Customer Evidence Audit
Customer Evidence Audit helps the founder or programme team critically review quality, source, recency and decision relevance of evidence. Within Customer Discovery, Validation & Evidence, 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.
Critically test the quality, consistency and readiness of Customer Evidence Audit. The objective is to remove ambiguity around quality, source, recency and decision relevance of evidence, 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 Customer Evidence Audit 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.
problem hypothesis; target customer assumptions; existing interviews or evidence; current alternatives; evidence log; existing artefact to review; specific context for quality, source, recency and decision relevance of evidence.
The founder gathers all customer discovery logs, interview transcripts, survey results, and willingness-to-pay signals collected to date into a single structured repository. They map each item of raw data to the specific customer problem or value proposition claim it purports to support. This establishes a clean, auditable inventory of primary and secondary evidence prior to critical evaluation.
ObjectiveCompleting this action establishes a centralised, structured inventory of all customer evidence collected across the venture's lifecycle. It ensures the audit covers all relevant data points, preventing selective reporting and creating a solid foundation for objective validation.
What's expectedThe founder must produce a comprehensive inventory log linking every customer transcript, survey response, and landing page metric to specific venture claims. The compiled data room asset must detail original source files, date of collection, and sample sizes without omitting negative or ambiguous data.
Open action arrow_forwardConsultant stress-test · 5 questions- 1.How have you ensured that unsupportive interview transcripts or drop-off data were not excluded from this evidence pool?
- 2.What proportion of the assembled data represents direct observation of customer behaviour rather than self-reported opinion?
- 3.How clearly can an external reviewer trace a line from your core value proposition claim back to raw source transcripts?
- 4.Which specific customer segments are over-represented or under-represented in the data set you have collated?
- 5.Why did you choose this specific cut-off boundary for relevant customer interactions, and what legacy data was left out?
- A data-room asset titled Customer Evidence Audit
- A review note with a verdict, evidence gaps, risks, recommended corrections and a clear continue / repeat / escalate decision
- It should update the venture DNA with specific evidence or decisions about quality, source, recency and decision relevance of evidence, create a visible milestone in the founder journey, and generate one or more recommended next tasks
Bertie co-pilot turns interview notes and customer signals into tagged evidence, detects confirmation bias, scores evidence quality, and recommends validation or MVP tasks. For this task, it should focus on quality, source, recency and decision relevance of evidence, 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.