Business Intelligence
Analytics designed around business decisions, not vanity traffic.
I built a reporting layer that connects website behavior to customer inquiries, opportunity quality, commercial demand, and operational activity.
The Reporting Problem
Traffic is not the same as business demand.
Common web analytics can answer how many visitors arrived, which pages were viewed, and broad acquisition channels. But the business needed to understand which visits became inquiries, which inquiries showed high intent, which product or service categories generated demand, how customer behavior connected to lead quality, how operational activity changed over time, and which reporting periods were trustworthy.
Event and Session Intelligence
First-party event collection built around business questions.
Session & Page Context
First-party session identifiers, page views, and entry pages captured without third-party trackers.
Engagement Signals
Scroll depth, time on page, and time on site collected through an allowlisted set of tracked events.
Session Path
The ordered sequence of pages a visitor moved through before an inquiry or exit.
Acquisition Context
Referrer, source, device, and browser context attached to the session.
Inquiry Linkage
Events connected to the lead or opportunity they eventually produced, when a session converts.
Event Allowlisting
Only a defined set of business-relevant events is collected, rather than logging every possible interaction.
Business Event Taxonomy
Events are meaningful only when mapped to business concepts.
Raw event names are not useful on their own. Each tracked event maps to a business category so the reporting layer can answer questions in commercial terms rather than technical ones.
Lead Intelligence
Scoring supports staff judgment, it does not replace it.
Behavioral and declared-intent signals contribute to lead scores, attention bands, recommended actions, category detection, and opportunity prioritization. The scoring system gives staff a starting point for triage, not a final decision.
Aggregation Layer
The engineering problems behind every chart.
Turning raw events into a dashboard number requires solving a consistent set of problems on every query:
Forward-Only Reporting
Reporting correctness required an explicit boundary.
The system intentionally supports forward-only analytics from a defined reporting start point rather than mixing incompatible historical data into current reporting.
- Earlier data may have been collected under different rules.
- Changing event definitions can make historical comparisons misleading.
- Newly introduced fields may not exist historically.
- Production resets should be explicit and explainable.
- Dashboards should not imply precision that the underlying data cannot support.
Production Data Decisions
The hardest analytics decisions were not visual. They were about what the numbers were allowed to mean.
Production Proof
What the reporting layer can answer.
The analytics system connects website behavior to customer inquiries, commercial categories, and operational activity. It can help answer: What type of demand is arriving? Which pages produce inquiries? Which sessions indicate high intent? What commercial categories are growing? How is website activity becoming business activity?
Analytics Overview
Demand and Activity
Intent and Inquiries
Engineering Ownership
Built, deployed, and maintained end to end.
Related Systems
Part of the connected commercial platform.
Concierge Commerce Platform
See how analytics fits into the full platform: customer acquisition, lead intelligence, client records, quoting, and staff workflows.
View the flagship case study →
Email Intelligence System
See how session behavior is captured and packaged into a Customer Intent Report at the point of form submission.
Read the technical deep dive →
The analytics system does not only report what happened on the website. It translates customer behavior into operational information the business can act on.
View all systems →