We often find ourselves trying to make sense of outputs coming from multiple models. Here’s a general set of guidelines for building connections between Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA), designed to improve their integration and application:
Include Offline Data in MTA: Use probabilistic attribution or modeled estimates to capture offline channels like Direct Mail, Addressable TV, and in-store interactions within MTA.
Incorporate Granular Online Data in MMM: Add data from specific digital platforms like YouTube, programmatic advertising, and social media to MMM to reflect online contributions more accurately.
Unified Data Pipeline: Build a centralized data repository that normalizes online and offline data for seamless integration into both models.
Outcome: A comprehensive data foundation that reduces blind spots and ensures both models reflect the full scope of marketing activities.
Measure Indirect Contributions in MMM: Identify how Enterprise-level campaigns influence LOB-level outcomes and vice versa, quantifying the spillover effects.
Integrate Halo Effects into MTA: Develop a framework to assign partial credit for indirect contributions from media that influence conversions across multiple channels or products.
Sensitivity Analysis: Use sensitivity testing to validate and refine the halo effects captured in both models.
Outcome: Improved understanding of cross-channel and cross-product impacts, enabling more informed allocation decisions.
Develop Marginal ROI Curves: Analyze how incremental spending in each channel affects returns to identify optimal investment levels.
Monitor Diminishing Returns: Identify when additional spend in a channel produces lower returns, signaling the need for reallocation.
Dynamic Efficiency Benchmarks: Replace static thresholds with dynamic benchmarks that adapt to channel, product, and investment levels.
Outcome: Efficient allocation of budgets, avoiding oversaturation of high-performing channels while identifying underinvested opportunities.
Reconcile Channel Contributions: Compare MMM and MTA outputs to identify where one model over- or under-attributes contributions (e.g., social media or Direct Mail).
Adjust Attribution Rules: Update MTA rules to reflect MMM’s macro-level insights for channels with significant offline or indirect impacts.
Iterative Calibration: Regularly refine both models through feedback loops, testing outputs against real-world performance.
Outcome: Consistent, aligned channel-level insights that reduce conflicting recommendations.
Define Model Use Cases: Clearly delineate when to use MMM (e.g., long-term strategy, channel mix decisions) versus MTA (e.g., short-term campaign optimizations).
Combine Outputs: Develop a blended framework that incorporates MMM’s macro-level trends with MTA’s granular insights to provide a comprehensive view.
Stakeholder Training: Educate teams on interpreting and applying both models effectively, ensuring alignment in decision-making.
Outcome: A clear and actionable framework that bridges the gap between MMM and MTA, facilitating better strategic and tactical decisions.
Frequent Updates: Update both models regularly to reflect changes in media landscape, competitive dynamics, and consumer behavior.
Integrate New Technologies: Incorporate AI and machine learning to improve prediction accuracy and uncover deeper insights.
Cross-Model Validation: Use overlapping data and scenarios to validate the accuracy of each model, ensuring robustness.
Outcome: Models that remain relevant and reliable, providing accurate insights for evolving marketing environments.