Marketing Mix Modelling History: Origins To Modern Trends

Introduction

Marketing mix modelling (MMM) began in the 1960s as a statistical method to measure the impact of marketing on sales. Early models used linear regression to analyze TV, print, and radio campaigns. MMM evolved to include digital channels, multi-touch attribution, and predictive analytics. It helps marketers allocate budgets, forecast ROI, and optimize campaigns using historical data. Today, MMM blends econometrics with AI and privacy-safe data practices. The history of MMM reflects a shift from simple media analysis to full-funnel, data-driven decision-making across traditional and digital platforms.

This growing complexity laid the groundwork for marketing mix modelling. Over the years, MMM has transformed from a niche statistical exercise into an essential component of many marketers’ toolkits.

Today, it’s at the heart of multi-channel campaigns, helping organisations optimise their spending, measure ROI, and predict future performance.

But how did we get here? Let’s take a journey through the history of marketing mix modelling, exploring its evolution, its impact on modern marketing, and what it has for the future.

The Origins of Marketing Mix Modelling

MMM originated in the aftermath of World War II, yet its seeds were planted much earlier during the Great Depression of 1929-1939.

The "Executive as a mixer of ingredients" idea was originally introduced by business icon Neil H. Borden in 1949. During the 1950s and 1960s, it was referred to simply as MM, not yet MMM! Later, in the 1960s, Borden admitted that he borrowed the "Marketing Mix" concept from his associate, James Culliton.

During the Great Depression of 1929-39, as advertising's economic role was questioned, Borden was commissioned to analyse its economic impact. This work led to the publication of a behemoth 970-page book, "Economic Effects of Advertising," in 1942.

This research was the beginning of Borden's interest in understanding marketing holistically.

In the 1950s, after introducing the idea of the "Executive as a mixer," the nascent MMM models were introduced. Borden developed a complicated model based on at least twelve factors that never gained popularity.

What is Marketing Mix Modelling?

At its core, marketing mix modelling is a statistical approach that helps marketers understand the impact of various media and marketing channels on business outcomes. By analysing historical data, MMM uncovers patterns in how marketing inputs, such as advertising spend on TV, digital, and print, affect outputs like sales, brand awareness, and customer engagement.

Originally developed to address the complexity of measuring the impact of traditional media, MMM provided a way to disentangle the effects of overlapping campaigns.

According to research by Deloitte Insights in 2023, 53.5 % of US marketers already use MMM, and another 30 % say it’s the best technique for surfacing growth drivers.

Over time, as digital channels emerged and grew, MMM adapted to include a broader range of data inputs. Today, it combines advanced analytics with machine learning techniques to provide marketers with actionable insights.

The Rise of the Four Ps

But the 1960s - the Mad Men era saw the emergence of the classic Four Ps: Product, Price, Promotion, and Place (distribution). E. Jerome McCarthy and Philip Kotler proposed the Four Ps framework in 1960, which has been widely adopted. The Four Ps have become the standard part of marketing.

An enduring concept, the Four Ps framework has evolved into one of the most lasting and widely recognised structures in marketing. Even now, the Four Ps are featured in every standard marketing textbook.

Historically, the Four Ps were initially developed in an environment focusing on consumer packaged goods (CPG), a standard business model in the 1960s. As a result, large consumer packaged goods companies like P&G, AT&T, Kraft, Coca-Cola, and Pepsi have made MMM an integral part of their marketing planning.

Again, the roots of the Four Ps can be found in the 1930s Great Depression. The Four Ps were developed under the influence of the 1930s theory of monopolistic competition, another echo of the Great Depression of 1929.

During this phase, MMM overshadowed most previous techniques:

• Organic Functionalist approach
• System-oriented approaches
• The parameter theory developed by the Copenhagen School
• Earlier approaches: Institutional, Functional, Geography-related Regional all suffered the same fate

The four Ps were considered the “tablet of faith”. But, over time, others tried to expand the Four Ps paradigm by adding other Ps, such as People or Process. There are several variations of the Four Ps: Seven Ps, Four Cs, and more.

