What the Product Qualified Lead model design prompt does

Act as a product-led growth and revenue-science lead designing an auditable PQL model.

The prompt will, at minimum:

  • Validate datasets, definitions, time windows, market scope and source-of-truth ownership before assessing product qualified lead model design
  • Examine ideal-customer fit, account and user identity, activation, depth and breadth of use, recency, collaboration, intent signals, buying roles, sales capacity, label quality, calibration, bias and feedback loops; 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 an interpretable PQL definition, scorecard, validation plan, routing thresholds and controlled rollout; 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

  • Confirmed context, assumptions and decision criteria
  • Current-state diagnosis and strategic options
  • Recommended target model with rationale
  • 30/60/90-day implementation roadmap
  • KPI, risk, dependency, decision and QA register

Variables

PlaceholderPurpose
{{account_master}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{activation_definition}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{buying_roles}}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
{{crm_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{icp_definition}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{lookback_window}}Date, time or period value; state ISO format, time zone, start/end boundary and comparison period
{{product_event_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{product_name}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{sales_capacity}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{sales_outcomes}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{success_metrics}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{target_market}}Target market
{{user_master}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance

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 Product Qualified Lead model design in ChatGPT

Open a new ChatGPT chat, paste the filled-in Product Qualified Lead model design 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: