AI Attribution Model for Marketing: From CPA to Long-Term Value
MarTech

How we developed an ML-powered attribution model for a digital commerce company that shifted budget decisions from short-term CPA to predicted 6-month LTV, improving spend efficiency and customer acquisition quality.

  • TECHNOLOGIES

    • Python
    • BigQuery
    • Google Analytics
    • Google Tag Manager (server-side)

  • TEAM COMPOSITION

    • Data Engineer
    • ML Engineer
AI Attribution Model for Digital Commerce Company

Background

Our client is a large-scale digital commerce company operating in a highly competitive, multi-channel acquisition environment.

The marketing team manages a mix of paid and organic channels, with significant investment in performance marketing and a strong reliance on data for campaign optimization.

Project Recap

  • Despite having mature analytics in place, key decisions were still driven by short-term metrics, limiting long-term growth.

  • A custom ML-powered attribution tool that evaluates every channel based on predicted 6-month customer lifetime value instead of first-touch conversions.

  • More efficient budget allocation, reduced spend on low-quality acquisition, and a shift toward long-term, value-driven marketing decisions.

Challenge

The client’s marketing team relied on traditional attribution models that assign most or all conversion value to the final touchpoint. This created a distorted view of performance. As a result, marketing investments risked reinforcing short-term gains at the expense of long-term growth:

  • Undervalued Top-of-Funnel: Channels that support early-stage engagement (e.g. content, brand, social) appeared unprofitable
  • The CPA Trap: Low-CPA channels were prioritized, even when they attracted low-retention users
  • Misaligned Spend: Budget was optimized for what looked efficient, not what actually drove revenue

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Solution: ML-Driven Value Forecasting

We developed a predictive analytics solution that helps the client’s marketing team evaluate channels based on long-term customer value in addition to immediate conversions. The solution operates as a data layer integrated with the existing marketing stack, supported by custom Tableau visualizations.

  • Eliminating misleading signals

    The AI-powered tool replaces last-click attribution with an LTV-weighted prediction model, removing reliance on surface-level CPA.

  • Value over first transaction

    Every touchpoint is evaluated based on its contribution to predicted 6-month customer lifetime value, not just the initial conversion.

  • Data grounded in real behavior

    The platform uses historical and behavioral data to analyze how different channels drive repeat purchases and retention.

  • Seamless integration

    Built as a data layer on top of the current stack, the AI tool integrates with marketing and analytics platforms and delivers insights through custom Tableau dashboards.

  • Continuous, dual-layer forecasting

    The system runs two estimation cycles: hourly forecasts for intraday optimization and daily updates for a 6-month forecasting window, supporting both immediate actions and strategic planning.

  • Actionable outputs for automation and teams

    The solution provides automated bidding signals for platforms like Meta Ads, while also delivering data marts with campaign scoring and recommendations for human-in-the-loop decisions where more nuanced judgment is needed.

The model gave us a much clearer view of which channels were creating real value. Some assumptions we had relied on no longer held up once we looked at retention and lifetime value.

Chief Marketing Officer,
Digital Commerce Company

Results

Our solution helped the client shift from short-term metrics to value-driven growth in their performance marketing.

  • “The most interesting part of this project wasn’t even the model itself, it was the inversion. The channel with the lowest CPA turned out to produce the worst customers. Last-click wasn’t just missing signals, it was misleading. Once that became clear in the data, the client’s budget decisions followed naturally. The real challenge was building the data layer that made this visible.”

    – Denys Pluhatar, AI Lead at Beetroot

  • Reduced Hidden Acquisition Waste

    Marketing spend was rebalanced toward high-LTV acquisition sources. The client uncovered opportunities that were previously overlooked.

  • More Efficient Budget Allocation

    Marketing spend was rebalanced toward high-LTV acquisition sources. The client uncovered opportunities that were previously overlooked.

  • Higher Quality of Acquired Customers

    By optimizing for lifetime value, the client gained the possibility to attract users more likely to retain and generate repeat revenue.

  • More Confident Decision-Making

    The company’s marketing team can now act on predictive insights rather than waiting for lagging indicators.

  • Long-Term Competitive Advantage

    While competitors continued optimizing for visible CPA metrics, the client leveraged predictive insights to invest in long-term value, building a competitive edge that is difficult to replicate.

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