The 1970s revolutionised marketing. Before this, measuring marketing impact was guesswork. Statisticians at the University of Chicago created the first statistical models, which quantified the crucial link between marketing activities and sales.

Although effective, these early models were cumbersome, requiring months of expert work, a luxury for large corporations with big budgets.

Relationship Marketing and the Service Industry

With the rise of the service industry in the 70s and 80s, the most significant push was to include "service" in the standard Four Ps. Without service, the critics pointed out, "customer service will be isolated from the rest of the organisation." Customer service must be everyone's concern, they argued. So, we should not isolate ourselves and be siloed in a specific department.

In the 1990s, criticisms of the Four Ps and MMMs took a new turn. The "service industry," which started a decade earlier, had reshaped priorities, emphasising long-term customer relationships over traditional marketing strategies. In this new environment, MMM is not just wrong; it's dangerous and must be avoided at all costs.

This is when customer relationship management (CRM) systems became popular. Focusing on long-term relationships with customers has a higher economic impact than focusing on acquisition. This makes sense, especially if you're in the service sector.

But would the same principle apply to other sectors? The law of double jeopardy, promoted by Byron Sharp, argues that CRMs are not sufficient to drive cross-selling and continued purchases from a brand. It's the size of the brand that determines the frequency of cross-sales.

As we celebrate the new millennium, a crisis exists: The Dot-com bubble is about to explode.

The Digital Revolution

Digital commerce is the future, thanks to the rapid adaptation of the Internet and digital experiences. The digital frontier is rapidly expanding through new platforms like Google, Facebook, Amazon, and eBay, as well as new technologies such as smartphones.

With the digital revolution of the 2000s and 2010s, new marketing channels emerged: Social media, Search, Digital display, Video, etc. In this new globalised economy, powered by the Internet and connectivity, brands can use to reach new audiences.

During the 2010s, marketing technologies were centred around attribution techniques, such as multi-touch attribution (MTA). These techniques use cookies to track users' browsing behaviour across the internet.

But with the latest restrictions and phasing out third-party cookies in major browsers, a new era of MMM has started.

Numerous new startups offer MMM solutions, either as Software as a Service (SaaS) or as custom development for specific clients.

According to the CMO Survey in 2023, digital marketing now commands 53.8% of marketing budgets, showing that MMM models likely need to account for a more digital-heavy mix than ever before.

The new phase of MMM features the return of AI, raising curiosity about how these technologies will integrate in the next few years.

It seems that we're going back to Neil H. Borden's original idea in the 1950s, which was a long list of marketing ingredients. The modern MMM systems have dozens of channels and exogenous factors, such as seasonality and economic conditions.

Today, the legacy of Marketing Mix Models lives on, continually adapting to the ever-changing marketing environment. The MMM of the 2020s is privacy-centric and focuses on holistic measurement across the entire marketing portfolio.

What Does MMM Mean for Marketers Today?

For contemporary marketers, MMM is more than just a measurement tool. It’s a strategic compass. By using MMM, businesses can:

• Optimise Budgets Across Channels

Marketers can figure out which channels are working best and adjust their budgets to focus on those. This helps them get the most value from their ad spend and reach their goals more effectively.

• Measure Incrementality

MMM helps marketers understand the incremental impact of each channel. This means they can determine not just which channels contribute to sales, but how much additional revenue is generated by increasing investment in a specific channel.

According to Forrester’s Marketing Survey, 2023, about 30% of B2C marketers use MMM tools to better understand how marketing drives value for the business.

• Enhance Collaboration Between Teams

By providing a clear, data-backed picture of performance, MMM facilitates better communication between marketing, finance, and executive teams. It helps align everyone around shared goals and ensures that marketing strategies are tied to business outcomes.

• Adapt to a Privacy-First World

As consumer privacy regulations become stricter and third-party cookies fade away, MMM offers a viable alternative to user-level tracking. By relying on aggregated data, MMM allows marketers to measure effectiveness without compromising consumer privacy.

