Elements of MMM

Marketing-mix models decompose total sales into two components:

Base Sales: This is the natural demand for the product driven by economic factors like pricing, long-term trends, seasonality, and also qualitative factors like brand awareness and brand loyalty.

Incremental Sales: Incremental sales are the component of sales driven by marketing and promotional activities. This component can be further decomposed into sales due to each marketing component like Television advertising or Radio advertising, Print Advertising (magazines, newspapers etc.), Coupons, Direct Mail, Internet, Feature or Display Promotions and Temporary Price Reductions. Some of these activities have short-term returns (Coupons, Promotions), while others have longer term returns (TV, Radio, Magazine/Print).

Marketing-Mix analyses are typically carried out using Linear Regression Modeling. Nonlinear and lagged effects are included using techniques like Advertising Adstock transformations. Typical output of such analyses include a decomposition of total annual sales into contributions from each marketing component, a.k.a. Contribution pie-chart.

Another standard output is a decomposition of year-over year sales growth/decline, a.k.a. ‘Due-to charts’.

The very break-up of sales volume into base (volume that would be generated in absence of any marketing activity) and incremental (volume generated by marketing activities in the short run) across time gain gives wonderful insights. The base grows or declines across longer periods of time while the activities generating the incremental volume in the short run also impact the base volume in the long run. The variation in the base volume is a good indicator of the strength of the brand and the loyalty it commands from its users.

Market mix modeling can determine the sales impact generated by individual media such as television, magazine, and online display ads. In some cases it can be used to determine the impact of individual advertising campaigns or even ad executions upon sales. For example, for TV advertising activity, it is possible to examine how each ad execution has performed in the market in terms of its impact on sales volume.

MMM can also provide information on TV correlations at different media weight levels, as measured by Gross Rating Points (GRP) in relation to sales volume response within a time frame, be it a week or a month. Information can also be gained on the minimum level of GRPs (threshold limit) in a week that need to be aired in order to make an impact, and conversely, the level of GRPs at which the impact on volume maximizes (saturation limit) and that the further activity does not have any payback.

While not all MMM’s will be able to produce definitive answers to all questions, some additional areas in which insights can sometimes be gained include:

• the effectiveness of 15-second vis-à-vis 30-second executions;
• comparisons in ad performance when run during prime-time vis-à-vis off-prime-time dayparts;
• comparisons into the direct and the halo effect of TV activity across various products or sub-brands.

The role of new product based TV activity and the equity based TV activity in growing the brand can also be compared. GRP’s are converted into reach (i.e. GRPs are divided by the average frequency to get the percentage of people actually watching the advertisement). This is a better measure for modeling TV.

Trade promotion is a key activity in every marketing plan. It is aimed at increasing sales in the short term by employing promotion schemes which effectively increases the customer awareness of the business and its products. The response of consumers to trade promotions is not straight forward and is the subject of much debate. Non-linear models exist to simulate the response. Using MMM we can understand the impact of trade promotion at generating incremental volumes. It is possible to obtain an estimate of the volume generated per promotion event in each of the different retail outlets by region. This way we can identify the most and least effective trade channels. If detailed spend information is available we can compare the Return on Investment of various trade activities like Every Day Low Price, Off-Shelf Display. We can use this information to optimize the trade plan by choosing the most effective trade channels and targeting the most effective promotion activity.

Price increases of the brand impact the sales volume negatively. This effect can be captured through modeling the price in MMM. The model provides the price elasticity of the brand which tells us the percentage change in the sales for each percentage change in price. Using this, the marketing manager can evaluate the impact of a price change decision.

For the element of distribution, we can know how the volume will move by changing distribution efforts or, in other words, by each percentage shift in the width or the depth of distribution. This can be identified specifically for each channel and even for each kind of outlet for off-take sales. In view of these insights, the distribution efforts can be prioritized for each channel or store-type to get the maximum out of the same. A recent study of a laundry brand showed that the incremental volume through 1% more presence in a neighborhood Kirana store is 180% greater than that through 1% more presence in a supermarket.^[6] Based upon the cost of such efforts, managers identified the right channel to invest more for distribution.

When a new product is launched, the associated publicity and promotions typically results in higher volume generation than expected. This extra volume cannot be completely captured in the model using the existing variables. Often special variables to capture this incremental effect of launches are used. The combined contribution of these variables and that of the marketing effort associated with the launch will give the total launch contribution. Different launches can be compared by calculating their effectiveness and ROI.

The impact of competition on the brand sales is captured by creating the competition variables accordingly. The variables are created from the marketing activities of the competition like television advertising, trade promotions, product launches etc. The results from the model can be used to identify the biggest threat to own brand sales from competition. The cross-price elasticity and the cross-promotional elasticity can be used to devise appropriate response to competition tactics. A successful competitive campaign can be analyzed to learn valuable lesson for the own brand. television & Broadcasting: the application of MMM can also be applied in the broadcast media. Broadcasters may want to know what determine whether a particular will be sponsored. This could depend on the presenter attributes, the content, and the time the program is aired. these will therefore form the independent variables in our quest to design a program salability function. Program salabibility is a function of the presenter attributes, the program content and the time the program is aired.