9 Marketing Mix Modelling Tools to Try in 2024

For marketers exploring attribution courses, accurately measuring the impact of broadcast campaigns is essential. It requires an integrated approach that combines traditional methods and modern analytics to empower businesses in making informed decisions, optimizing campaigns, and maximizing ROAS (return on ad spend). Key metrics like conversion rate, customer acquisition cost, and customer lifetime value offer deep insights into campaign performance.

With stricter privacy rules limiting data tracking, digital marketers are turning back to marketing mix modelling tools and software to ensure clear visibility into marketing effectiveness.

With third-party cookies going away, browsers blocking trackers, and privacy laws multiplying, marketers are struggling to track users and measure campaign success.

Traditional tracking is crumbling, leaving marketers in a quandary.

They need new ways to navigate these murky waters, and marketing mix modelling presents itself as the answer.

Even tech giants like Google and Meta are recognizing its potential.

In this article, we delve deep into the realm of marketing mix modelling, exploring the latest tools, software, and methodologies that are shaping the world of marketing measurement.

Here’s what we’ll cover:

  • What is marketing mix modelling?
  • Why is MMM growing in popularity?
  • The best marketing mix modelling software

💡 Pro Tip: Browser restrictions and privacy regulations are getting tighter, but that doesn’t mean marketing measurement has to suffer. We’re leading the way with innovative cookieless solutions like marketing mix modelling to ensure you get the insights you need without compromising privacy.

Skip to learn more about Ruler’s MMM or book a demo to see it in action.

What is marketing mix modelling?

Marketing mix modelling (MMM) is a powerful statistical analysis tool that acts like a control panel for your marketing.

It analyses a bunch of variables giving you a clear picture of how each channel impacts your bottom line.

These variables typically include:

  • Historical sales and revenue data
  • The amount of marketing budget spent on various marketing activities
  • Data on consumer demographics, attitudes, and behaviors
  • Product features, pricing, and distribution
  • Data on economic indicators
  • Information on competitor activity

By using techniques like multivariate regression, MMM goes beyond just showing you what’s being spent—it reveals the interrelationships between your marketing efforts.

This allows you to see which channels are truly driving sales and which ones might be underperforming.

With these insights, you can optimize your marketing budget, allocate resources more effectively, and maximize your return on investment.

More importantly, MMM helps you predict the impact of future campaigns, allowing you to adjust the dials for optimal sales performance.

Why is MMM growing in popularity?

Marketing mix modelling might have its roots in the 1960s, but it’s experiencing a modern resurgence due to the complexities of today’s digital marketing landscape. Let’s break it down:

Increasingly complex customer journeys: The days of relying on a single marketing channel for growth are over. A whopping 73% of consumers juggle multiple channels throughout their shopping journey, according to Harvard Business Review. This multi-channel reality leaves businesses overwhelmed by the sheer volume of data and struggling to understand which channels truly drive sales. As campaigns balloon in complexity, the challenge of discerning each channel’s contribution becomes even more daunting. By leveraging advanced analytics, MMM cuts through the clutter, revealing which channels deliver the biggest bang for your marketing buck and helping you unlock hidden synergies to optimize your marketing strategy for success.

Browser restrictions and stricter privacy regulations: Major web browsers like Safari and Firefox have already implemented measures to block third-party cookies, with Google Chrome set to follow suit by the end of 2024. This shift towards user privacy necessitates a focus on first-party data collected directly from visitors. But even first-party cookies face limitations. Apple’s Intelligent Tracking Prevention (ITP) notably reduces the lifespan of first-party cookies in Safari, allowing them to be stored for only 1-7 days, depending on specific settings. Consequently, users returning to a website after 24 hours are treated as new visitors, complicating the tracking of user behavior over time. Further complicating matters, regulatory frameworks such as GDPR and CCPA are heightening risks associated with cookie usage, with nations like Denmark, Belgium, and Germany adopting stringent legislation like the Telecommunications Telemedia Data Act. These laws extend beyond third-party cookies, prohibiting the utilization of any cookie without explicit user consent. These challenges, compounded by Apple’s App Tracking Transparency (ATT) requiring developers to seek user permission before tracking activity across third-party apps and websites, have muddied the waters of marketing measurement. Marketing mix modelling shines as a beacon of hope, bypassing the need for cookies and ensuring compliance with privacy regulations.

