What this prompt does
This prompt turns ChatGPT into a SaaS pricing analyst that works through a seven-step framework rather than handing back vague advice. You feed it your real numbers — [product_name], [product_description], [target_market], your [current_pricing], [current_mrr], and [customer_count] — and it grounds every recommendation in that context instead of generic playbook talk. The structure forces the model to first audit competitors, then reason about your value metric, then design tiers, before it ever suggests a price change.
The variables shape the depth of each step. [competitor_count] controls how many rivals get a tier-by-tier teardown, [tier_count] sets how many pricing tiers it designs, and [churn_assumption] feeds the elasticity model so the revenue projections reflect your actual retention sensitivity. [conversion_goal] and [enterprise_threshold] steer the free-trial and enterprise sections. Because the steps build on each other, the value-metric choice in step 2 propagates into the tier design and elasticity math, which is exactly how a real pricing exercise should flow.
When to use it
- You are about to repackage or reprice an existing SaaS product and want a structured second opinion before touching billing code.
- You suspect your value metric (per seat vs. usage) is capping growth and want the trade-offs modeled explicitly.
- You are designing a new tier structure and need psychological anchoring logic for the middle plan.
- You want a quick elasticity sensitivity table across several price points without building a spreadsheet from scratch.
- You are redesigning a pricing page and need a layout that reduces decision fatigue.
- You are preparing a pricing recommendation for founders or a board and need the reasoning laid out step by step.
Example output
Expect a long, sectioned report rather than a single answer. You get a competitor comparison block (one mini-profile per rival covering tiers, feature gating, value metric, free-tier limits, and enterprise approach), a recommended value metric with justification, a tier table mapping features to ideal-customer profiles, an elasticity scenario table spanning -20% to +30%, a trial strategy with upgrade triggers, an enterprise discount framework, and a described pricing-page layout. It reads like a consultant's deliverable you can act on section by section.
Pro tips
- Be specific with
[current_pricing]— list every tier and price, because the model anchors all tier redesign on what you already charge. - Set
[churn_assumption]to your real monthly churn; a wrong baseline makes the elasticity table misleading rather than useful. - If you only sell one value metric today, still let step 2 run — the per-seat vs. usage comparison is often where the biggest insight lands.
- Tune
[competitor_count]down to 3 for a fast pass or up to 7 when you want a thorough market map; more competitors means a longer but slower response. - Treat the elasticity numbers as directional, not forecasts — they assume a single churn sensitivity and ignore segment mix, so validate against your own cohort data.
- Run it twice with different
[tier_count]values to compare a simpler 3-tier model against a 4-tier-plus-enterprise structure.
