An attribution model that distributes credit across multiple AI citations and other marketing touches that influenced a single conversion, rather than crediting only the first or last touch.
Multi-touch attribution (MTA) for AEO assigns fractional credit to each AI citation, organic page, paid ad, or other touchpoint that contributed to a closed-won deal. Common MTA models include linear (equal credit to all touches), time-decay (more credit to recent touches), U-shaped (more credit to first and last), and W-shaped (more credit to first, lead-creation, and last). For AEO specifically, time-decay and W-shaped models tend to work best because AI citations often appear early in the consideration cycle.
Single-touch models systematically undervalue AEO because AI citations rarely drive immediate conversions. The actual value of an AI citation is creating consideration that converts weeks later through other channels. MTA captures this; single-touch doesn't.
A buyer reads a ChatGPT answer citing your brand on day 1, visits your homepage from a Google search on day 30, downloads a whitepaper on day 45, books a demo on day 60, and closes for $50K ARR on day 90. Linear MTA assigns the AI citation 25% credit ($12.5K). Time-decay assigns less because it was first. U-shaped assigns more (40% credit) because first-touch is weighted heavily.
The terms in this glossary aren't theoretical — they're what Lantern's product calculates and reports every month for B2B SaaS teams. See yours in 7 days. 14-day free trial.
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