Customer behavior evidence and reporting workspace

Reduce the time from scattered behavior data to a reviewed, decision-ready report. For marketing and product teams acting on customer behavior data, convert permitted analytics events, feedback responses, social posts and spend records into an evidence-backed behavior report with source links and named-owner approval. The benefit is a testable hypothesis, measured through reviewed reports per analyst hour and decisions traced to evidence; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect permitted analytics events, feedback responses, social posts and spend records, then follow this sequence: 1. Ask questions in plain English and return answers about user behavior. 2. Organize households into groups and behavioral segments. 3. Identify top customers from segment and spend data. Resolve uncertain cases with qualified reviewers, approve the evidence-backed behavior report, and measure reviewed reports per analyst hour and decisions traced to evidence against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One agreed data schema and permissioned source set; final interpretation and campaign decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve data permissions, source attribution, consent records and usage rights. Named owners approve substantive interpretations and campaign scope. One agreed data schema and permissioned source set; final interpretation and campaign decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One agreed data schema and permissioned source set; final interpretation and campaign decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: ask questions in plain English and return answers about user behavior; organize households into groups and behavioral segments. Support the third module with operator review: identify top customers from segment and spend data. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Customer-owned analytics, feedback and social accounts and permitted spend data. Cloud data storage, analytics and feedback platform import/export and reporting destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Data sources and permissions, Analysis workspace, Report and review. Use a source list with connection states, a central canvas for segments, sessions and feedback, and a right-hand panel for evidence, comments and approval. Let users compare periods and segments side by side. Display draft, changes requested and approved states. Provide a shareable report link with comments anchored to the relevant chart or quote. Make the task-specific outcome evidence-backed behavior report visible beside its evidence, review state and value baseline.