Data & Decision Intelligence
Product Growth Analytics
See where users arrive, where they drop, and which change deserves validation.
Define critical behavior and identity, then establish funnels, cohort analysis, feedback, experiment records, and anomaly alerts for inspectable product decisions.
Discuss the scopeDefinitions, lineage, and decision records
Composed around your scope
Reference operating scenario
Typical objects, ownership, and next actions explain how this system works; this is not a live product.
Current decision view
Cohort and funnel analysis
| Business object | State | Source | Next action |
|---|---|---|---|
| Event, identity, and metric design | Review | Business system | Identify the moments that affect conversion and retention |
| Funnel, retention, path, and cohort analysis | Waiting | Business system | Establish continuous experimentation and learning |
| Feedback, experiments, and action review | Waiting | Business system | Identify the moments that affect conversion and retention |
Fields, access, and integrations are confirmed per project
What can change?
Events, funnels, retention, feedback, and experiments form one product learning loop.
- Identify the moments that affect conversion and retention
- Establish continuous experimentation and learning
Reusable modules
Product and privacy analytics
Funnels, paths, conversion, and retention observations
Decision dashboards
Layered metrics, anomalies, explanations, and action queues
Market research
Source snapshots, research briefs, and opportunity judgments
Forms and document flows
Governed records, document packages, and completion evidence
Evidence ledger
Citable evidence, conflicts, and approval state
Does this sound familiar?
01
The company has no shared management dashboard, so key numbers must be collected from departments on demand.
02
Teams use different definitions for the same metric, so meetings are spent reconciling numbers.
03
Reports show changes but do not lead into root-cause investigation, ownership, or improvement actions.
What we can build
01
Event, identity, and metric design
02
Funnel, retention, path, and cohort analysis
03
Feedback, experiments, and action review
Evidence and boundaries
Client Case
Content2Page
A governed pipeline that turns structured content and images into validated, previewed, reviewed, queued, and published SEO landing pages.
The primary workflow is implemented locally; production cutover still depends on CMS, storage, and notification credentials.
Concept Preview
SocialPulse
A multi-platform social collection and AI analysis experiment for local intelligence and desktop delivery.
Platform collection stability, authorization boundaries, data normalization, and production monitoring are incomplete.
How it works
01
Behavior observation
Capture critical events, sources, and user context.
02
Journey & drop-off
Compare cohorts, paths, funnels, and retention.
03
Feedback evidence
Bind surveys and interviews to actual behavior.
04
Experiment & learn
Record hypotheses, changes, outcomes, and next action.
Example system
Product Growth CenterThe reference build makes business objects, states, and workflow visible; it is not presented as client work.
Growth loop
Concept Preview
Active
Capture critical events, sources, and user context.
Teams know total traffic but not why users convert or drop.
Customizable to business boundaries
Start from the current problem, without choosing technology first.
Share the current state, roles, available data, and first required outcome; we will define the reuse, integration, and custom boundary.