The Revenue Hidden in Your Marketing Attribution Data

Episode notes

Most B2B marketers look at attribution reports and see a mess: last-touch credits the wrong channel, first-touch buries the closer, and the CMO wants a single number. In this episode, Lucas and Luna dig into why the default attribution models in most marketing automation platforms are structurally biased - and what a better approach looks like. They walk through a concrete example: a software company that ran a head-to-head test of last-touch versus a simple multi-touch model, and found that their paid search spend was overvalued by 40 percent while their email nurture was undervalued by 25 percent. They explain the difference between attribution and measurement, why the goal isn't a perfect model but a consistent one, and how to choose a model that matches your sales cycle length and content maturity. If you've ever felt queasy about your attribution numbers, this episode gives you a practical path forward.

Related episodes across the Index

Other episodes covering the same guests and topics, from across The B2B Podcast Index.

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• Stella: Why strong ROAS can hide what's really driving revenueThe SaaS Growth podcast · on Multi-touch attribution72 / 100
• How CMOs Use Causal AI to Find Hidden Growth LeversThe CMO Podcast with Fexingo · on Marketing measurement

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