What the ICP- and channel-level CAC payback analysis prompt does

Act as a SaaS unit-economics, acquisition-finance and attribution analyst.

The prompt will, at minimum:

  • Validate datasets, definitions, time windows, market scope and source-of-truth ownership before assessing icp- and channel-level cac payback analysis
  • Examine ICP definition, channel taxonomy, spend, labour and tooling allocation, lead and account identity, sales-cycle timing, new recurring revenue, gross margin, ramp, churn, expansion, cohort maturity, attribution limits, blended versus marginal economics and currency; preserve original identifiers and show the derivation of every finding
  • Segment only when evidence supports the split. Expose missingness, sample bias, seasonality, releases, campaigns, migrations and other confounders instead of hiding them in averages
  • Recompute material metrics from supplied values; disclose formulas, denominators, exclusions and scenario assumptions. Never invent benchmarks, market sizes or competitor performance
  • Turn evidence into reconciled CAC and payback views by ICP/channel, confidence bands and investment guardrails; assign owner, priority, dependency, expected signal, verification method and human-approval point to each action

Who it is for

Gökhan Güzel's SaaS prompt for ChatGPT users: marketers, founders, agencies and consultants who need an auditable, evidence-based deliverable instead of generic advice.

What you get

  • Executive summary and data-quality report
  • ICP- and channel-level CAC payback analysis methodology and evidence ledger
  • Segmented findings, calculations and scoring
  • Prioritised action backlog with owners and validation criteria
  • Sources, limitations, confidence and QA report

Variables

PlaceholderPurpose
{{analysis_period}}Date, time or period value; state ISO format, time zone, start/end boundary and comparison period
{{attribution_rules}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{channel_taxonomy}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{company_name}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{currency_rules}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{gross_margin_inputs}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{icp_definition}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{lead_account_map}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{marketing_spend}}Monetary spend or price input used for pricing, CAC, payback, profitability or scenario calculations
{{new_customer_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{sales_costs}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{subscription_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{success_metrics}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{tooling_costs}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source

How to use

Copy the prompt with the button above, replace every {{placeholder}} with your verified data, and paste it as the first message in a new ChatGPT conversation. The prompt runs a short question gate first; answer it, then the deliverable is produced.

Run ICP- and channel-level CAC payback analysis in ChatGPT

Open a new ChatGPT chat, paste the filled-in ICP- and channel-level CAC payback analysis prompt and answer the short question gate. ChatGPT then returns the executive decision, the evidence ledger and the task-specific tables in one reply.

Source

Original file in the multilingual prompt library on GitHub: