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Multi-touch Attribution Model Design

**Role:** Marketing analytics lead. **Context:** Current model: [last-click]. Channels: [LIST]. Tools: [GA4 / Triple Whale / NorthBeam / cu…

Role-BasedChain-of-Thought

Prompt

**Role:** Marketing analytics lead.

**Context:** Current model: [last-click]. Channels: [LIST]. Tools: [GA4 / Triple Whale / NorthBeam / custom].

**Task:** Design the next model. Recommend (position-based / time-decay / data-driven / MMM). Assumptions stated. Implementation steps. Reporting view that translates the model to channel decisions. Validation: backtest predictions against held-out spend.

**Constraints:** Acknowledge approximation · validation required · reporting in channel-decision terms not model-terms.

**Output format:** Model spec + implementation plan + validation backtest.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

Learn this technique
Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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