Petavue, please analyze all Closed-Won deals from the last 180 days and compare revenue under two simple models:
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First-Touch: Assign 100% of revenue to the first interaction.
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Last-Touch: Assign 100% of revenue to the final interaction. Then:
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Summarize total revenue by Channel and by the top 20 Campaigns for each model
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Produce a variance table showing First-Touch $, Last-Touch $, and the % difference
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Flag any rows where variance exceeds ±30%
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Recommend which model better matches buyer behavior, based on median journey length and number of touches
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List all channels where the variance between First-Touch and Last-Touch revenue exceeds ±30 percent, showing both model values and variance.
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List the top 10 campaigns by absolute variance, including First-Touch $, Last-Touch $ and % variance.
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Summarize median journey length and average touch count for each flagged channel or campaign.
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Highlight channels and campaigns where First-Touch outperforms Last-Touch by ≥30 percent, with supporting buyer-journey statistics.
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.
Strategic Impact
Delivering a clear comparison of attribution models ensures your revenue crediting matches actual customer paths and strengthens performance decisions. Business outcomes:
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Guides budget allocation toward channels and campaigns under the most appropriate attribution model
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Reduces reporting disputes by flagging large model-driven revenue shifts for policy review
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Increases trust in analytics through data-driven commentary on model selection