Campaigns — Deals Analysis (Influence)
Petavue, please review all deals closed-won in the last 12 months and map every campaign interaction on the associated contacts. Then:
1. Calculate Revenue Credit
- Last-Touch Model: 100% credit to the final campaign interaction.
- Position-Based Model: 40% credit to the first interaction, 40% to the last, and split the remaining 20% equally across any middle touches.
2. Aggregate Results
- Sum revenue attributed to each campaign under both models.
3. Comparison Table
- Columns:
- Campaign
- Revenue (Last-Touch)
- Revenue (Position-Based)
- Difference (%) between the two
4. Flag for Review
- Highlight any campaign where the revenue difference is ±25% or more.
- List campaigns where position‑based revenue exceeds last‑touch by ≥25 percent, showing both model values and variance.
- List campaigns where last‑touch revenue exceeds position‑based by ≥25 percent, with corresponding revenue figures.
- For each flagged campaign, show a sample of three deals with their full touch sequences for manual review.
- Recommend attribution policy adjustments for campaigns with the largest model discrepancies.
What This Prompt Does
This prompt retrieves every deal that closed‑won in the past 12 months and lists all campaign touches on its related contacts. It then applies two influence models—single‑touch (last touch receives full credit) and position‑based (40 percent to first touch, 40 percent to last touch, 20 percent evenly across middle touches)—and aggregates revenue by campaign under each model. The result is a side‑by‑side comparison table showing each campaign’s revenue under both models, the percentage delta between them, and flags any campaigns where the attribution difference exceeds 25 percent.
Strategic Impact
Uncovering attribution discrepancies ensures your marketing credit aligns with actual influence and highlights policies that may need adjustment.
Business outcomes:
→ Improves budget allocation by understanding true campaign impact under different models
→ Enhances attribution policy fairness by flagging large revenue shifts for review
→ Increases confidence in performance reporting through transparent model comparisons