Optimize · Pricing / Offer Tests

Test a Productized vs Custom Offer

Run a head-to-head test of a productized offer against your current custom scoped offer with margin and close-rate gates.

foundermanagerIntermediate⏱ 4-6 hours of offer design work
When to use
Use when discovery and scoping eat 8+ hours per pitch, when proposal turnaround is slowing deals, or when you have a service line repeatable enough to package. Avoid if your wins all come from highly bespoke six-figure deals.
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] (custom scoped) @ [CURRENT_PRICE] avg | Win rate: [WIN_RATE] | Avg deal: [AVG_DEAL_SIZE] | Test audience: [TEST_AUDIENCE] | Hypothesis: [HYPOTHESIS] | Avg scoping hours per pitch: [SCOPING_HOURS] | Repeatable service line: [REPEATABLE_SERVICE]
Design a head-to-head test between a productized version of [REPEATABLE_SERVICE] (fixed scope, fixed price, fixed timeline) and the existing custom-scoped offer, with explicit close-rate, ACV, and margin gates.

- Productized variant must specify: exact deliverables, timeline, price, what is excluded
- Compare time-to-close, ACV, and gross margin (custom often hides scope creep)
- Define which leads are eligible for the productized track
- Include a "graduation rule": when does a productized client get upsold to custom
- Kill if productized margin 
Sections: (1) Variant Comparison table (Custom vs Productized: scope/price/timeline/exclusions), (2) Eligibility Routing Rules, (3) Metrics + Decision Rules (close rate, ACV, time-to-close, gross margin), (4) Graduation Path, (5) Kill Criteria.
Variables
  • [AGENCY_NAME] — Your agency name
  • [SERVICES] — Core services
  • [CURRENT_OFFER] — Current custom offer description
  • [CURRENT_PRICE] — Avg current price
  • [WIN_RATE] — Current close rate %
  • [AVG_DEAL_SIZE] — Current ACV
  • [TEST_AUDIENCE] — Segment to test against
  • [HYPOTHESIS] — What you expect to happen
  • [SCOPING_HOURS] — Hours spent scoping each custom pitch
  • [REPEATABLE_SERVICE] — The service repeatable enough to productize
Example input
Agency: Vertex SEO — technical SEO for ecom | Current offer: custom technical audits @ $8-18k | Win rate: 24% | Avg deal: $12k | Test audience: Shopify Plus brands, $5-50M GMV | Hypothesis: a $6,900 productized audit closes 2x faster at acceptable margin | Scoping hours: 6 per pitch | Repeatable service: technical SEO audit
Example output
1) Variants:
| Field | Custom (Control) | Productized |
|---|---|---|
| Scope | Bespoke per crawl | 14-day audit, 7 fixed sections |
| Price | $8-18k (avg $12k) | $6,900 flat |
| Timeline | 3-6 weeks | 14 days |
| Excluded | n/a | implementation, ongoing mgmt |

2) Routing: Shopify Plus, $5-50M GMV, no prior audit in 12mo → Productized. Enterprise, multi-stack, RFP → Custom.

3) Metrics:
| Metric | Custom | Productized Decision |
|---|---|---|
| Close rate | 24% | adopt if >=35% |
| ACV/pitch | $2,880 | adopt if >=$2,400 (volume offsets) |
| Time-to-close | 21 days | adopt if 10%.
Pro tips
  • Productize the service where your scoping conversation is the same 80% of the time — that's where the leverage is
  • Always design the graduation path before launch — productized offers should feed your custom pipeline, not cannibalize it
  • Track delivery hours per productized engagement weekly — margin erosion shows up there first
Works with
ClaudeChatGPTGemini
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