Why Hasn’t Marketing Mix Modelling (MMM) Advanced Much Since its Introduction?
Marketing Mix Modelling (MMM) is transforming a classical adage that half of the advertising spends are being wasted, with the problem being the inability to identify which half. An enormous amount of data is being generated every year to assist marketing managers to make decisions on the optimum utilization of marketing budgets. By analyzing data […]READ MORE >>
Marketing Mix Modelling (MMM) is transforming a classical adage that half of the advertising spends are being wasted, with the problem being the inability to identify which half. An enormous amount of data is being generated every year to assist marketing managers to make decisions on the optimum utilization of marketing budgets. By analyzing data on multiple marketing spends and its effect on sales, marketers can allocate budgets efficiently and devise an effective marketing strategy. Marketing mix modelling has been assisting marketing managers in formulating marketing plans with the help of valuable analytical tool to optimize the marketing mix to achieve increased sales value.
Since the time marketing mix modeling was first used during the early 90’s, it hasn’t advanced much regarding issues covered and underlying methods used. Here are some of the reason its growth has been hindered:
The Attribution Problem
Marketers are equipped with an arsenal of tools to improve their brand’s performance. These tools do not work in isolation, but rather the synergy between them is what causes a brand to succeed in the marketplace. Marketing professionals are always posed with the same problem. If there is an increase in sales or other metrics, which marketing or promotional tool should it be attributed to? Is the increase in sales caused by increased advertisement spend or a reduction in price? This can pose to be a roadblock during the resource allocation process. However, with the advent of digital technology, attribution models like first interaction, time decay, position based model, and backward looking last click attribution seeks to resolve this problem area.
How to Quantify this Information?
Insights obtained from marketing mix modelling depends heavily on the quality of input data. Abundant information can be captured with the increasing prominence of big data. One of the biggest challenges in this area is the measurement of unquantifiable information. Marketing indicators such as customer satisfaction, brand image, emotions, customer feedback, along with observational tools like eye tracking, video, physiological measurements, tracking, facial expressions analysis, and head movement cannot be expressed in terms of numbers. Marketing mix modelling does not consider such information unless it can be quantified.
The Case of Marketing Myopia
A brand is created by years of deliberate and skillful marketing efforts. Marketing professionals often attribute a brand’s success to short-term effects of media and marketing efforts. Recent marketing activities may increase brand value in the short run, but a brand’s real value is determined by its consistency, relevance, and distinction over the long run. Marketing mix modelling often struggles to adjust this long-term significance accurately.
Marketing experts have come up with various models to address these issues, offering a vast scope for improvement in marketing mix modelling. Digital marketing has also made contributions in this field by providing massive amount of data and acting as a field to test products and brands.
To gain more insights on the trends in marketing analytics
- Marketing Mix Modeling and its Strategic Impact on an Organization’s Success
- Top 3 Ways in Which Pricing Analytics Can Enhance Your Business