2.0 Overcoming marketing complexity with marketing mix modeling

table of contents
introduction
1. Evolution of Marketing Mix Modeling (MMM)
2. 2.0 Current Scenario of Marketing Mix Modeling
3. 2.0 The future of marketing mix modeling
3.1. Increased usage of AI and ML
3.2. Diversifying MMM dimensions across industries
3.3. Real-time MMM
conclusion

introduction
As we move into the digital age, Chief Marketing Officers (CMOs) need to move away from traditional tracking methods (reliant on clicks) and choose better marketing models suited to this era. The solution to this problem is the implementation of what is called MMM (Marketing or Media Mix Modeling), or MMM 2.0.

CMOs need to understand the essence of marketing mix modeling (MMM) before jumping to implementation conclusions. MMM enables data-driven decision-making, effective resource allocation, and better marketing performance measurement.

Marketing mix modeling (MMM) is considered one of the oldest forms of marketing strategy, invented to track and analyze fluctuations in sales performance. This model is an effective guide for strategy and planning and provides a competitive advantage for increasing market responsiveness. This gives CMOs and marketing professionals better customer insights, allowing them to optimize ROI, help manage risk, and support continuous improvement of marketing strategies.

This article describes the evolution of marketing mix modeling, marketing mix modeling best practices, and the future of marketing mix modeling.

1. Evolution of Marketing Mix Modeling (MMM)
Marketing mix modeling is not a buzzword. It has been used in the marketing world for decades as a powerful tool for measuring the effectiveness of marketing campaigns.

In the 1980s, MMM was first introduced into analytics systems by Procter & Gamble (P&G) to measure marketing predictions, conversion rates, and incremental experiences.

In the early days of MMM, this model was very slow and expensive. Marketers performed a manual process that involved extensive data collection and preparation through TV commercials, radio ads, billboards, and more.

However, a number of tools and techniques now help CMOs automate their MMM models, making them faster, more accessible, and more efficient. 2.0 MMM models take into account a variety of factors such as macroeconomic trends, competitive activity, and seasonality to provide a comprehensive view of the overall impact of your marketing efforts.

For example, marketers can strategize their campaigns simply by automating data. This data is available in Google Sheets through the Marketing Mix Model tool.
The output from MMM is divided into three parts.

As a result, MMM is gaining popularity in this digitalized business world, and its use cases can help other industries build omnichannel marketing.

2. 2.0 Current Scenario of Marketing Mix Modeling

After a leap of 30 years, the current scenario of 2.0 Marketing Mix Modeling is built on the foundation of machine learning, artificial intelligence, predictive analytics, and the combination of sound business knowledge and external factors of the business world.
CMOs can simplify marketing by applying “smart constraints” and assumptions that can find real-world applications for implementing MMM 2.0 in this digital age.

For marketing advertisers, the use of 2.0 will lead to a “rapid update process” that captures reactions to constant changes in variable relationships and recalibrated curves to understand the latest trends in the market and create robust marketing strategies. will help with the transition.

MMM best practices are essential for B2B businesses looking to hold space in this day and age, but CMOs need a deeper understanding of KPIs like site traffic, consumer engagement measurement, and search query volume. These metrics will become more valuable to marketers who want to collect pent-up demand and convert customer demand into better her ROI and customer experience.

Testing your MMM model is essential to experiment with new creatives, audiences, channel strategy changes, touchpoints, and customer location-level targeting. Testing your feed with MMM ensures consistency and updates the model to reflect new media consumption and purchasing habits. It provides an opportunity to quickly explore and implement new ideas for specific campaigns targeting key customer segments.

In the long run, the MMM model requires a unified marketing measure that helps optimize marketing campaigns through real-time optimization of marketing investments and customer targeting. This approach leverages first-party and third-party data sources to further drive an omnichannel business perspective.

3. 2.0 The future of marketing mix modeling

Compared to traditional MMM, 2.0 MMM opens the door to new technologies and reduces manual workload for marketers by automating data collection, cleansing, and preparation processes. Marketing executives believe that MMM will continue to evolve and influence the next generation of MMM. Let's check out some of the MMM trends we can expect to see in the coming years.