- Circana
- 3 hours ago
- 5 min read
Table of Contents:
Best Practices for Marketing Mix Modeling Rely Heavily on Accurate and Comprehensive Data
Ultimately, successful marketing mix modeling depends on the quality and completeness of the data on which it is built. Poor data inputs lead to inaccurate models and unreliable predictions.
One significant issue is a lack of granularity and consistency. Data often comes from disparate sources, including a company’s internal systems, walled garden platforms, and third-party trackers. Each has its own format and level of detail, which complicates analyses. Needed data also extends beyond marketing to include non-marketing information, such as supply chain disruptions and out-of-stock events.
Aligning data from different sources with point-of-sale (POS) data poses another challenge. For instance, data for consumer promotions or shopper marketing programs may only include start and end dates. This may require analysts to distribute their spend evenly by week, an assumption that weakens the model's precision. Similarly, obtaining detailed metrics for activities like in-store sampling is difficult, often leading to their exclusion from the analysis.
Managing Data Quality and Harmony Across Platforms and Ecosystems
Brands can overcome issues associated with fragmentation by harmonizing accurate, high-quality data. These insights are crucial, because brands don’t use MMM for a rearview perspective: They want robust information that helps them best determine where to spend their marketing dollars.
If data is incomplete, the reliability of predictions is questionable at best. When results don’t pan out, marketing leaders are less confident when asking for additional funding or defending against budget cuts. In contrast, a highly accurate model enables marketing leaders to confidently reallocate budgets, moving money from low-performing to high-performing drivers, and to ask for similar or more funding next time.
Organizations new to marketing mix modeling often lack the infrastructure or processes required to access and extract essential data efficiently, resulting in delays and limited analytical opportunities. Third parties and data clean rooms are integral in centralizing and standardizing disparate data sources for greater quality, consistency, and reliability. Secure environments for data aggregation and privacy-compliant sharing allow for data integrity, comparability, and a holistic view necessary for effective MMM.
Circana’s marketing mix models, for example, are validated using R-squared and Mean Absolute Percentage Error (MAPE) testing and achieve over 90% accuracy. This level of precision instills confidence for making critical business decisions and managing leadership's expectations. High-quality models also help quantify the long-term impact of marketing investments, creating a flywheel effect in which sustained, optimized marketing spend drives continuous growth.
Effectively Attributing ROI in Marketing Strategies
A core function of MMM is to measure the incremental contribution and financial impact (measured by ROI or ROAS) from marketing strategies. The goal is to provide insights not just to which channels that are driving volume but also whether they are doing so efficiently. This detailed view allows marketers to see a crucial distinction: some channels may drive significant incremental volume but have a low ROI, while others may show a strong ROI but generate less volume.
Understanding this nuance and providing context are key. Circana’s vast benchmark database helps brands contextualize their performance. By comparing their ROI to industry and category averages, brands can determine if their results are strong, average, or weak, providing a clear path for improvement.
Notably, it’s important to distinguish between total ROI/ROAS (total return) and marginal ROI (return on the last dollar spent). While total ROI gives a broad view of a channel's efficiency, marginal ROI is key to optimization. Optimization analyses look at the marginal return across different tactics to determine the most profitable place to allocate the next incremental dollar. This allows for precise, data-driven budget adjustments that maximize overall impact.
Lag Between Modeling and Implementing Strategies
The turnaround time for getting insights and applying them is crucial in marketing mix modeling. The value of analytical outputs declines with delay.
Circana streamlines marketing mix modeling delivery, with full-service, consult-led engagements producing results in as few as 10 weeks, far exceeding historical industry timelines. Agility is also important to brands, who can refresh and update models as often as monthly, helping them shift MMM from a retrospective analysis to a dynamic, real-time business asset.
In addition to speed, operational readiness is pivotal in effective modeling efforts. Recommendations require a solid foundation and process readiness, and Circana supports brands to ensure that organizations, partners, and resources are equipped to operationalize model-driven strategies.
Marketing Mix Modeling Accuracy and Staleness
Models can become outdated because accuracy depreciates with market change. The rate at which a model becomes stale depends on the volatility of the market, the category, and consumer behavior.
In fast-moving sectors, a model can lose its relevance in a matter of months. Regular refreshes are essential to ensure the model accounts for new trends, competitive actions, and shifts in media consumption. Solutions offering monthly or quarterly updates maintain the strategic value of MMM.
Brands also benefit from methodologies that support regular scenario testing and calibration at both DMA and store levels. This ensures an optimal model fit and forecast precision tailored to a brand’s objectives and most recent data.
Marketing Mix Modeling Cost, Time, and Expertise
For years, marketing mix modeling was perceived as time-consuming, expensive, and requiring specialized internal resources. This belief no longer reflects the current landscape, however.
In addition to marketing mix modeling solutions with a faster timeline between 10 and 16 weeks, platforms like Circana's Liquid Mix™ enable clients to refresh their models on a monthly basis. Brands can adapt quickly to changing market conditions and campaign performances, transforming MMM from a slow, retrospective exercise into a dynamic, forward-looking tool.
Drive Incremental Growth with Circana’s Marketing Mix Modeling Solutions
Across every dimension — data integrity, ROI clarity, speed, relevance, and scalability — Circana equips brands to conquer the complexities of marketing mix modeling. Our full suite of solutions is designed to meet different needs and budgets. This includes traditional consultant-led marketing mix modeling, the agile Liquid Mix platform for faster refreshes, and other media measurement options like sales lift studies. This flexible approach allows brands of all sizes to access powerful analytics and drive growth sustainably, without needing to build a large, specialized internal team. By leveraging the right Marketing Mix tools and expertise, brands can implement a robust measurement framework that is both effective and cost-efficient.
