See how marketing mix modeling works, why it matters, and how to set it up to get better insights.
Marketing mix modeling is making a comeback in the world of digital marketing.
As tracking becomes less reliable and third-party cookies fade out, marketers are finding it increasingly difficult to measure the effectiveness of their marketing campaigns.
MMM has been rediscovered as a way to overcome these challenges by providing marketers with a more holistic view of their marketing performance.
There’s no doubt that MMM will play a key role in the future of marketing measurement. If you’re a marketer, now is the time to start learning about and adopting this technology—and this article will help you get a head start.
Here’s what you’ll learn:
- What is marketing mix modeling
- How does marketing modeling work
- Why is MMM gaining popularity
- Marketing mix modeling vs marketing attribution
- How to get started with MMM
💡 Pro Tip
Chances are you’re here because you’ve considered using marketing mix modeling. If so, why not give Ruler Analytics a look? Ruler takes the best of marketing mix modeling and multi-touch attribution to give you the bigger picture and help you understand the factors that affect your sales, deals and ROI.
Book a demo to see Ruler’s MMM in action
What is marketing mix modeling?
Marketing mix modeling (MMM) is a statistical tool that helps companies understand how each part of their marketing impacts customer behaviour, sales, and ROI.
The term “marketing mix” was first introduced back in the 1960s, and MMM as an in-depth analytical method took off in the 1980s and 90s.
Lately, MMM has seen a revival in the digital world as marketers navigate issues like Apple’s privacy updates, walled-off data, and the decline of third-party cookies.
MMM lets you break down your performance by channel, so you can see which marketing tactics are really moving the needle.
It brings in various types of data to evaluate the effectiveness of your marketing. Some of the core data it considers includes:
- Sales data. Historical sales data e.g. unit sales, revenue, and market share.
- Marketing spend data. The amount of marketing budget spent on various marketing activities, such as advertising, promotions, and sales support.
- Market and consumer data. Data on consumer demographics, attitudes, and behaviours, as well as data on market trends and conditions.
- Product data. Product features, pricing, and distribution.
- Economic data. Data on economic indicators, such as interest rates, inflation, and consumer confidence.
- Competitor data. Information on competitor marketing activities, such as advertising spend and promotions.
Let’s take Ruler’s marketing mix modelling, for example.
Ruler’s MMM uses the data sources listed above to show the impact of different marketing touchpoints and identifies the point at which additional investment in a marketing channel will no longer result in a proportional increase in sales or revenue.
While it may seem like doubling your PPC ad spend from £0.6m to £1.2m would double your conversions, in reality, your returns will likely diminish as you increase your ad spend.
This is because there is a point at which additional investment in PPC will no longer result in a proportional increase in conversions.
Diminishing returns is just one benefit of MMM too. Other benefits include the ability to:
- get a better understanding of your target audience.
- improve your understanding of the customer journey.
- better understand the role your invisible touchpoints play in the buyer’s journey e.g. ad, radio and TV views
More on Ruler shortly, first let’s take a deeper dive into how the concept of MMM works…
💡 Skip ahead to the demo
If you’re ready to learn how Ruler can help you improve your marketing ROI, book a demo and see for yourself all the good stuff we have to offer.
Chat with us and see Ruler in action
How does marketing mix modeling work?
Marketing mix modeling (MMM) uses multiple linear regression (MLR) to analyse the relationship between business results (like sales) and different marketing activities.
In MLR, you’re looking at how one or more independent variables (like advertising spend or price) impact a dependent variable (like sales or revenue). This helps estimate how changes in marketing tactics could affect future business outcomes.
In MMM, the dependent variable is usually a business metric we care about, like:
- Sales volume – to see how different marketing activities influence sales.
- Revenue – to track how much money is being generated by sales.
- Market share – to understand the effect of marketing on your position in the market.
Independent variables in MMM are the marketing activities or factors that might drive those results, such as:
- Advertising spend – how much is invested in promotion across various channels.
- Price – to explore how price adjustments impact sales.
- Promotions – discounts, coupons, or offers that might boost sales.
- Distribution – product availability across different locations, which can influence sales.
While MLR is commonly used, other methods like time-series analysis, logistic regression, or even machine learning can be applied, depending on the needs of the analysis.
Why is marketing mix modeling gaining popularity?
By now, you might be wondering why marketing mix modeling is even worth your time.
After all, you likely already have tracking set up—whether through Google Analytics or metrics from your ad platforms. But, there are several important reasons to consider it.
Complex customer journeys
Gone are the days when businesses could rely on a single marketing channel to drive growth.
Today’s customer journey is anything but straightforward—people are browsing ads on Meta, searching for solutions on Google, and diving into product reviews on TikTok.
Marketing across these different channels is crucial, but stitching those journeys together to understand the impact of each touchpoint is a big challenge.
Many traditional tools, like Google Analytics, are click-based, use first-party cookies, and have limited lookback windows, which falls short for mapping out complex customer journeys over time.
Limited tracking
On top of the already challenging journey mapping, tracking data has gotten trickier, especially on Safari, thanks to Apple’s Intelligent Tracking Prevention (ITP).
With ITP, first-party cookies now only last 1-7 days, depending on user settings.
Combine this with Apple’s App Tracking Transparency (ATT) rules, which force developers to ask permission before tracking activity across apps and sites, and it’s clear that the ways we used to measure marketing success are getting muddied.
To make things tougher, Google Analytics doesn’t capture essential data like media channel performance, ad impressions, or ad spend’s impact on revenue—making it harder to get a clear view of what’s actually working.
