Imagine your team has just launched the marketing campaign of the year. You chose the best channels and allocated the budget based on past performance, but as the campaign gets underway, the results fall short of your projections. What went wrong?
Traditional marketing mix modeling (MMM), which relies on historical data, often fails to account for recent market changes that impact purchasing decisions.
If you’ve ever had a time when your top channels weren’t performing as well as expected, you’re not alone. Let’s review the differences between a test-calibrated MMM and traditional models—and discover why you should consider calibration to improve marketing measurement and outcomes.
What is test calibration in MMM?
Test calibration in MMM is the process of refining and adjusting a model by integrating data from controlled experiments—such as A/B studies and lift tests—real-time market insights, and ongoing performance metrics.
Using MMM test results to calibrate your model improves the accuracy of its predictions. Calibration gives you the confidence to plan new campaigns, adapt strategies, and analyze the real impact of your marketing efforts.
Compared to the traditional marketing mix model, test-calibrated MMM is more:
- Accurate: A calibrated model brings in up-to-date insights from tests into your marketing forecasts, helping you gauge the effectiveness of your campaign with greater precision.
- Adaptable: Calibrated MMM continuously adjusts to recent shifts in market trends and customer behavior, while traditional models depend only on past sales data, clicks, conversions, and other performance metrics.
- Effective at measuring incrementality: By integrating results from tests, calibrated models let you isolate the impact of specific channels and tactics on performance, minimizing the influence of seasonal trends or biases in the analysis.
Top 3 reasons why you should integrate MMM and calibration tests
If you use email and social media to promote a new product, for example, traditional MMM could attribute most of your conversions to email based on history, overlooking the product’s appeal on social media. In this case, setting up a lift test could reveal that a specific social media video is generating higher engagement, allowing you to reallocate resources and focus on what’s driving the most impact.
Consider these top three reasons to understand why a model that integrates MMM and calibration tests matters:
- Calibration enhances marketing performance measurement
Calibration involves conducting tests to measure the impact of campaigns, strategies, and offers on performance metrics. Then, you incorporate these results into the model to adjust its predictions, allowing you to map out the right channels for launching a new product and estimating overall performance.
You can use:
- A/B testing: This experiment involves exposing two groups to different marketing treatments. By comparing their outcomes, you can isolate the impact of a specific variable—a call to action, a copy, an image, or other elements.
- Lift test: Similar to A/B testing, it measures the incremental impact of marketing activity by comparing the performance of two different groups and calculating the difference—known as lift—between their results.
- Randomized controlled trials (RCTs): RCTs are a more rigorous type of controlled experiment that involves a larger sample size and more stringent randomization procedures.
These methods can improve your marketing performance measurement, allowing you to make more informed decisions about resource allocation and campaign strategy. With a Bayesian approach, you can continually update your model as new data becomes available, enhancing predictive accuracy for your next marketing initiative.
- MMM test results support better resource allocation
A test-calibrated MMM allows you to continuously incorporate new experimental data, making the model more responsive to these shifts. For example, you can use A/B testing on ad platforms to evaluate two different creatives in a digital marketing campaign. By identifying which creative resonates better with your audience, you can reallocate budgets and resources to the channels and messages that drive the best results.
Discover strategies to overcome cost challenges in marketing decisions
- Calibrated models enable the creation of new marketing strategies
When testing new marketing channels, creative formats, or targeting strategies, relying on a traditional MMM can leave you uncertain about the true impact of these changes. A test-calibrated model integrates direct results from your experiments into the overall analysis, providing a clear picture of what’s working and what isn’t. This level of validation helps you refine your tactics based on:
- Conversion rates: Understanding which campaigns or creatives drive the highest conversion rates allows you to focus on successful strategies.
- Customer engagement metrics: Analyzing click-through rates, time spent on content, and interactions helps gauge how well different formats resonate with your audience.
- Cost per acquisition (CPA): Evaluating the cost of acquiring a customer through different channels enables you to allocate budgets more efficiently.
- Return on ad spend (ROAS): Measuring the revenue generated from each marketing dollar spent facilitates the evaluation of the most profitable channels.
- Audience segmentation performance: Identifying which segments respond best to specific messages or formats supports you in adapting your targeting strategies.
How AI accelerates marketing mix model calibration
AI empowers high-performing marketing teams to operate more efficiently and supports the calibration of your MMM. According to HubSpot’s 2024 State of Marketing Report, seven in ten marketers using AI and automation report that this technology enhances the overall customer experience.
Artificial intelligence streamlines marketing mix model calibration with:
- Data integration: AI facilitates the gathering and integration of data through advanced machine learning algorithms, allowing for a comprehensive view that informs the calibration process.
- Advanced analytics: While traditional calibration methods can be time-consuming, AI identifies patterns and relationships within the data in real time—helping you pinpoint which variables have the most significant impact on marketing outcomes.
- Predictive modeling: AI enhances predictive analytics capabilities, allowing teams to calibrate the model more accurately by anticipating changes in user behavior and market dynamics.
- Lift test evaluation: AI analyzes lift test results and compares them with model predictions, enabling you to identify discrepancies and refine the model accordingly.
- Bayesian optimization: AI employs this technique to search for the best parameters for the marketing mix model, leading to a more accurate model that adapts quickly to new data.
Explore the evolution of MMM: from traditional to machine learning
Experience the power of calibration in MMM
Keen is an AI-powered MMM platform that forecasts, analyzes, and optimizes your marketing investments across all channels. Our software empowers your team to update MMM with data insights, leveraging the full benefits of a Bayesian marketing mix model. By implementing A/B tests and measuring the real impact of your campaigns, you can discover how incremental your media performance is—and much more.
See for yourself the power of test-calibrated MMM. Start your Keen trial today.