💡 Pro Tip: Even though browser limitations and privacy concerns have made tracking more difficult, user identification through deterministic methods remains valuable. Techniques like marketing mix modelling are used to effectively address the data gaps. See how we’re blending deterministic tracking with probabilistic modelling to achieve a comprehensive understanding of what works and what doesn’t.

Rise of privacy-conscious users: While restrictions on cookies have been a hot topic, a bigger challenge for marketers is the growing number of privacy-conscious users. People are actively taking steps to block trackers and avoid being followed online. Ad blockers are another tool in the user’s privacy kit. They not only stop annoying ads but also make it harder for companies to collect data on users. When you combine these trends with stricter browser settings and regulations like GDPR and CCPA, marketers have less data to track users and measure the success of their campaigns.

The best marketing mix modelling tools and software

Given the complexities of tracking marketing impact across various channels, robust marketing mix modelling solutions are essential.

Below we’ve curated a list of the most innovative MMM tools for 2024 and beyond, perfect for both MMM newbies and those looking to boost their existing setup.

Let’s dive in.

Ruler Analytics: Ruler starts by tracking user journeys from the moment they visit your website. It does this anonymously at first but can connect user journeys to specific leads generated through forms, calls, or chats. Ruler collects data on where your visitors came from and what pages they viewed through first-party cookies and unique identifiers. This data is then linked to your CRM and other marketing tools. By enriching your leads with this attribution data, you can see the exact sequence of touchpoints that led to your highest-value leads and customers.

Cassandra: Cassandra uses cutting-edge machine learning to analyze all your data and build a custom media plan that continuously optimizes your return on investment. Unlike other MMM tools that can take a long time to set up, Cassandra claims it can get you started in just 3 weeks. Plus, it helps you test different marketing mixes in the real world to see what works best.

Google’s Meridian: Google launched a free tool called Meridian to replace LightweightMMM and track ad performance across all your channels. It’s even better than their previous option, offering more powerful features. Meridian goes beyond just tracking – it analyzes how all your marketing efforts, including ads, work together to boost sales and achieve your goals. This translates to smarter media spending. Meridian even helps you plan your budget across channels more effectively. To make things even better, Google plans to add its own data to the mix. This includes valuable insights like how many people saw your ads (reach) and how often they saw them (frequency) on YouTube, along with search query volume. Meridian’s a beta program right now, so not everyone has access yet. But Google assures us it’ll be widely available soon.

Keen: Keen’s decision system empowers you to forecast future revenue performance across all marketing channels. The platform claims to ensure a 25% enhancement in marketing-influenced revenue by incorporating your data and combining it with over 40 years of research and a decade of metadata. It constructs a robust marketing mix model tailored to propel your business forward, allowing you to unveil prospective revenue streams for your business across varying budget levels and anticipate potential ROI from investments in new channels before initiating testing.

Leavened: Leavened is a marketing measurement technology company that was created by a team of experts in both marketing and analytics. Their platform helps businesses increase their marketing return on investment (ROI) by streamlining the planning, measurement, and optimization processes based on consumer behavior. They address common frustrations in marketing measurement, such as time, cost, and lack of transparency, with their non-cookie technology tools that allow for real-time adjustments.

Maximus: Developed in R, Maximus harnesses machine learning algorithms and statistical methods to delve into historical data, assessing sales contributions and ROI for each activity while forecasting future outcomes. It offers both auto and manual modelling options, supported by a recommendation engine for optimal fits. Users can effortlessly compare models, adjust variables, and categorize them into groups like media, promotion, and seasonality. Results are presented in clear tabulated reports or visual charts, highlighting sales contributions effectively.

Meta’s Robyn: Meta’s Robyn is a free, open-source tool that uses artificial intelligence to unlock the secrets of your marketing data. This powerful software is designed for businesses with extensive advertising data, especially those focused on digital and direct response campaigns. Robyn automates tedious tasks and delivers valuable insights into how your ads perform over time. By analyzing complex datasets, it helps you identify the most effective channels to reach your target audience, ultimately optimizing your marketing campaigns for better results.

Nielsen: Nielsen marketing mix modeling empowers clients to assess their investment impact, identify effective strategies, and adjust budgets accordingly. With sophisticated data models covering comprehensive and detailed information, it provides answers to vital marketing queries, ensuring confident decision-making for present and future campaigns. Thanks to automated systems and seamless integration, clients gain valuable ROI insights in a matter of weeks. Tailored simulations aid in optimizing the marketing mix and facilitating strategic planning in weeks that follow.

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