User:Sreejithn/MarketingMixModeling

Marketing Mix Modeling is a term of art for the use of multivariate regressions on sales and marketing time series data to (i) estimate the impact of various promotional tactics on sales and then (ii) forecast the impact of future sets of promotional tactics. It is often used to optimize promotional tactics with respect to sales revenue or profit. The techniques were developed by econometricians and were first applied to consumer packaged goods since manufacturers of those goods had access to good data on sales and marketing support. In the recent times MMM has found acceptance as a trustworthy marketing tool among the major consumer marketing giants in the industry.

History
The model was developed by Neil Borden who first started using the phrase in 1949. “An executive is a mixer of ingredients, who sometimes follows a recipe as he goes along, sometimes adapts a recipe to the ingredients immediately available, and sometimes experiments with or invents ingredients no one else has tried." (Culliton, J. 1948)

According to Borden ,"When building a marketing program to fit the needs of his firm, the marketing manager has to weigh the behavioral forces and then juggle marketing elements in his mix with a keen eye on the resources with which he has to work." (Borden, N. 1964 pg 365).

Jerome McCarthy (McCarthy, J. 1960), was the first person to suggest the four P's viz price, promotion, product and distribution which constitute the most common variables used in constructing a marketing mix. According to McCarthy the marketers essentially have these four variables which they can use while crafting a marketing strategy and writing a marketing plan. In the long term, all four of the mix variables can be changed, but in the short term it is difficult to modify the product or the distribution channel.

Another set of marketing mix variables were developed by Albert Frey (Frey, A. 1961) who classified the marketing variables into two categories: the offering, and process variables. The "offering" consists of the product, service, packaging, brand, and price. The "process" or "method" variables included advertising, promotion, sales promotion, personal selling, publicity, distribution channels, marketing research, strategy formation, and new product development.

Recently, Bernard Booms and Mary Bitner built a model consisting of seven P's (Booms, B. and Bitner, M. 1981). They added "People" to the list of existing variables, in order to recognize the importance of the human element in all aspects of marketing. They added "process" to reflect the fact that services, unlike physical products, are experienced as a process at the time that they are purchased.

Marketing Mix Model
Marketing Mix Modeling (MMM) is an analytical approach that use past data (Syndicated Point-of-sale data and companies’ internal data) to quantify the sales impact of various marketing activities. Mathematically, this done by establishing a simultaneous relation of various marketing activities with the sales, in the form of a linear or a non-linear equation, through the statistical technique of regression. MMM defines the effectiveness of each of the marketing elements in terms of its contribution to sales-volume, effectiveness (volume generated by each unit of effort), efficiency (volume generated by each rupee spend) and ROI (rupee generated by each rupee spend). These learnings are then adopted to adjust marketing tactics and strategies, optimize the marketing plan and also to forecast sales while simulating various scenarios.



This is accomplished by setting up a model with the sales volume/value as the dependent variable and independent variables created out of the various marketing efforts. The creation of variables for Marketing Mix Modeling is a complicated affair and is as much an art as it is a science. Once the variables are created, multiple iterations are carried out to create a model which explains the volume/value trends perfectly. Further validations are carried out, either by using a validation data, or by the consistency of the business results.

The output can be used to analyze the impact of the marketing elements on various dimensions. The contribution of each element as a percentage of the total plotted year on year is a good indicator of how the effectiveness of various elements changes over the years. The yearly change in contribution is also measure by a due-to analysis which shows what percentage of the change in total sales is attributable to each of the elements. For activities like television advertising and trade promotions, more sophisticated analysis like effectiveness can be carried out. This analysis tells the marketing manager the incremental gain in sales that can be obtained by increasing the respective marketing element by one unit. If detailed spend information per activity is available then it is possible to calculate the Return on Investment of the marketing activity. Not only is this useful for reporting the historical effectiveness of the activity, it also helps in optimizing the marketing budget by identifying the most and least efficient marketing activities.

Once the final model is ready, the results from it can be used to simulate marketing scenarios for a ‘What-if’ analysis. The marketing manager can reallocate this marketing budget in different proportions and see the direct impact on sales/value. He can optimize the budget by allocating spends to those activities which give the highest return on investment.

Base and Incremental Volume
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

Television Advertising
For the TV advertising activity, we can know how each ad copy has performed in the market in terms of its impact on sales volume. We can gain insights into the direct and the halo effect of TV activity and hence optimize advertising spends across various products or sub-brands under the same brand. We can know the effectiveness of a 15-seconder ad vis-à-vis a 30-seconder ad. We can also know how an ad has performed when it was run during a prime-time slot vis-à-vis during an off-prime-time slot. Hence depending on the differential cost one can identify the most optimal way to allocate TV advertising budget. Not just this, MMM also tells us the effectiveness in terms of volume response at various levels of GRPs within a time frame, be it a week or a month. We can know the minimum level of GRPs (threshold limit) in a week that need to be aired in order to make an impact; we can know the level at which the activity gives maximum ROI; we can know the level of GRPs at which the impact on volume maximizes (saturation limit) and that the further activity does not have any payback. The role of new product based TV activity and the equity based TV activity in growing the brand can also be identified.

Trade Promotions
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 decrease the cost per item for the consumer. 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 etc. We can use this information to optimize the trade plan by choosing the most effective trade channels and targeting the most effective promotion activity

Pricing
Price changes of the brand impacts the sales 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.

Distribution
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. Now based upon the cost of such efforts, managers identified the right channel to invest more for distribution.

Launches
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.

Competition
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.