- Circana
- 8 hours ago
- 7 min read
Jonathan Dizney, VP, Client Sales and Insights, Marketing and Media
Susan Goetz, VP, Global Solution Consulting, Media Measurement
Table of Contents:
Marketing Mix Modeling (MMM) enables companies to measure the effectiveness of each marketing tactic so they can understand the impact of their efforts on sales across the business. It is a valuable measurement tool that allows businesses to connect the dots between creative, audience, reach and frequency, brand equity, marketing channels, trade, and retail spend. Organizations with a disciplined approach to MMM can improve ROI as much as 10% to 15% annually.
Given the scope of the process, many brands and advertisers want to know before they delve into such projects how long it will take for them to stand up and execute the models. The short answer: High-quality insights require some time, as data integrity is critical to informed decisions. Although some data can be gleaned in days or even hours, thanks to enhanced automation capabilities, it can take weeks and months to build up the information needed to create and roll out the most effective models. Timelines will vary and, while speed and agility are hallmarks of MMM, it’s important to focus on how to achieve the best results: The when is driven by the what, how, and where.
Factors Influencing MMM Timelines
The timeline for an MMM project is rarely static or set in stone. Several variables dictate the pace of moving from initial discussions to actionable results.
Complexity of Scope
One might assume that the biggest factor in the timing of MMM programs is the number of sales channels. Although that can be a factor, very little marketing is highly specific to a single sales channel.
Other complexities have a greater effect on when and how marketing mix projects unfold. For example, introducing geographic granularity to gauge differences between market areas adds weight to the process. If you are not marketing at a geographic level, this level of detail may not be warranted.
Having too many marketing tactics also adds layers of complexity that can impact timing. Trying to model the “long tail” of spend, instead of focusing on the core tactics, often leads to results that can cloud decision-making with granularity that isn’t actionable. Moreover, adding too many variables (such as media tactics or granular breaks in media tactics) can drive high correlation within the model, which can obscure clear insights on the most critical tactics.
The composition and complexity of teams likewise contribute to the timetable. MMMs typically affect many business functions, including marketing, sales, finance and external agencies; although collaboration is pivotal to outcomes, MMM recommendations may change during the process among larger teams.
In general, increased complexity doesn’t always equate to a better MMM. It is more important to get actionable insights at the level that empowers decisions, instead of striving for a theoretically “perfect” model that includes every dollar spent.
Data condition and quality
Data is the biggest wild card in any marketing mix timeline. The modeling phase itself is predictable; the backend timeline rarely shifts based on scope. However, the state of data before it reaches the modelers might lead to stagnancy in the process.
It’s been said that projects only move as fast as the slowest piece of data, and that applies to MMM. If data is inconsistent, not clean, or scattered across multiple owners, the timeline stretches. A lack of proper data preparation forces the project to pause, waiting for inputs to be cleaned and validated.
Understanding where data is internally is key. Who are your data owners? How are they tracking data? If there is no accountability in data, even outside of MMM, it can cause issues during the process.
Methodology
The choice of statistical methodology plays a role in the MMM trajectory. Advanced statistical models do not drastically affect the modeling timeline. Whether you use standard regression or more complex machine learning techniques, the computational time is manageable.
The risk lies in rushing the methodology. There is no benefit to speeding toward a wrong decision. It is better to invest time ensuring the model accurately reflects reality than to expedite a process that yields flawed strategic guidance.
Marketing mix modeling automation tools
Automation is a powerful accelerator in MMM that speeds data collection, the identification of outliers, and harmonization. Automated tools can quickly flag inconsistencies that would take a human analyst days to find. This streamlines the validation phase, allowing the team to move faster into actual modeling and insight generation. That said, automation can’t replace human knowledge or experience during decision-making phases.
In-house vs. external expertise
Deciding between building an in-house team or hiring an external partner influences MMM timelines. An internal project may extend the timeline, because MMM is not the team's full-time focus. If an internal team is juggling multiple priorities, the process can be in limbo or delayed. External partners specialize in this work; it is their core competency. Circana’s MMM experts, for example, see thousands of MMM programs a year, and can spot emerging opportunities and problems earlier and build on learnings and best practices.
