Prompt library
Marketing

First vs Last Touch Attribution

Compare revenue credit under First‑Touch and Last‑Touch models for channels and top campaigns.
Table of contents
Prompt
Follow-up Prompts
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Heading 3
applicable Roles
Demand Gen
Analytics
Prompt
Copy

Petavue, please analyze all Closed-Won deals from the last 180 days and compare revenue under two simple models:

  1. First-Touch: Assign 100% of revenue to the first interaction
  2. Last-Touch: Assign 100% of revenue to the final interaction

Then:

  • Summarize total revenue by Channel and by the top 20 Campaigns for each model
  • Produce a variance table showing First-Touch $, Last-Touch $, and the % difference
  • Flag any rows where variance exceeds ±30%
  • Recommend which model better matches buyer behavior, based on median journey length and number of touches
Follow-up Prompts
Copy
  • List all channels where the variance between First‑Touch and Last‑Touch revenue exceeds ±30 percent, showing both model values and variance.
  • List the top 10 campaigns by absolute variance, including First‑Touch $, Last‑Touch $ and % variance.
  • Summarize median journey length and average touch count for each flagged channel or campaign.
  • Highlight channels and campaigns where First‑Touch outperforms Last‑Touch by ≥30 percent, with supporting buyer‑journey statistics.
Action Prompt

What This Prompt Does

This prompt attributes 100 percent of Closed‑Won revenue from the past 180 days to both a First‑Touch model and a Last‑Touch model, then aggregates results by channel and the top 20 campaigns. It generates a variance report showing First‑Touch dollars, Last‑Touch dollars and percentage variance for each row. Any channel or campaign with variance exceeding ±30 percent is flagged and accompanied by commentary suggesting which model better represents the buyer journey, based on median journey length and touch count.

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Strategic Impact

Delivering a clear comparison of attribution models ensures your revenue crediting matches actual customer paths and strengthens performance decisions.

Business outcomes:

 → Guides budget allocation toward channels and campaigns under the most appropriate attribution model

 → Reduces reporting disputes by flagging large model‑driven revenue shifts for policy review

 → Increases trust in analytics through data‑driven commentary on model selection