# Cohort Vs. Non-Cohort Reporting

The Integrous Analytics system enables two complementary reporting approaches, each designed for different business questions:

### Cohort Reporting (using the Engagement object)

* **Best for:** Measuring marketing campaign effectiveness and conversion rates
* **Shows:** All Activations (new sales journeys) and the subset of Opportunities they generate
* **Business use case:** "Which campaigns generate the most qualified leads that convert to opportunities?"
* **What's included:** Source Attribution, sales progression (Stages 1-6), and outcomes for each distinct Sales Journey
* **Limitation:** Doesn't include all Opportunities in the system (only those connected to Activations), similar to the "Leads with Converted Lead Information" stock report

### Non-Cohort Reporting (using the Opportunity object)

* **Best for:** Revenue analysis and ensuring all pipeline is accounted for
* **Shows:** All Opportunities with source attribution and qualification data from connected Activations
* **Business use case:** "What's the source breakdown of all revenue in our pipeline?"
* **What's included:** All Opportunities with attribution data (Stages 1-6)
* **Limitation:** Doesn't include all new sales processes generated (only those connected to Opportunities)

{% hint style="info" %}
**🛠️ Technical Detail:** This approach is possible because the Integrous Analytics system stores sales progression data across both objects with bidirectional relationships:

* Stages 1-3 fields are stored on the Engagement object and are layered onto opportunities via the Opportunity "Attribution Activation" relationship
* Stages 4-6 fields are stored on the Opportunity object and are layered on Activation Engagements via the Engagement "Primary Opportunity" relationship
  {% endhint %}

### Why both approaches work together

The dual architecture ensures you can answer both "How effective are our campaigns at generating sales processes?" (Cohort) and "Where did all our revenue come from?" (Non-Cohort) without sacrificing accuracy in either analysis.


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