A cloud-first workflow that turns structured product data into KPI analysis, funnel diagnostics, segment insights, AI-assisted hypotheses, and automated decision-ready reports.
Product information can be spread across events, user attributes, feature usage, and historical metrics. Turning it into a repeatable product review can require manual analysis and interpretation.
Deterministic period comparison before Gemini interprets the data.
Six steps from signup to core action, with user counts, conversion rates, and step drop-off.
Activation by platform, acquisition channel, and plan.
Feature adoption and historical activation/retention provide additional product context.
Gemini is not the source of truth for the KPI calculations. MetricLens computes numerical evidence first, then uses Gemini to interpret that evidence.
Calculated KPIs, funnel movement, segments, feature adoption, and historical metrics.
Potential explanations are clearly separated from observed evidence.
The report translates product friction into a measurable experiment recommendation.
Primary and guardrail metrics keep recommendations measurable.
| Output | Purpose |
|---|---|
| Executive summary | Fast review of the main product signals |
| KPI + funnel tables | Inspect numerical evidence |
| Segment + feature analysis | Locate meaningful differences |
| AI analysis | Interpret evidence and frame hypotheses |
| Experiment + guardrails | Translate analysis into a testable next step |
The included portfolio dataset is synthetic. Pre/post comparisons are descriptive and do not establish causality. Gemini model availability is account/API dependent.
Google Apps Script · Google Sheets · Gemini API · Google Docs · Google Drive · Product Analytics · Funnel Analysis · Workflow Automation