Rise of privacy-conscious users
Of course, privacy isn’t just about policies and browser settings.
People are actively taking control, using tools like ad blockers to stop tracking in its tracks. Ad blockers don’t just keep annoying ads at bay; they also limit the data companies can collect on user behaviour.
Combined with regulations like GDPR and CCPA, these trends mean marketers have less data and less clarity on their campaigns. For businesses, it’s a whole new landscape that requires creativity and adaptability to measure success effectively.
Marketing mix modeling sets out to combat these issues and helps tackle the tricky parts of tracking by showing marketers what’s working at every stage of the funnel. It makes sure the right channels get credit and budgets are spread out fairly.
Marketing mix modeling vs marketing attribution: What’s the difference?
Marketing mix modeling (MMM) and marketing attribution are often compared but are actually quite different. They both evaluate marketing’s impact, but their approaches vary a lot.
Marketing attribution
Marketing attribution software assigns credit for sales or conversions to the marketing touchpoints that influenced the customer’s journey—ads, emails, site visits, etc. Multi-touch attribution (MTA) specifically spreads credit across multiple touchpoints to highlight which interactions were most effective.
Pros of Marketing Attribution
- Fast to implement, ideal for quick decision-making.
- Provides quick feedback on individual channel performance.
Marketing mix modeling
We’ve already covered MMM and how it works, but MMM is a statistical analysis of how various marketing activities (both online and offline) impact overall sales and business metrics.
But there’s one thing we haven’t yet pointed out.
MMM uses a broader approach to marketing measurement and often incorporates marketing attribution as one of the inputs in its analysis. So you can think of marketing attribution as a subset of MMM.
Marketing attribution information can be used in MMM to better understand the effectiveness of different marketing channels and how they work together to drive sales.
Pros of MMM
- Quantifies everything, even seasonal or behavioural changes.
- Measures offline channels like TV, radio, and print.
- Statistically reliable and privacy-safe, great for the post-cookie world.
- Provides insights into long-term impact, useful for planning.
How to get started with marketing mix modeling
To get started with MMM, you need to gather and organise your data, decide which models to use, and set up the system.
These steps typically include:
- Gathering data. To start with MMM, you need to collect data from your marketing and sales tools. This includes data on your marketing activities (e.g. media spending, ad campaigns), as well as business outcomes (e.g. sales, ROI, etc.)
- Setting up a MMM system. Once you have the data, you’ll need to set up the modeling system. This involves choosing the models you want to use, deciding on the metrics to track, and setting up the software.
- Analyse the data. This generally involves looking at the relationships between your marketing and business data and discovering insights about how your marketing activities impact your business outcomes.
- Adjust and optimise. Finally, use the insights from your analysis to adjust and optimise your marketing mix and maximise your return on investment (ROI).
These steps sound simple, but in practice, it’s quite difficult to set up successfully, especially if you’re new to the principle of marketing mix modeling.
Another option is to invest in an attribution and MMM solution. It’s less complicated and often more cost-effective than having to hire a statistician to build a marketing mix modeling system from scratch.
Take Ruler Analytics, for example.
Ruler takes the best of multi-touch attribution and marketing mix modeling to help you better understand your marketing performance and identify areas to prioritise.
It lets you see how all of your channels (online and offline) are performing and how well they can be attributed to conversions and sales.
How does Ruler’s attribution and marketing mix modeling work?
Ruler starts by tracking the entire customer journey at the visitor level on a first-party basis.
It captures the marketing source from each session, page views, UTM variables, Click IDs, and Cookie IDs which are then matched to a lead driven from a form, phone call or live chat.
Ruler passes the marketing source data you’ve captured on your leads to your CRM and other marketing tools. This allows you to enrich your leads and opportunities with attribution data so you can see exactly how your marketing impacts pipeline generation.
When a lead is marked as closed as won, the revenue data is sent back to Ruler. With revenue and opportunity data in Ruler, you can easily measure and validate the impact of your marketing sources, campaigns and keywords.
Now, let’s talk about its MMM.
Ruler uses marketing mix modeling alongside its multi-touch attribution to give you the bigger picture and help you understand the factors that affect your sales and ROI.
Here’s a breakdown of the five key features in Ruler’s MMM:
- Impression Attribution Modeling: Ruler’s impression attribution helps you track those hard-to-see touchpoints, like when people view an ad but don’t click, then later go to your website and convert. With this feature, you’ll get insights into how, when, and where people interact with your ads and offline marketing, like TV or radio spots.
- Diminishing Returns: We’ve mentioned this before, but Ruler uses diminishing return curves to gauge how your ROI changes over time. This way, it can show you how much potential remains in each of your ad channels.
- Budget Optimiser: Ruler’s budget optimizer makes your ad spend more efficient. It reallocates funds from channels where returns are levelling off (like Google search) to those with more growth potential (say, Meta ads).
Predicted Upside: The budget optimiser also shows the impact of these adjustments on your revenue. For example, a reallocated budget might add £1.3 million in sales, bumping your ROI from £1.9 million to £2.1 million.
Book a demo of Ruler to see it in action for yourself here.
Need help getting started with marketing mix modeling?
With marketing mix modeling set up successfully, you’ll have data linking your sales to your marketing efforts.
You’ll be able to see a clear picture of your marketing wins (and losses), so you can improve and optimise your marketing strategy for maximum results.
If you’re looking for a marketing reporting system that unifies data across online and offline channels and links your conversions directly to revenue, then Ruler is for you.
It allows you to access revenue data and link it directly to your marketing activities without any of the headache of doing it yourself.
