Table of Contents
Introduction
1. The Evolution of Marketing Mix Modeling (MMM)
2. The Present Scenario of 2.0 Marketing Mix Modeling
3. The Future of 2.0 Marketing Mix Modeling
3.1. Increased Usage of AI and ML
3.2. Diversifying MMM’s Dimensions Across Industries
3.3. Real-time MMM
Conclusion
Introduction
As we transition to a digital era, the chief marketing officers (CMOs) need to move away from the traditional method of tracking (which relies on clicks) and choose a better marketing model that fits well in this era. The solution to this problem is the implementation of MMM (Marketing or Media Mix Modeling) or rather, what we will call -MMM 2.0.
Before jumping to the conclusion of implementation, CMOs need to understand the essentiality of marketing mix modeling (MMM), which enables data-driven decision-making, effective resource allocation, and better marketing performance measures.
Marketing mix modeling (MMM) is considered one of the oldest forms of marketing strategy that was invented to track and analyze fluctuations in sales performance. This model is an effective guide for strategies, and plans, and provides a competitive advantage to enhance market responsiveness. This helps the CMOs and marketing professionals gain better customer insight so that they can optimize ROI, assist risk management, and support continuous improvement in marketing strategies.
This article discusses the evolution of marketing mix modeling, the best practices for marketing mix modeling, and the future of marketing mix modeling.
1. The Evolution of Marketing Mix Modeling (MMM)
Marketing mix modeling isn’t a buzzword. It has been around in the marketing world for decades as a powerful tool for measuring the effectiveness of marketing campaigns.
Back in the 1980s, MMM was first deployed by Procter & Gamble (P&G) into their analytics system to measure their marketing forecasts, conversion rates, and incremental experiences.
In the early days of MMM, this model was quite slow and expensive. Marketers manually conducted the process involving extensive data collection and preparation through TV commercials, radio ads, and billboards.
However, today, numerous tools and techniques help CMOs automate their MMM models, which are faster, more accessible, and more efficient. The 2.0 MMM model takes into account various factors, such as macroeconomic trends, competitive activity, and seasonality, to provide a holistic view of the overall impact of marketing activities.
For example, marketers can start strategizing their campaigns just by automating their data, which is available in Google Sheets through marketing mix model tools.
The outputs from MMM are divided into three parts:
| Scorekeeper | The first part is MMM as a scorekeeper that shows the overall increments impacting the marketing investments in the business. |
| Forecaster | The second part is as a forecaster, as it predicts the outcomes that lower and raise the marketing budgets that often contribute to the overall budget. |
| Coach | The last part is a coach, as it suggests a shift in the current marketing scenarios that improve performance. |
As a result, the MMM has become more popular in this digitized business world, and its use cases help other industries create omnichannel marketing.
2. The Present Scenario of 2.0 Marketing Mix Modeling
After a leap of thirty decades, the current scenario of 2.0 marketing mix modeling is built on the foundation of machine learning, artificial intelligence, predictive analytics, and a mixture of correct business knowledge and external factors in the business world.
CMOs can simplify their marketing by applying “smart constraints” and assumptions that will enable them to find the real application areas of implementing MMM 2.0 in this digital era.
For marketing advertisers, the use of 2.0 will help in moving to the “Rapid Refresh Process” to capture the constant change in variable relationships and response to the recalibrated curves to understand the latest trends in the market and create a robust marketing strategy.
While the best practices of MMM are essential for B2B businesses that are looking to make a space in this era, CMOs need to get a better understanding of the KPIs, such as site traffic, measure consumer engagements, and search query volumes; these metrics will become more valuable for marketers who are looking to harvest pent-up demands and convert the customers demands into better ROI and customer experience.
Testing the MMM model is essential to experiment with new creatives, audiences, changes in channel strategies, touchpoints, and geo-level targeting of customers. Testing the feed in MMM ensures consistency and gives an update in models to reflect new media consumption and purchasing habits. It provides the opportunity to explore and implement new ideas quickly on specific campaigns targeting key customer segments.
In the long run, the MMM models need to have a unified marketing measure that will help in optimizing the marketing campaigns through real-time optimization of marketing investments and targeting customers. This approach further drives the omnichannel business perspective with the help of first- and third-party data sources.
3. The Future of 2.0 Marketing Mix Modeling
When compared with the traditional MMM, the 2.0 MMM has unlocked doors for new technologies and it eases the manual workload of marketers by automating the data collection, cleansing, and preparation process. It is believed by marketing stalwarts that MMM will continue to evolve and influence the next generation of MMM. Let’s check out some of the MMM trends that we can expect in the coming years:
3.1. Increased Usage of AI and ML
Multinational companies and large organizations are advancing their models by using LLMs (Large Language Models), such as Google Gemini and ChatGpt. As we expect these models to mature, they will play a prominent role in improving actionability and accuracy in MMM results.
3.2. Diversifying MMM’s Dimensions Across Industries
With an increase in use cases, MMM will no longer be limited to the retail or marketing industry. This model has the potential to explore numerous possibilities in a wide range of industries, such as IT, fintech, and healthcare, by enabling third-party cookies to easily track users’ adoption of low-cost attribution models as a touchpoint on the customer journey by providing them with the best solutions.
3.3. Real-time MMM
The implementation of AI and ML software in the MMM model can be further developed and tested to interpret and translate the data into actionable insights, identify trends and patterns, highlight areas where marketing budgets can be allocated, and provide real-time decisions and recommendations for CMOs to optimize their marketing campaigns.
Conclusion
2.0 marketing mix modeling has become an advanced avenue for marketers and CMOs to measure unique capabilities such as shifting focus to incremental measurement, quantifying internal and external impacts, incorporating offline and online conversion, and estimating multi-touch attributions.
Embracing the power of MMM is not just a mere tool; it is a future guide for multichannel retailing.
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