What Is Marketing Mix Modeling? Benefits and Challenges

Marketing mix modeling is a statistical technique for measuring how different marketing channels impact business outcomes. The goal of the modeling process is to transform raw data on various marketing channels’ performance into actionable insights for marketing teams. 

According to the 2025 edition of The CMO Survey, 63% of marketers reported growing pressure from their CFO to prove marketing effectiveness. Brands rarely put their marketing budget into a single channel, so it’s important to see what’s working, forecast results, and allocate marketing spend effectively. Marketing teams use the insights from marketing mix modeling to prove impact and make a case for increased marketing spend to executives. 

Read on to learn what marketing mix modeling means, how it compares to other statistical techniques for marketing measurement, and when it makes sense for an ecommerce brand to invest in MMM. 

What is marketing mix modeling?

Marketing mix modeling (MMM) is a data-driven method for understanding how different marketing channels drive revenue. It uses historical data from your mix of marketing channels to measure which channels have the highest return on investment (ROI). 

MMM requires a few years of your store’s sales history and a statistical model that analyzes all of the data from your marketing investments. The model accounts for the influence of internal factors (such as the relationship between different marketing channels) and external factors (like seasonal trends, competitor activity, and economic shifts). The result is an in-depth report on marketing effectiveness that goes beyond individual campaign metrics like click-through rate (CTR).

By considering how multiple marketing variables interact with non-marketing factors, you’ll get a more accurate understanding of success: For example, you might assume that increased sales during your shoppable TV ads flight mean TV is a successful channel—but MMM might reveal that your seasonal email campaign actually had a greater impact on sales overall. 

Marketing mix modeling vs. marketing attribution

MMM is different from another common technique for measuring marketing efforts: attribution models. 

First-touch and multitouch attribution trace individual consumer behavior and assign credit for sales to specific touchpoints, like an ad a customer clicked on or an email they opened. These models require trackable clicks and often use cookies, small data files that a web browser stores on a user’s device, to understand customer behavior and credit business growth to specific marketing channels. 

While attribution models serve a purpose, they have limitations. As data privacy laws tighten around the use of customer data, the rise of cookieless tracking can challenge the accuracy of first-touch attribution and multitouch attribution. They also only track digital marketing, which excludes offline marketing campaigns. If a customer walks past a billboard and makes an online purchase a few days (or a few weeks) later, attribution models have no way of crediting it.

MMM doesn’t rely on individual shopper data. The model works with weekly ad spend and sales volume totals to measure channel or campaign effectiveness. However, MMM also comes with limitations: It’s more expensive to run, requires a large set of aggregate sales data, and lacks granularity for day-to-day decisions.

You don’t necessarily have to choose between attribution or marketing mix modeling. Many marketing teams use both models in parallel.

Benefits of marketing mix modeling

• It provides zero-click and offline channel insights
• It complies with strict privacy laws
• It differentiates between marketing-driven sales and baseline sales

Here are the potential benefits of marketing mix modeling:

It provides zero-click and offline channel insights

Marketing efforts like TV commercials and subway posters drive sales without leaving a click trail, so attribution typically gives them zero credit. Marketing mix modeling estimates the contribution of zero-click and offline channels using spend and sales pattern data, so you can understand how both online and offline marketing tactics affect business outcomes.

It complies with strict privacy laws

Since MMM runs on aggregate totals, rather than cookies or pixels, it’s less vulnerable to new privacy laws or updates that degrade ad platform data. For example, Apple’s App Tracking Transparency makes every app ask permission before tracking consumers. MMM keeps working, even as devices share less about each customer.

It differentiates between marketing-driven sales and baseline sales

Many brands would achieve some revenue even if their marketing spend were zero, thanks to repeat customers and predictable seasonal demand cycles. Marketing mix modeling takes baseline sales data into account, so the email campaign you run on barbecue grills doesn’t get inflated credit right before the July 4th weekend.

Challenges of marketing mix modeling

• It requires historical performance data
• It can give imperfect predictions
• It requires expertise and budget

Attribution has flaws, and so does MMM. Here are the challenges to keep in mind as you approach marketing mix modeling:

It requires historical performance data

A trustworthy marketing mix model analyzes at least two to three years of weekly sales data, with some level of variation in how much you spent on different marketing tactics. If your store launched seven months ago or you spend the same amount every month, the model likely won’t have enough data to provide meaningful insights. 

It can give imperfect predictions

Like any predictive model, MMM finds correlations and can mistake coincidence for cause. For example, it might credit sales to your scaling GEO strategy for sales that actually came from a viral TikTok moment.

MMM is great for informing your team’s marketing strategy at large, but it doesn’t necessarily support day-to-day decision-making. The model sees each channel as one big bucket: You’ll see directionally that Meta is working, but not which of the 12 ads inside your account deserves the credit. And since it typically refreshes monthly or quarterly, it’s not a huge help for deciding what to pause (or double down on) today. 

As such, it’s not uncommon to run attribution models and MMM side by side and let conviction cover the rest. That means funding marketing you believe fits your brand, at a budget you can afford to be wrong about. For example, Manscaped Founder Paul Tran built the men’s grooming company on trackable Facebook ads, then expanded into sponsorships and out-of-home campaigns, including a UFC sponsorship, a Penn Station takeover, and a billboard outside JFK Airport. 

