Introduction to Marketing Mix Modelling (MMM) Part I

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Overview

In the foundation concept of marketing, there are 4 pillars or leaves which marketers will control primarily. Known also as the 4Ps, it has been defined as the “set of marketing tools that the firm uses to pursue its marketing objectives in the target market”. The 4Ps are: product, price, promotion, and place.

The 4Ps of Marketing

Marketing mix modeling (MMM) is statistical analysis such as multivariate regressions on sales and marketing time series data to estimate the impact of various marketing tactics (marketing mix) on sales and then forecast the impact of future sets of tactics.

Marketing mix modeling looks at the historical relationships between marketing spending and business performance in order to help you determine your business drivers and how much you should spend — along with the best allocation across products, markets, and marketing programmes.

Objectives

The MMM enables marketers to identify campaigns that could bring in higher revenue, decrease marketing spend and help to better target the campaigns.

Effective marketing can therefore be defined as having the right product at the right time at the right place and available at the right price.

The purpose of using MMM is to understand how much each marketing input contributes to sales, and how much to spend on each marketing input.

MMM helps in the ascertaining the effectiveness of each marketing input in terms of Return on Investment. In other words, a marketing input with higher return on Investment (ROI) is more effective as a medium than a marketing input with a lower ROI.

Advantages

1. Data-driven decision making: MMM provides marketers with a quantifiable understanding of the impact of different marketing initiatives on sales outcomes, enabling them to make data-driven decisions.
2. Budget optimization: MMM helps marketers to determine the optimal allocation of their marketing budget, as it provides a clear understanding of which marketing initiatives are driving the most sales.
3. Real-time analysis: MMM can be used to evaluate the impact of different marketing activities in real-time, allowing marketers to make quick and informed decisions.
4. Comparison across segments and channels: MMM enables marketers to compare the impact of different marketing initiatives across different segments, channels, and time periods, helping them to make better decisions on where to allocate their resources.

Disadvantages

1. Limited scope: MMM only considers the 4 Ps of the marketing mix and may not take into account other factors that can impact sales outcomes, such as competition, market trends, and consumer behavior.
2. Reliance on historical data: MMM relies on historical sales and marketing data to make predictions, which may not always reflect current market conditions.
3. Model limitations: MMM models can be complex and may not accurately reflect the impact of certain marketing initiatives in certain situations.
4. High cost: MMM can be a costly investment for companies, requiring specialized software and personnel to implement.

Marketing Mix Models

Common methods for MMM include:

Common Marketing Mix Models

1. Regression analysis: Regression analysis is a statistical method that is used to identify the relationship between different marketing inputs and sales outcomes. This method is commonly used in MMM to understand the impact of various marketing initiatives on sales.
2. Time series analysis: Time series analysis is a statistical method that is used to analyze sales and marketing data over time. This method is used in MMM to understand the impact of marketing initiatives on sales across different time periods.
3. Experimental design: Experimental design is a method that involves manipulating different marketing inputs and observing the impact on sales outcomes. This method is used in MMM to determine the causal relationship between marketing initiatives and sales.
4. Structural equation modelling: Structural equation modelling is a statistical method that is used to understand the complex relationships between different marketing inputs and sales outcomes. This method is used in MMM to provide a comprehensive understanding of the impact of different marketing initiatives on sales.
5. Machine learning algorithms: Machine learning algorithms, such as decision trees, random forests, and neural networks, are used in MMM to identify patterns in sales and marketing data and make predictions about the impact of different marketing initiatives on sales outcomes.

These methods are used in combination to provide a comprehensive understanding of the impact of different marketing initiatives on sales outcomes. The choice of method depends on the type of data available and the specific research questions being addressed in the MMM analysis.

Conclusion

In conclusion, MMM is a valuable tool for marketers seeking to make data-driven decisions and optimize their marketing spend. By providing insights into the impact of different marketing initiatives on sales outcomes, MMM enables marketers to improve the ROI of their marketing efforts and drive better results.