“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.
Market mix models are multifaceted, often including the interactions and interdependencies within marketing mix variables, lagged responses, competition, and simultaneous relations. This chapter highlights some of these factors that need to be taken into consideration while developing superior market models.
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Solutions for market mix modelling.
Marketing has changed. More so in practical terms, and marketing education is lagging.
The fundamental change lies in the application of analytics and research. Every aspect of the marketing mix can be sensed, tracked and measured.
That does not mean that marketers need to become expert statisticians. We don't need to learn to develop marketing mix models or create perceptual maps. But we should be able to understand and interpret them.
MarketingMind helps. But the real challenge lies in developing expertise in the interpretation and the application of market intelligence.
The Destiny market simulator was developed in response to this challenge. Traversing business years within days, it imparts a concentrated dose of analytics-based strategic marketing experiences.
Like fighter pilots, marketers too can be trained with combat simulators that authentically reflect market realities.
But be careful. There are plenty of toys that masquerade as simulators.
Destiny is unique. It is an authentic FMCG (CPG) market simulator that accurately imitates the way consumers shop, and replicates the reports and information that marketers use at leading consumer marketing firms.
While in a classroom setting you are pitted against others, as an independent learner, you get to play against the computer. Either way you learn to implement effective marketing strategies, develop an understanding of what drives store choice and brand choice, and become proficient in the use of market knowledge and financial data for day-to-day business decisions.