Also read - Marketing carryover effect guide

Marketing Mix Modelling vs. Attribution Modelling

In the marketing analytics realm, Marketing Mix Modelling (MMM) and attribution modelling often appear side-by-side, yet their objectives and methods are quite distinct. To make informed decisions, it’s critical to understand the nuances of each approach.

Marketing Mix Modelling: MMM is a top-down approach that evaluates the overall impact of marketing investments. By analysing historical data at an aggregated level, it measures the combined influence of various channels - both traditional (TV, print) and digital (search, social), on business outcomes like sales or revenue. 

This long-term, big-picture perspective helps identify which media channels are most effective over time, guiding high-level budget allocations and strategy adjustments.

Attribution Modelling: In contrast, attribution modelling focuses on the granular, bottom-up data. It assigns credit to specific touchpoints within the customer journey, such as a display ad click or a social media engagement, highlighting what drove a particular conversion. 

Attribution models are often used to optimise immediate campaign performance by zeroing in on short-term interactions and refining individual tactics. Unlike MMM, attribution modelling generally stays within the realm of digital channels where detailed tracking data is abundant.

Key Differences:

• Scope: MMM is holistic and evaluates multiple channels together; attribution modelling is more tactical and channel-specific.
• Data Granularity: MMM relies on aggregate data, making it better for long-term strategy; attribution modelling uses user-level data, which is ideal for quick, iterative campaign adjustments.
• Purpose: MMM supports macro-level budget planning, while attribution modelling helps refine and optimise day-to-day campaign performance.

Together, these approaches provide complementary insights, empowering marketers to balance strategic foresight with immediate, actionable optimisation.

Recent Evolution of MMM

The landscape of Marketing Mix Modelling has transformed significantly since its inception. Originally grounded in basic regression analyses and limited datasets, MMM now incorporates advanced methodologies and cutting-edge technology, broadening its scope and improving its accuracy. Here are the recent advances in MMM:

• Incorporation of Digital Channels

Earlier models were primarily built around traditional media, but modern MMM frameworks now seamlessly integrate digital channels, such as programmatic advertising, paid social, and influencer campaigns. This integration provides a more comprehensive view of the media ecosystem and allows for more precise measurement of cross-channel impacts.

• Advanced Statistical Techniques

The introduction of Bayesian modelling and machine learning algorithms has enabled marketers to account for complex dynamics like diminishing returns, channel interactions, and non-linear trends. These improvements have resulted in improved insights and more reliable ROI estimations.

• Real-Time Data and Agility

Modern MMM is no longer limited to quarterly or annual assessments. By leveraging real-time data feeds, marketers can make more agile adjustments, optimising campaigns on a rolling basis and responding to changes in consumer behaviour or market conditions.

• Privacy and Compliance

As data privacy regulations have tightened, MMM has adapted by relying on aggregated data rather than individual user identifiers. This approach not only ensures compliance but also solidifies MMM as a future-proof methodology.

• Democratisation and Accessibility

With cloud-based platforms and open-source tools, MMM is no longer exclusive to large enterprises with deep pockets. Businesses of all sizes can now access these sophisticated models, levelling the playing field and fostering widespread adoption.

These advancements have significantly elevated the role of MMM in the marketing toolkit. By adopting modern methods, marketers can better understand their media investments, confidently shift budgets to high-performing channels, and remain competitive in a rapidly evolving advertising landscape.

Also read - Comprehensive Checklist for Enterprise MMM Implementation

MMM’s Role in Revenue Growth Analytics: A Digital Evolution

In the digital age, Marketing Mix Modelling (MMM) has evolved from a traditional ROI measurement tool into a core component of Revenue Growth Management (RGM) strategies. Historically focused on TV, print, and radio, today’s MMM integrates digital signals like clicks, impressions, and conversions alongside offline spend to offer a unified view of marketing impact.

As marketing grows more fragmented and consumer journeys become more complex, MMM helps leaders:

• Quantify the incremental revenue driven by each channel - digital or traditional.
• Pinpoint high-ROI levers (e.g., paid search vs. TV vs. promotions) that fuel sustainable growth.
• Guide spend allocation based on performance, seasonality, and market shifts.
• Align with finance and sales teams on growth forecasting and margin impact.

