- Circana
- 3 hours ago
- 6 min read
Table of Contents:
How Can Marketing ROI Be Measured Accurately in an Omnichannel Retail Landscape?
Instead of looking at touchpoints in isolation, brands should view them as a linked system. Today’s consumers shop at different physical and digital locations, so exposure to ads across that linked ecosystem likely influences purchases at any given stop in the omnichannel journey. Brands, then, can entice omnichannel buying and also gauge how their various platforms are contributing to sales.
External factors also affect the precision of ROI assessments. For instance, an uptick in soup sales during an especially cold winter might be wrongly attributed to a simultaneous advertising campaign if weather data is excluded from the analysis. Failing to account for such circumstances can lead to misattribution of sales and a distorted view of what truly drives revenue.
What Role Does Marketing Mix Modeling Play in Measuring ROI?
Marketing mix modeling (MMM) has re-emerged as a solution to challenges that other ROI measurement approaches, such as multi-touch attribution, struggle to solve. As walled gardens like search engines and social media platforms limit data visibility and as privacy regulations tighten, MMM offers a resilient alternative. It utilizes data above the user level — often at the store or market level — ensuring privacy compliance while still capturing the full scope of marketing effectiveness.
As the shopper journey has evolved and broadened, so, too, has MMM. Moving beyond static reports and a backward-looking “report card,” it is now more actionable and decision-oriented. Today’s MMM allows for business driver analysis, showing brands exactly what drove their sales, whether it was media, price, or external economic factors.
MMM analyzes marketing impact
The core strength of MMM lies in its ability to establish causality, not just correlation. It separates marketing efforts that actually lead to incremental sales from those that happen to coincide with strong performance. This is achieved by quantifying the impact of marketing efforts against a baseline, essentially asking what would happen to sales if a specific activity were removed or increased.
To ensure accuracy, MMM rigorously accounts for non-marketing factors such as pricing strategies, promotional activities, seasonality, and broader economic conditions. By controlling for these variables, the model isolates the true marketing impact for a clearer ROI. For example, a sales spike during a holiday promotion can be reviewed to reveal how much lift came from the season itself versus the advertising support.
Without this level of scrutiny, certain tactics appear deceptively effective. Lower-funnel tactics, such as branded search or retargeting, might seem efficient in platform-specific reporting because they capture the consumer right before the purchase.
However, these channels are frequently overvalued until MMM is applied, indicating that demand originated from upper-funnel investments in brand awareness that occurred weeks earlier.
MMM supports cross-functional decision making
One of the most understated benefits of MMM is its ability to align marketing and finance teams. Historically, these departments have spoken different languages: Marketing focuses on engagement and brand health, while finance demands hard ROI numbers.
MMM bridges this gap by providing a precise, mathematically derived ROI number that finance can trust. It moves the conversation from qualitative feelings about a campaign to quantitative facts about financial contribution.
Implementing a shared MMM framework enhances accountability across the organization. It influences long-term investment decisions by validating the necessity of upper-funnel spending, which often yields slower but more sustainable returns than short-term performance marketing. This alignment prevents the proverbial “race to the bottom” where CFOs might otherwise push for cutting costs in areas that don't show immediate ROI, inadvertently stifling future growth.
Advanced analytics reveal deeper insights into marketing performance
Advanced analytics within MMM reveal insights that likely remain hidden in standard attribution reporting. While a standard report might indicate that sales declined in a given quarter, MMM gets to the reasons for the decline. It might reveal that although media spending was efficient, a competitor's aggressive pricing strategy or an unseasonably cold or warm month dampened the results.
This depth of understanding allows brands to move beyond simple performance tracking to strategic optimization. It uncovers the interaction effects between drivers, such as how a price promotion interacts with a specific media channel to amplify results. By understanding these dynamics, brands can optimize the entirety of their annual spend, ensuring that every dollar works harder.
What to Look for in a Marketing Mix Modeling Solution
Analytical Strength and Scalability
When choosing an MMM solution, robust analytics are the starting point. High quality data, including granular point-of-sale (POS) and casual data, is the foundation of informed decision-making that uncovers opportunities for optimization and stronger returns. Building on that foundation, brands amplify results by working with partners who have extensive industry experience in both collecting and analyzing insights.
