The Next Generation of Marketing Mix Modeling: Innovations for Enhanced Performance Measurement

• Marketing Analytics

In today’s ever-evolving marketing landscape, businesses face the challenge of accurately measuring the impact of their marketing activities and optimizing their budgets for maximum return on investment (ROI). Traditional marketing mix modeling (MMM) has long been used as a statistical analysis technique to assess the effectiveness of various marketing inputs on desired business outcomes such as sales, conversions, and installs. However, with the emergence of new media channels, privacy concerns, and the need for real-time insights, the industry is witnessing a rise in next-gen MMM solutions that offer enhanced capabilities and efficiency.

The Evolution of Marketing Mix Modeling

Marketing mix modeling has undergone significant transformations over the years, adapting to technological changes, data availability, and business requirements. Initially, MMM was primarily managed globally and executed locally, often outsourced to consulting firms. However, as the need for more frequent and granular insights grew, organizations began exploring options for in-house MMM capabilities. This shift gave businesses greater control over their data and tailored the modeling process to their needs.

Despite these advancements, traditional MMM faced several challenges that necessitated further innovation. The emergence of new media channels, such as retail e-commerce, posed a challenge for traditional MMM methods, which struggled to incorporate these dynamic channels into the analysis effectively.

Businesses wanted to understand the impact of spending on new media and channels on their overall marketing performance.

Additionally, the desire for higher granularity and frequency of data availability fueled the demand for MMM at a faster pace.

Another crucial aspect that traditional MMM needed to address was quantifying the long-term impact of media channels on brand equity. Businesses wanted to measure the ROI of each media channel and understand its contribution to building brand equity over time. Furthermore, media attribution towards specific consumer segments became a priority, enabling marketers to tailor their strategies and messaging to different target audiences.

The Next Generation MMM: Innovations and Solutions

To address these challenges and meet the evolving needs of businesses, the next generation of MMM has emerged, incorporating new analytical methodologies and leveraging technological advancements. These innovations aim to drive scalability, accessibility, and actionable insights for cross-functional teams & not just marketers.

We at Course5 have leveraged these to drive impact at scale across our client organizations.

1. Speed and Scalability with AI

One of the primary goals of the next-generation MMM is to deliver insights at a faster pace without compromising accuracy. By leveraging the power of artificial intelligence (AI) and advanced modeling techniques, MMM solutions can now provide results within weeks or even days. By automating data collection, analysis, and reporting processes, decision-makers can access near-real-time insights and make data-driven decisions more efficiently.

2. Democratization of Insights

The next-generation MMM solutions focus on democratizing insights by empowering cross-functional teams to utilize the findings from MMM. With the onset of DIY custom modules and natural language processing (NLP), insights generated from MMM can be shared with teams across the organization. This democratization of insights enables marketers, brand teams, and other stakeholders to access and leverage MMM insights for their decision-making processes.

3. Measuring Long-Term Impact and Brand Equity

Next-generation MMM aims to measure the long-term impact of media channels on brand equity by connecting media spending, reach, awareness, brand recommendation, intent to purchase, and sales. This interconnected tree structure allows businesses to understand the holistic impact of their marketing efforts, from initial spending to final sales. Advanced modeling approaches, such as latent growth models and graph neural networks (GNNs), help capture the complexity of new media sources, including retail media.

4. New Data sources: Incorporating New Media (RMN)

The third wave of advertising has arrived; retail media is already the 4th largest advertising media. It continues to double up every year and surpasses spending across OOH, Cinema, and Audio, with the spending and integration of customer data platforms (CDPs) to enhance media attribution and investment planning. By connecting CDP data with MMM insights, businesses can better understand consumer behavior and tailor their media strategies accordingly. This integration enables marketers to optimize their media spending and deliver personalized experiences to different customer segments.

5. New Data sources: Consumer Segments – CDP Data within MMM

The next generation of MMM emphasizes the integration of customer data platforms (CDPs) to enhance media attribution and investment planning. By connecting CDP data with MMM insights, businesses can gain a deeper understanding of better understand consumer behavior and tailor their media strategies accordingly. This integration enables marketers to optimize their media spending and deliver personalized experiences to different customer segments.

6. Privacy and Compliance

Privacy and compliance are paramount business concerns in today’s data privacy-focused landscape. Next-generation MMM solutions prioritize privacy-friendly approaches, ensuring data collection and analysis adhere to regulatory requirements. By using aggregated rather than granular user behavior data, MMM solutions can provide valuable insights while safeguarding user privacy.

7. Gamification and Simulations

Next-generation MMM solutions incorporate gamification elements and simulations to enhance the decision-making process. Marketers can simulate various scenarios and assess the impact of different marketing strategies on their outcomes. These simulations help businesses make informed decisions, optimize budgets, and maximize marketing ROI.

The Future of MMM: Harnessing the Power of AI

As the industry continues to evolve, the role of AI in MMM is expected to grow. AI-powered MMM solutions have the potential to revolutionize performance measurement by providing real-time, scalable, and accurate insights. With AI modules and technologies, businesses can drive MMM at Scale, make insights accessible to cross-functional teams, and further refine their marketing strategies.

Here are key points that business & client organizations need to consider while driving the MMM journey.

1. Ensuring setting up the proper scope for measurement.
2. Align business questions – what can be answered via MMM and what cannot.
3. Review data & hypotheses to ensure alignment with cross-functional teams.
4. MMM is not magical – believe in the process and try recommendations.

   

5. Continuous process vs. just executing MMM one time or Once a year, setting up a validation process.
6. Setup a unified system to track & store data.
7. Set up a Benchmark database of ROI and elasticity by brand/channel for future ref.

In conclusion, the next generation of MMM offers innovative solutions to address the challenges faced by traditional MMM methods. Through advancements in AI, data integration, privacy compliance, and faster analytics processes, marketers can harness the power of MMM to optimize their marketing activities, understand the long-term impact of media channels, and enhance their overall marketing performance. By embracing these innovations, businesses can stay ahead of the competition and achieve greater success in the dynamic world of marketing.