In recent times, we have seen the proliferation of new media (Internet, viral marketing, event marketing, sports marketing, product placement, cell phones, etc.), decreased television viewership, the advent of TiVo and similar technology where viewers can skip through commercials, and increased cost-cutting pressures. All of this has combined to increase demands for marketing departments to maximize the return on their marketing investments; that is, to optimize the combination of marketing and advertising investments to generate the greatest sales growth and/or maximize profits. Marketing mix modeling measures the potential value of all marketing inputs and identifies marketing investments that are most likely to produce long-term revenue growth.

Typically, marketing mix modeling involves the use of multiple regression techniques to help predict the optimal mix of marketing variables. Regression is based on a number of inputs (or independent variables) and how these relate to an outcome (or dependent variable) such as sales or profits. Once the model is built and validated, the input variables (advertising, promotion, etc.) can be manipulated to determine the net effect on a company’s sales or profits.

The data that go into creating a marketing mix model include:

• Economic data
• Industry data
• Category data
• Advertising data (including copy testing)
• Promotional data
• Competitive data
• Service data
• Product data
• Pricing data
• Features & performance
• Market outcome data
• Sales
• Revenues
• Profits