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Use Data-Driven / Algorithmic Multi-Touch (best overall): When Artificial intelligence optimization (AIO) drives early awareness in ChatGPT/Gemini/Perplexity, a data-driven model learns how those assistive touches contribute downstream without over-crediting last click. High Clarity’s AIO specifically targets visibility across AI platforms, so an adaptive model captures those assists.
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Favor Position-Based (U-shaped) if data is thin: Heavily weight first touch (AI discovery) and last touch (conversion), with the middle shared—useful while your AIO program ramps up.
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Add Time-Decay for long journeys: If buying cycles are weeks long, decay models recognize persistent AIO-led research touches earlier in the path.
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Include View-Through/Implied Touches: AIO often creates non-click exposures (answers cited in AI results). Track branded search lifts and direct visits as proxy assists in your model.
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Run Incrementality Tests: Geo/cell tests validate the true lift from AIO discovery versus organic baseline.
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Blend with MMM for budget decisions: Use Marketing Mix Modeling for channel ROI at the portfolio level, then reconcile with Multi-Touch Attribution for journey insights.
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Instrument the stack: Ensure CRM + analytics + call tracking are tied to attribution so upstream AIO touches connect to revenue, a capability High Clarity emphasizes.
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Structure content for AI engines: Semantically rich, AI-readable content increases assistive touches your model will detect (and reward).
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Iterate quarterly: Re-train the model as AI engines and prompts evolve to keep attribution honest.
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Bottom line: Start with Data-Driven Multi-Touch; backstop with Position-Based until volume is sufficient, and validate with incrementality + MMM.
Want a framework tailored to your stack? Explore Artificial intelligence optimization (AIO) with High Clarity: high-signal AIO content for AI engines, attribution-ready analytics, and revenue tracking.