Modern MMM, powered by cloud computing and machine learning, now supports faster refresh cycles, scenario planning, and even near-real-time optimisation, and makes it an indispensable part of revenue growth analytics in the digital era.

Future Trends in Marketing Mix Modelling

As we look to the future, several trends are set to shape the evolution of MMM:

• Integration with First-Party Data: With the decline of third-party data sources, businesses are investing in their own data ecosystems. MMM will increasingly incorporate first-party data from CRM systems, loyalty programs, and direct-to-consumer sales, providing a richer understanding of customer behaviour.
• Cross-Device and Cross-Platform Measurement: The modern consumer journey spans multiple devices and platforms. Future MMM frameworks will continue to evolve to account for these complexities, offering marketers a more holistic view of how their campaigns resonate across different touchpoints.
• Increased Automation and Real-Time Reporting: Advances in technology are making it possible to automate MMM processes, delivering insights faster and more frequently. Marketers will no longer have to wait weeks or months for results. They’ll be able to adjust campaigns on the fly based on real-time data.
• Greater Accessibility for Small Businesses: Historically, MMM was the domain of large enterprises with extensive budgets and data science resources. Today, open-source tools, cloud-based platforms, and AI-powered solutions are democratising MMM, making it accessible to businesses of all sizes.

Key Takeaways

• Marketing Mix Modelling has evolved from simple regression analyses to sophisticated, machine-learning-powered frameworks.
• It enables marketers to measure ROI, allocate budgets more effectively, and understand the incremental impact of their campaigns.
• The future of MMM lies in integrating first-party data, embracing cross-platform measurement, and leveraging automation for real-time insights.
• As privacy regulations tighten, MMM offers a privacy-safe alternative to user-level tracking, ensuring compliance without sacrificing performance insights.

Conclusion

Marketing Mix Modelling has come a long way since its early days. What started as a basic statistical tool has grown into a powerful, technology-driven approach that helps marketers navigate an increasingly complex landscape.

By understanding its history, leveraging its capabilities today, and preparing for future innovations, businesses can ensure they remain competitive and data-driven in their marketing efforts.

So, the next time you plan your marketing strategy, consider how the history and ongoing evolution of Marketing Mix Modelling can guide your decisions and lead to better results.

The history of Marketing Mix Models is a story of resilience and adaptation. From its origins in the 1950s to the digital revolution of the 2000s, MMM has evolved to meet the changing needs of marketers. Today, MMM is more important than ever, as brands seek to understand the impact of their marketing efforts across a wide range of channels.

As we look to the future, it will be fascinating to see how MMM continues to evolve in response to new technologies and consumer behaviour.

If you're interested in learning more about Marketing Mix Models and how they can help your business, ELIYA can help. Contact us today to learn more about our MMM solutions and how they can benefit your brand.

FAQs

1. What is the history of Marketing Mix Modelling (MMM)?

MMM originated in the mid-20th century to measure the effectiveness of traditional media like TV, radio, and print. It helped marketers identify which channels delivered the best ROI. The goal was to improve budget allocation and drive revenue growth through data-backed decisions.

2. When did Marketing Mix Modelling first emerge?

MMM emerged in the 1960s when companies began using regression analysis to connect marketing spend with sales outcomes. It offered a more scientific, quantifiable way to evaluate campaigns. This marked a shift from intuition-driven to evidence-based marketing.

3. How has MMM evolved over the decades?

MMM now accounts for digital channels like search and social media alongside traditional ones. It leverages machine learning, Bayesian models, and automation for deeper insights. Open-source tools and cloud tech have made it scalable and more accessible to all brands.

4. Who were the pioneers in Marketing Mix Modelling?

Early MMM was driven by statisticians and economists working with big CPG brands. They applied econometric models to sales and media spend data. Their work laid the foundation for today’s marketing analytics and measurement standards.

5. How did the digital revolution change MMM?

Digital channels introduced granular data like impressions, clicks, and conversions into the modelling process. MMM adapted with faster refresh cycles and more real-time analysis. It also began integrating with multi-touch attribution to provide a complete view of marketing impact.