Marketing Mix Modeling Timeline Breakdown
While the pace may be different for each company, a typical MMM project follows a rather structured path. Understanding these phases helps manage expectations and optimize resources.
Data collection and cleaning
Data input is the basis of MMM projects. The length of this phase depends on an organization's data maturity.
If a company has a strong handle on data owners and collection processes, this phase might consume 30% of the total project time. However, if data isn’t clean or is scattered and is not reviewed regularly, this collection phase can eat up 70% or more of the timeline.
Key data sources that frequently cause delays include trade, consumer promotion, and retail media. Other lags can be caused by changing agencies during a recent period, a move often stalls the data retrieval process.
It is important to have complete, consistent data for at least the latest 52 weeks. Although you want as many data points as possible to capture seasonality and trends, the immediate past year must be broken down exactly how you want it measured.
Model building and calibration
Once data is validated, the modeling phase begins. Among other steps, this involves running statistical regressions, calibrating the model against historical trends and validating that the outputs make business sense.
If inputs are clean, this phase is mostly efficient. To optimize the timeline, an organization can focus on ensuring that the model captures the top tactics that drive the bulk of its business results.
Insights, optimization and reporting
The final phase of MMM transforms mathematical outputs into business language. This is where you answer the “So what?” questions. The timeline here includes interpreting the results, building optimization scenarios for future spend, and presenting findings to stakeholders. This phase shouldn’t be rushed, as it is the bridge between data science and marketing strategy.
Ongoing updates
MMM should not be a “one and done” exercise. If it is only an annual process, your organization never gets good at it. Regular updates ensure that data collection muscles remain strong and that insights reflect current market conditions. Marketers can ensure faster alignment between media spend and sales data by maintaining clean data and tracking it continuously, rather than scrambling to assemble it once a year.
Ultimately, the goal of MMM is not to just measure what was done in the past, but to influence positive change in the future. The models uncover marketing elements that worked well and the ones that didn’t, so marketers can adjust accordingly.
Turn Marketing Data into Measurable Growth with Circana’s Marketing Mix Solution
Navigating the complexities of Marketing Mix Modeling requires precision and expertise. By understanding the levers that impact your timeline, including scope complexity and data integrity, you can better prepare your organization for success. Circana’s Marketing Mix solution empowers companies to measure their market share, understand the underlying consumer behavior driving it, and accelerate their growth. Using MMM in tandem with lift studies that measure incrementality amplifies the success of campaigns. By leveraging the right expertise and focusing on clean, actionable data, you can reduce timelines and increase the impact of marketing decisions.
Frequently Asked Questions about Marketing Mix Modeling
How does Marketing Mix Modeling work?
Marketing mix modeling uses historical data to analyze how unique marketing activities incrementally impact ROI and other key performance indicators. Marketing Mix Modeling platforms analyze the relationships between a variety of factors related to a brand’s marketing efforts. This can help brands and advertisers optimize their spend and shift strategy based on what methods are driving sales or brand growth.
How accurate are Marketing Mix Modeling results?
Marketing mix modeling outputs are extremely dependent on the modeling methodology used and the quality of the data. The recommendations that come out of the model are most valuable to brands that have clean, organized, and comprehensive data and are able to implement the recommended changes to strategy. In terms of platforms, some platforms are analyzing data down to an individual store level vs a DMA-level (designated marketing area). Granularity plays a large role in accuracy, especially when measuring the incremental impacts of individual marketing channels.
How much data is needed for accurate Marketing Mix Modeling?
Because marketing mix modeling relies on historical data to measure impact, more data is always better. In terms of “more” this can be understood both in terms of time, and comprehensiveness. For example, having at least 2-3 years of historical data is often a necessary foundation. That said, complexity is also key. Accuracy is based on data quality as much as quantity. Daily data is preferred over weekly, and data needs to account for seasonality, economic shifts, digital/offline spend, pricing, promotions, and sales. The more complete data a brand can put into their Marketing Mix Modeling, the more accurate their results will be, which is why Marketing Mix Modeling is an effective long-term strategy.
