"When discussing their results, they tell us that Facebook's reporting or Google Analytics show the ad campaigns as barely breaking even. Yet they keep investing in this channel. They reason that Facebook can only see a fraction of the sales, so if Facebook is reporting a 1x return on ad spend (ROAS) then it's probably at least 2x in reality."
"We see this contradiction all the time. Marketers track results as best they can, but unreliable tooling means they end up making gut decisions. This is a constant issue with marketing attribution. Here we discuss how the marketing world ended up in this position and how new alternatives can help marketers avoid existing issues. A brief intro to rule-based attribution Analytics teams have typically had a variety of attribution models,"
Marketers spending heavily on Facebook often see reporting from Facebook or Google Analytics that shows campaigns barely breaking even, yet they continue investing because they believe the platforms underreport sales and actual ROAS is higher. Unreliable attribution tooling forces many marketers to rely on gut decisions. Rule-based attribution models include single-touch (post-click) and multi-touch approaches like time decay, linear, and U-shaped distributions. Single-touch assigns conversions to a single click; multi-touch spreads credit across touchpoints according to rules. The limitations of these models create measurement gaps and motivate the search for new, more accurate alternatives.
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