Optimize · Pricing / Offer Tests

Generate Three Pricing Test Variants for a Retainer

Generate three testable price variants for an existing agency retainer with hypothesis and decision rules.

foundermanagerIntermediate⏱ 2-3 hours of pricing committee work
When to use
Use when your current retainer price is stale, close rate or LTV has shifted, or you suspect you are leaving margin on the table. Best run before a quarter starts so you have a clean cohort of opportunities to test against. Skip if you have fewer than 8 new pitches per month — sample size will be too small.
The prompt
You are an agency monetization strategist who runs pricing tests responsibly (no race to the bottom). You design experiments with explicit hypotheses, clear primary metrics, and pre-committed kill criteria so the agency learns regardless of outcome.
Agency: [AGENCY_NAME] — [SERVICES] | Current offer: [CURRENT_OFFER] @ [CURRENT_PRICE] | Win rate: [WIN_RATE] | Avg deal: [AVG_DEAL_SIZE] | Test audience: [TEST_AUDIENCE] | Hypothesis: [HYPOTHESIS] | Cost to deliver: [DELIVERY_COST] | Test window: [TEST_WINDOW]
Generate three testable price variants for the current retainer (one lower, one same-price-different-structure, one higher) so we can run a structured pricing test against [TEST_AUDIENCE] over [TEST_WINDOW].

- Each variant must include: name, structure, price, hypothesis, primary metric, sample size needed, kill criteria
- No variant priced below cost-to-deliver + 40% gross margin
- Hypothesis must be falsifiable (specify expected lift and direction)
- Primary metric must be one of: close rate, ACV, LTV, time-to-close
- Include a pre-committed decision rule for each variant

Markdown table with columns: Variant | Offer Structure | Price | Hypothesis | Primary Metric | Sample Size | Decision Rule. Then a 3-bullet "how to run this test" section.
Variables
  • [AGENCY_NAME] — Your agency name
  • [SERVICES] — Core services (e.g. SEO + paid social)
  • [CURRENT_OFFER] — Existing retainer description
  • [CURRENT_PRICE] — Current monthly price
  • [WIN_RATE] — Current close rate as %
  • [AVG_DEAL_SIZE] — Current ACV
  • [TEST_AUDIENCE] — Segment to test against (e.g. inbound demos, ecom DTC)
  • [HYPOTHESIS] — What you believe will happen
  • [DELIVERY_COST] — Monthly cost to deliver
  • [TEST_WINDOW] — Test duration (e.g. 60 days)
Example input
Agency: Northwind Digital — SEO + content for B2B SaaS | Current offer: 12-keyword SEO retainer @ $4,500/mo | Win rate: 22% | Avg deal: $54k LTV | Test audience: inbound demos from $5-30M ARR SaaS | Hypothesis: we can raise price 20% without close rate dropping more than 4pts | Cost to deliver: $1,800/mo | Test window: Q3
Example output
| Variant | Offer Structure | Price | Hypothesis | Primary Metric | Sample Size | Decision Rule |
|---|---|---|---|---|---|---|
| A — Control | 12-keyword retainer | $4,500/mo | Baseline | Close rate | 30 pitches | Keep if close >=22% |
| B — Anchored Up | Same scope, repositioned as "Pipeline SEO" | $5,400/mo | +20% price holds close within 4pts | Close rate + ACV | 30 pitches | Adopt if ACV/pitch >=$990 (Control: $990) |
| C — Outcome Tier | Base $3,900 + $1,200/qualified ranking | $3,900 + perf | Performance hook lifts close 6pts on price-sensitive demos | Close rate | 30 pitches | Kill if blended margin <55% |

How to run:
- Randomize by demo day-of-week to avoid rep bias
- Lock pitch deck and discovery script across variants
- Review at 30 pitches per variant; full kill/scale call at 90
Pro tips
  • Pre-commit your decision rules in writing before the test starts — it's the only protection against retrofitting the story
  • Hold the discovery script constant; only the price/structure should vary
  • Track ACV per pitch (not just close rate) — a 5pt close drop at +20% price is still a win
Works with
ClaudeChatGPTGemini
Done with prompts? Time to install the system
Book a STAOS call
Related prompts