Retail marketing mix analysis

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Retail marketing mix analysis

History[ edit ] The term marketing mix was developed by Neil Borden who first started using the phrase in 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.

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The "offering" consists of the product, service, packaging, brandand price. The "process" or "method" variables included advertising, promotion, sales promotion, personal selling, publicitydistribution channels, marketing researchstrategy formation, and new product development.

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.

Desktop modeling tools such as Micro TSP have made this kind of statistical analysis part of the mainstream now. Most advertising agencies and strategy consulting firms offer MMM services to their clients.

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Mathematically, this is 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 effortefficiency sales volume generated divided by cost and ROI.

These learnings are then adopted to adjust marketing tactics and strategies, optimize the marketing plan and also to forecast sales while simulating various scenarios. The creation of variables for Marketing Mix Modeling is a complicated affair and is as much an art as it is a science.

The balance between automated modeling tools crunching large data sets versus the artisan econometrician is an ongoing debate in MMM, with different agencies and consultants taking a position at certain points in this spectrum. 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 measured 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.

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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.

They can optimize the budget by allocating spends to those activities which give the highest return on investment. Some MMM approaches like to include multiple products or brands fighting against each other in an industry or category model - where cross-price relationships and advertising share of voice is considered as important for wargaming.

Retail marketing mix analysis

Components[ edit ] Marketing-mix models decompose total sales into two components:Marketing Module David F. Miller Center for Retailing Education and Research page 3 Outline Merchandising Management Pricing Management Retail Communication.

Fig. 1 presents the conceptual framework guiding the meta-analysis. To develop the framework, we reviewed the literature on retail marketing-mix instruments and patronage to identify key constructs.

Retail marketing mix analysis

We propose that 24 instruments, assigned to seven groups, affect the outcome variables. Featured. McKinsey Global Institute Our mission is to help leaders in multiple sectors develop a deeper understanding of the global economy.

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