Product-Market Fit Diagnostic
Product-Market Fit Diagnostic helps the founder or programme team assess usage, retention, revenue, repeatability and strongest customer segment. Within GTM, Sales, Product-Market Fit & Revenue, it turns a broad or uncertain area of the venture into a concrete Bertie work product that can be reviewed, improved and reused. The task is intentionally discrete: it should produce a specific artefact, decision, evidence item or risk signal rather than general learning notes.
Establish a clear baseline and decision score for Product-Market Fit Diagnostic. The objective is to remove ambiguity around usage, retention, revenue, repeatability and strongest customer segment, give the founder a decision-ready output, and make it clear whether the venture should progress, repeat the task with stronger evidence, escalate to expert support, or move into a linked stage.
Bertie or a programme manager assigns Product-Market Fit Diagnostic when the venture needs a decision-ready output for this group. Typical triggers include group-gate reviews, evidence gaps identified by the co-pilot or founder request.
ICP hypothesis; product/MVP status; customer evidence; pricing assumptions; early traction or pipeline data; specific context for usage, retention, revenue, repeatability and strongest customer segment.
The founder aggregates all historical data covering product analytics, customer contracts, billing logs, and user activity records. They centralise metrics across usage frequency, active accounts, MRR or ARR, churn rates, and customer acquisition channels into a structured repository.
ObjectiveCompleting this action establishes a single source of truth for the venture's historical performance metrics. It ensures the diagnostic relies on verified operational data rather than fragmented memory or anecdotal observations.
What's expectedThe founder must present a clean data repository containing raw telemetry, financial transaction records, and cohort activity tables. Every metric must trace back to verifiable data sources such as Stripe logs, CRM exports, or analytics dashboards.
Open action arrow_forwardConsultant stress-test · 5 questions- 1.What percentage of your logged users have generated zero activity over the last 30 days?
- 2.How have you verified that your revenue data distinguishes between non-recurring setup fees and true repeatable subscriptions?
- 3.Where are the critical tracking gaps in your product analytics that might be disguising user drop-off?
- 4.How consistent is your data collection period across different customer cohorts?
- 5.Which data points relied on manual self-reporting by customers rather than automated system logs?
- A data-room asset titled Product-Market Fit Diagnostic
- A diagnostic score, interpretation of the strongest and weakest signals, and a short list of priority interventions
- It should update the venture DNA with specific evidence or decisions about usage, retention, revenue, repeatability and strongest customer segment, create a visible milestone in the founder journey, and generate one or more recommended next tasks
Bertie co-pilot analyses pipeline, calls, usage and retention evidence, improves messaging and experiments, and recommends revenue or PMF interventions. For this task, it should focus on usage, retention, revenue, repeatability and strongest customer segment, prompt the founder for missing inputs, draft or improve the output, flag weak assumptions, and record the result back into the relevant data-room section.
A mentor or evaluator can review the output at the group gate. Programme managers can require an advisor checkpoint before Bertie moves the venture forward.