5-Steps for forecasting revenues on upcoming marketing campaigns

Managing the marketing budget with intelligent processesMarketing organizations typically have a budget and a revenue target – the working assumption being that the budget will be enough 🙂 to hit the target. Through the course of the year, the marketing manager has to decide how to allocate the budget across the marketing channels. The particular tactics in this post tackle two very specific parts of this problem – (1) How to estimate the revenues from an upcoming (direct mail or email) campaign; (2) How to tweak the campaign allocation around the maximum permissible list size.

First you need this foundation

This gives you a basic mapping of revenues per customer for past campaigns, such as the metrics below.

screenshot past campaign metrics


And then you need basic data (see the graphic below)

  • the fixed base cost of your campaignInput data for campaign revenue estimation screenshot
  • the cost per prospect(optional, but usually important for direct mail)
  • the cost of the promotion per responder (e.g. free shipping)
  • the cost per responder as % of sales (e.g. discount on marked price)
  • the list-size limit of the campaign.

The Five Steps to forecasting the response to an upcoming campaign are as below

  1. Rank order the segments on their revenues per contact
  2. Estimate the response per segment using the response coefficient from the past campaigns
  3. Allocate the permitted list size across the segments (in order of the ranks identified in #1)
  4. Compute the overall revenues across the allocated population
  5. (Optional) Override the allocation in #3 to enter your own segment penetration and re-do #4.

If you want to see the detailed computation download the spreadsheet linked below.

Download WorksheetThe above method is simple but is a reliable estimate. There are some caveats.

  • New (First-of-its-kind promotion) campaigns: The current design is based on look-alike campaigns. If introducing a new offer or creative, it is hard to gauge the expected returns. The recommendation in this case is to use the metrics from the next closest campaign and apply a ‘safety’ factor of 65% to tamp down on the expected revenues.
  • Fatigue and cannibalization: In general, the best customers will respond to most any of your offers, but repeatedly targeting them will not grow your revenues. It can create fatigue or cannibalize revenues across campaigns.
  • Seasonality: The design above is using the assumption that the returns from the previous month’s campaign hold true in the next month. This assumption is not valid like with seasonal spikes around Christmas, Black Friday etc. This issue can be addressed by incorporating seasonality in the calculation process.

The steps above are best industry practices. They can be manually executed or automated to give the marketing manager some breathing room.

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