In the age of analytics, The Marketing Analytics Practitioner’s Guide serves as a comprehensive guide to marketing management, covering the underlying concepts and their application.
As advances in technology transform the very nature of marketing, there has never been greater need for marketers to learn marketing.
Essentially a practitioner’s guide to marketing management in the 21st century, the guide blends the art and the science of marketing to reflect how the discipline has matured in the age of analytics.
Application oriented, it imparts an understanding of how to interpret and apply research data and big data with the aid of the analytical tools that practitioners use.
“It is difficult to hide from the illumination of a market response model.” — Hanssens et al. (2003).
Marketers need to align their brand’s marketing mix into a coordinated programme designed to drive revenue and profit. To achieve desired goals, they need an understanding of the level or combination of the mix variables that optimizes the brand’s performance. This in turn, requires an appreciation of how sales respond to the expenditures on these variables. It would be useful if there was a method to assess the impact of the elements of the mix, on sales and ROI.
Though far from perfect, there is such a method — it is referred to as market mix modelling or market response modelling. These models use statistical methods of analysis of historic market data, to estimate the impact of various marketing activities on sales. They reveal the effectiveness of the marketing mix elements in terms of their contribution to sales and profits, and can be benchmarked against costs to compute ROI. This knowledge empowers marketers to craft plans that optimize the use of resources, and infuse marketing decisions with the logic and discipline of analytics.
This chapter provides an outline of the key design considerations for market mix models. It guides you on how to apply them, and how to interpret and analyse the output of these models.
While an effort is made to keep the content simple and easy to understand, it does assume an understanding of statistics and econometrics. Market modelling in general requires specialized skills; unless you are interested in developing models, you do not need to know how market models are created. What you need to know is how to interpret and apply them. So if you suspect you lack the required knowledge of statistics, do feel free to skim the sections on design considerations and methodologies, and focus more on the section on analysis and interpretation.
Market response modelling is a vast topic covering a wide spectrum of methods and techniques. The discussion that follows is confined to a few such methods that are widely used by practitioners. None of them is ideal — the ideal model ought to be able to capture the full dynamics of the marketing mix, and its impact on sales. No such model exists today, though there are many that can turn data into actionable insights.