongside plan-level churn surfaces these dynamics.

There is no single formula. Instead, compare each plan across a consistent set of metrics:

Display these side by side in a matrix so you can immediately spot the outliers: the high-churn plan, the low-ARPU plan that dominates subscriber count, or the premium tier with excellent retention but minimal adoption.

1. 1

   Create clear value steps between tiers

   Each plan upgrade should unlock a feature or limit increase that customers genuinely need at the next stage of growth. If the difference between tiers is unclear, customers default to the cheapest option and never upgrade.

2. 2

   Consolidate or retire underperforming plans

   A long tail of legacy plans adds billing complexity and fragments the subscriber base. Migrate subscribers from discontinued plans to current tiers, offering incentives where necessary.

3. 3

   Test pricing changes on new sign-ups first

   Before repricing an existing plan, launch the new price to new subscribers only and compare conversion and retention metrics against the cohort on the old price. This reduces risk and provides clean data.