Understanding Bayesian Marketing Mix Modelling

Bayesian Marketing Mix Modelling

Marketing Mix Modelling involves the employment of analytical methods to quantify the influence of diverse marketing strategies on sales outcomes, profitability, and return on investment (ROI). It aids marketers in making informed decisions based on data, refining their marketing strategies, and distributing their budgets across various channels and tactics to achieve desired objectives. One such framework of marketing is the Bayesian Marketing Mix Model. As the marketing industry grows day by day, marketers keep on seeking new methods which help them make better marketing strategies and understand their marketing efforts more effectively in order to make data driven decisions. In the recent years, the Bayesian Mix Model has become a prominent method to bring advancement and betterment in the companies marketing strategy. It has become the most preferrable approach for analysts and agencies. Bayesian Marketing Mix Modelling allows the marketing managers to effectively use the potential of the Bayesian statistics while analysing their marketing strategies and their performances, while at the same time optimizing their media investments. This article aims on explaining to you the importance, significance and usage of the Bayesian Marketing Mix Modelling. 

In order to learn about the Bayesian Marketing Mix Model, we first need to understand what is a marketing mix model and how do we use it. 

By the time, as the marketing landscape grows increasingly complex day by day, it becomes important for the IT students, professionals, aspiring data engineers, and developers, to understand the role of Marketing Mix Modelling (MMM). As mentioned above, a marketing mix model helps the agencies and firms in understanding and evaluating their marketing strategies and making changes as required in order to grow their range. Marketing Mix Modelling involves certain elements to help enhance the statistical evaluation and understanding of the marketing strategies. The elements involve Price (amount which consumers are expected to pay), Place (location for the sales of a product), Product (goods and services), Process (methods carried out to deliver the product to a consumer), Promotion (interacting with the consumers regarding the products and services). Marketing Mix Modelling provides the companies with a better point of view about how they can improve their customer base and make decisions which will come off as helpful in their future plans. It helps the companies to look into their past performance and identify the current demands and correlations between the marketing tactics in order to forecast the future results. Companies by using such methods can point out the area of improvement and work on it to make it better.  

The new Marketing Model Mix is however, better than the traditional methods. The Traditional MMM dates back to the mid-20th century and was considered highly applicable and effective during its time. Its main features were to analyse historical data, employ channel attribution models and conduct market research surveys to measure the impact of marketing efforts. But with the change in the marketing landscape, this approach was proved hard to use as it limited the effectiveness of the model. Its reliability on the limited data sources became one of the major drawbacks of the traditional MMM and its attribution models were slow and lacked the accuracy of insights for the decision making. Due to this limitation, it became hard to get a detailed analysis while evaluating the impact of marketing efforts for certain channels and campaigns.  

Bayesian Marketing Mix Modelling: 

As the marketing landscape evolved, the marketing strategies evolved with it. Currently, the most highly used approach for marketing is the Bayesian MMM. The Bayesian MMM essentially plays two important roles, the first one is, it measures the effectiveness of various marketing channels by providing us with an average of their impact. This helps us in getting a specific idea of how much impact does a marketing method makes in sales. The second role of a Bayesian MMM is to predict a range of effects that are possible by various marketing methods. This helps us in pinpointing the smallest and largest effects a marketing method offers so as to make full use of the strategy.