Top Highlights
- Traditional marketing mix models (MMMs) struggle due to correlated channels, demand bias, and shape identification issues; these are largely caused by spend that moves in predictable patterns tied to demand.
- The key solution is to incorporate deliberate, varied spend patterns—via strategies like dark months, peak months, or weekly nudges—to generate independent variation and improve model accuracy.
- A designed budget phasing algorithm that schedules these variations can significantly reduce variance and bias—up to 70%—by making channels move independently and revealing their true effects.
- Implementing such phased plans costs only a small share (~1.5%) of revenue and can be scaled across many channels, unlocking more reliable insights and better optimized marketing investments.
Understanding How Your MMM Can Be Misled
Measuring marketing success is tough. Many rely on Marketing Mix Modeling (MMM) to figure out return on spend. Recently, open-source tools like Robyn and Meridian have made running an MMM easier. However, trusting the results is complex. Three main issues often cause problems. First, channels tend to move together because budgets are set in the same planning cycle. When they rise or fall together, the model can’t tell which one truly drives sales. Second, spend often follows demand. If demand isn’t observed directly, proxies are used, leading to biases. This means sales may seem to rise because of marketing, but some of that rise is due to demand changes, not marketing effectiveness. Third, identifying ad effects like saturation and adstock is hard. Without clear variation in spend levels, it’s impossible to accurately determine how ads decay or hit a limit. Recognizing these issues at their core helps us see why models often give uncertain or biased results.
The Role of Budget Variation and Its Challenges
Budget planning usually causes channels to move together. For example, if overall demand is high, all channels tend to increase, making their effects hard to separate. This correlation reduces the model’s ability to identify individual channel contributions. Simulation studies show that high correlation can cause estimated impacts to fluctuate widely. On top of that, since demand isn’t observed directly, models rely on proxies. When demand proxies miss important signals, models overstate or understate a channel’s effect. Similarly, adstock and saturation—how long ads work and when they hit diminishing returns—require different data patterns to identify correctly. Without spend at varied levels and over enough time, models can’t distinguish these effects, leading to wide ranges of possible responses. Essentially, when channels move together, the data lacks the independent variation needed for precise estimates.
Implementing Smarter Planning for Better Results
The good news is, you can improve your MMM’s accuracy by changing how you plan and allocate budgets. Instead of sticking to static budgets, a process called budget phasing intentionally varies spend across channels and time. This variation provides the data needed to separate channel effects, reduce bias, and clarify ad response shapes. Strategies like dark months, peak months, or weekly nudges help create independent variation. For instance, moving spend into one channel briefly or altering weekly budgets from the plan allows the model to see each channel’s true impact more clearly. Studies find that applying these phased approaches can cut variance by over 70% and bias by nearly 50%. Although this may mean temporarily “giving up” some revenue, it ultimately delivers more reliable insights. While these strategies cost a small percentage of the total revenue, they pay off by making your marketing measurement far more trustworthy and actionable.
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