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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
HubSpot’s conversion reporting doesn’t properly handle deals that revisit previous stages because it’s based on first-time stage transitions rather than ultimate outcomes. This creates artificially low conversion rates that don’t reflect true sales performance when deals naturally move back and forth.
Here’s how to build conversion reports that accurately reflect deals with complex stage progression patterns.
Build outcome-based conversion reporting using Coefficient
Coefficient enables accurate conversion reporting by importing comprehensive deal data from HubSpot and building custom logic that calculates conversions based on final deal status rather than linear progression. This approach accounts for the natural back-and-forth movement in complex sales processes.
How to make it work
Step 1. Import comprehensive deal data with stage history.
Pull HubSpot deals including Deal Stage History, Current Stage, Close Date, and Deal Amount. Use up to 25 filters to focus on specific time periods or deal characteristics for your conversion analysis.
Step 2. Create outcome-based conversion logic.
Build formulas that calculate conversions based on final deal status rather than linear progression. For Stage 2 conversion rate, use: =COUNTIFS(FinalStatus, “Closed Won”, StageHistory, “*Stage_2*”) / COUNTIFS(StageHistory, “*Stage_2*”). This counts all deals that ever visited Stage 2 and eventually closed won, regardless of revisits.
Step 3. Track stage revisit patterns for process insights.
Identify which stages deals commonly revisit using formulas like =LEN(StageHistory) – LEN(SUBSTITUTE(StageHistory, “Stage_2”, “”)) to count multiple visits to the same stage. This reveals process inefficiencies that impact conversion timing.
Step 4. Calculate time-adjusted metrics for realistic cycle duration.
Measure total time from first stage entry to final conversion, accounting for all revisits. This provides realistic sales cycle duration that includes the full customer journey, not just forward progression.
Step 5. Build dynamic cohort analysis for trend identification.
Group deals by entry date and track how revisit patterns affect ultimate conversion rates over time. This reveals trends in sales process effectiveness and helps identify when process changes impact deal progression.
Step 6. Set up automated reporting with alerts.
Schedule imports and email alerts when conversion metrics change significantly, ensuring your team stays informed about true sales performance without manual monitoring.
Track true conversion performance across complex deal journeys
This approach delivers conversion reports that accurately reflect real sales processes where deals naturally move back and forth before converting. Get started with outcome-based conversion tracking that shows true sales performance.
