Structure your readout as a decision-oriented narrative: lead with the headline and business impact, then walk through the methodology and assumptions, address data quality transparently, and close with clear, prioritized next steps. Keep the PM engaged by tying every finding to a product decision or action.
Pro tip: Frame the revenue loss as a range (best case, worst case) rather than a single number, and explicitly state what additional data would narrow the range—this shows rigor and invites collaboration rather than defensiveness.
Open with a one-sentence summary: the share of shops using pirated themes and the estimated monthly/cumulative revenue loss, emphasizing why it matters for Shopify. Use a simple, bold visual like a single KPI card or a bar chart showing the loss magnitude.
Briefly describe how you identified pirated themes and calculated revenue loss, then explicitly list the assumptions (e.g., conversion rates, average revenue per shop, attribution of loss). Use a simple diagram or bullet list to make assumptions transparent.
Proactively mention any data quality issues (e.g., missing data, false positives in piracy detection) and unusual patterns (e.g., spikes in certain regions or theme categories). Show how you validated or mitigated these, and what remains uncertain.
Propose 2-3 actionable next steps, prioritized by impact and effort, such as improving detection, testing a mitigation (e.g., warnings to shops), or running a deeper analysis. Tie each to a product decision or experiment.
End by stating what you need from the PM (e.g., prioritization, access to data) and invite questions or feedback to align on next actions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.