Scalability is a critical factor as well. Different business sizes and objectives require different approaches. A brand spending $5 million annually has different needs than a global conglomerate, yet both require models that are robust enough to stabilize results and ensure they make business sense.
Turning Models into Decisions
The differentiator between a solution that drives decisions and one that simply produces reports is the partnership model. A trusted MMM provider acts as a consultant, collaborating with clients to understand the nuances of their business before modeling begins and continues throughout the process. They ensure the model reflects reality, rather than just counting numbers in a vacuum.
Data granularity, refresh cadence, and scenario modeling are vital for real-world MMM applications. Granularity provides richer insights into specific store clusters or product lines, while a regular refresh cadence balances the need for speed with the necessity of statistical accuracy. Scenario modeling transforms the model into a live tool, allowing marketers to simulate outcomes: “If I shift 10% of my TV budget to digital video, what happens to my revenue?” This capability makes the model relevant every single day, not just during annual planning cycles.
How Circana’s Marketing Mix Modeling Helps Brands Turn Insights into Action
Circana’s Marketing Mix solution, anchored by an unmatched dataset and longstanding industry expertise, helps brands make the most strategic decisions with an average annual ROI increase of 10% to 15%. Powered by AI, the forward-thinking tool includes accurate historical modeling and AI-driven optimizations.
Our Liquid Mix™ self-service platform, launched in 2025, is self-funded from its own revenue coming from MMM solutions. During the first year of adoption of Liquid Mix, brands can generate up to a 45X return on their investment in the solution.
Ultimately, going beyond incremental ROI and ROAS, businesses can leverage Circana’s MMM solutions to identify which strategies and channels lead to true growth. As a result, budgets can be allocated across the right channels with the right messages that fuel revenue.
Frequently Asked Questions About Using Marketing Mix Modeling to Drive ROI
How Is Marketing Mix Modeling Different from Attribution Models?
MMM and attribution models serve different and distinct purposes in understanding marketing performance. MMM takes a holistic approach, analyzing the combined impact of all marketing activities and external factors on overall business outcomes, such as pricing, promotions, and economic conditions. It works with aggregated data, like market or store-level information, which makes it privacy-safe and ideal for strategic, long-term planning. In contrast, attribution models focus on assigning credit to individual touchpoints, often within digital channels, using granular, user-level data. This makes attribution models more tactical, helping marketers evaluate the short-term performance of specific campaigns.
When Should a Company Use Marketing Mix Modeling?
Companies should consider using MMM when they need a comprehensive understanding of how various factors influence their business performance. For instance, MMM is ideal for businesses looking to optimize their marketing budgets across multiple channels and campaigns. It is particularly effective for companies with annual marketing spends of $3 to $5 million or more, as it can deliver significant ROI improvements — often between 5% and 20% — after budget optimization. Additionally, MMM is a powerful tool for long-term strategic planning, helping businesses analyze year-over-year sales changes and identify the key drivers behind those shifts.
Can Marketing Mix Modeling Be Used for Budget Planning and Forecasting?
Yes, MMM is an excellent solution for budget planning and forecasting. One of its standout features is its ability to conduct scenario modeling, which allows businesses to simulate and optimize their investments under various conditions. This capability makes MMM an “always-on” solution, allowing companies to make informed decisions without the need for frequent reruns of the model. Additionally, MMM provides precise ROI calculations and dollar-for-dollar performance metrics, which help businesses allocate their budgets effectively. By offering insights into the relative impact of media, promotions, and other activities,
What Decisions Can Marketing Mix Modeling Help Improve?
MMM can enhance a wide range of marketing decisions. For example, it helps businesses determine which channels to prioritize for maximum ROI and identify the types of campaigns that are most likely to succeed. It also provides valuable insights into how to balance growth and efficiency, enabling companies to make informed trade-offs between maximizing ROI and achieving long-term growth. By accounting for all relevant data sources, MMM reduces risk and uncertainty, increasing the accuracy and credibility of marketing performance insights. On a strategic level, it informs long-term investment decisions, such as allocating resources to upper-funnel marketing efforts that drive awareness and attract new customers.