“I can guarantee you there’s no company out there that absolutely can track every single metric of their marketing spend and be really accurate,” Tran says on an episode of Shopify Masters. “There’s mixed media modeling. There’s last-click attribution. It’s so hard.”

This uncertainty requires accepting a bit of risk in your marketing spend. As Paul says, “We believe that a partnership with the UFC was phenomenal. But there’s no way to track how much it actually earned us in terms of revenue. With performance marketing, you spend a dollar on Facebook, you know what your ROAS [return on ad spend] is. Whereas [with] brand marketing, you just really don’t know.”

It requires expertise and budget

There are two ways to get started with marketing mix modeling. First, there are open-source tools like Meridian (created by Google) or Robyn (created by Meta). While these tools are free to download, they’re raw code, which means you need to hire or have an analyst who works in Python or R to run them for your team. 

Then, there are paid platforms you can access for a fee, but they’re often priced for brands with large marketing budgets. For example, Prescient AI, a popular MMM platform, is built for businesses spending $100,000 or more a month on advertising.

For many small ecommerce merchants, MMM can probably wait. If you sell through a couple of channels and nearly all your sales happen online, native tools—such as Shopify’s built-in attribution models—will answer most of your questions about what’s working or not. Many marketing mix elements can be expensive to execute and aren’t worthwhile until your marketing tactics outgrow what clicks can explain.

How to do marketing mix modeling

1. Gather sales data
2. Run the software
3. Validate the findings
4. Compare each channel’s performance
5. Test other marketing spend strategies

Use these steps as a guide to start marketing mix modeling:

1. Gather sales data

A typical model needs two to three years of history, recorded week by week, across three types of aggregated data:

• Sales data: This includes weekly revenue and order counts, which you should have handy from your store’s reports. If you run an online store, this data will live within the reports of your ecommerce platform. For brick-and-mortar stores, the numbers come from your point-of-sale system.

• Marketing data: Collect weekly marketing spend by channel, and ideally reach and impressions as well.

• Outside factors: Add any factors that influence your sales but don’t directly represent channels. Track factors such as holiday spikes (e.g., Christmas), discount campaigns you’ve run (e.g., 20% off for one weekend), price hikes, or a flagship product being out of stock for the month.

If you use Shopify, Shopify’s channel performance report tracks sales, sessions, cost, and ROAS, among other metrics. Export reports from your ad accounts to fill in the rest of your paid media and reference other bookkeeping to include any offline spending. 

For the outside factors, look back on your promotional calendar and the inventory reports. If you’re using an MMM platform like Prescient AI, it can also connect to Shopify and your ad accounts, pulling most of this raw data in automatically.

Then, you need to put all this information into a weekly format before you can use the model. The process of organizing data weekly happens automatically if you opt to use an MMM platform. If you’re taking a more manual or open-source approach, your data analyst can use an LLM to organize the information.

2. Run the software

Your chosen software will perform a regression analysis to examine the relationship between past advertising spend and sales history. This analysis estimates how much each channel contributed to overall brand revenue.

The model will account for how advertising works in the real world. For example, spending and sales aren’t expected to line up neatly within the same week. A person might see an ad this week and not buy your company’s product until next week—a lag that modelers call carryover. 

It should also account for diminishing returns when assessing results or making suggestions. To simplify, think of it like this: The first $1,000 you put into a channel reaches the people most likely to buy. But every $1,000 after that reaches people slightly less likely to care, until more budget stops producing anything at all.

3. Validate the findings

Before you get comfortable using the model to predict the future, validate it against known facts. You can withhold several recent months of marketing data for validation, then have the model forecast sales for those months and compare its predictions with your actual business outcomes.

This validation step, known as backtesting, is often done automatically on many MMM platforms. When the model forecasts $412,000 in total revenue for Q3, and you’ve recorded $407,000 in actual revenue, it means the model is well-calibrated. However, if it’s significantly inaccurate, that signals you need additional (or improved) data.

4. Compare each channel’s true performance

A functioning marketing mix model will report the share of revenue and ROI of every dollar spent across your marketing channels. Once it’s ready, compare the model’s numbers against attribution reports, such as Shopify’s marketing reports and the conversion counts inside your ad accounts. 

Look for numbers that don’t match. Maybe your last-click report says Pinterest drove 1% of sales while the model credits it with 7%. That mismatch could mean that shoppers see your lamp on Pinterest, take a screenshot, but never click. Then they buy it two weeks later through a Google query. Last-click reporting hands that sale to organic search, while the model notices sales rising and falling with your Pinterest spend and credits Pinterest. 

5. Test other marketing spend strategies

You can use the model to understand the hypothetical impact of decreasing your investment in underperforming marketing channels and increasing your investment in high-performing channels. For example, if the model shows Google Ads has hit its ceiling while email keeps earning on a small budget, trim Google spending and instead put that money into growing your email list. 

Let’s say you make that Google-to-email switch in January. Leave the budget alone for a quarter. Your total spending never changed, so if revenue comes in higher by April, the switch worked. Feed the new months into the model and run it again, now with the sales data results of your real experiment for more accurate modeling.