Business magazines and websites are abuzz with news about the value of marketing mix modeling as a way to help companies maximize returns on their marketing investments (ROMI). Despite the currency of this topic in the media, the concepts and tools of marketing mix modeling date back at least 30 to 40 years. The topic is of growing interest partly because of the corporate world’s interest in growing top line revenue.

The last couple of decades have witnessed unparalleled cost cutting and staff reductions among the Fortune 500 in the U.S. The opportunities for further cost reductions are diminishing in number and scale, so the pressure for long-term financial performance from public markets can only be met by renewed emphasis on new products and revenue growth.

A second reason for the growing interest in marketing mix modeling is the proliferation of new media (i.e., new ways to spend the marketing budget), including the Internet, online communities, search engines, event marketing, sports marketing, viral marketing, cell phones, text messaging, etc. No one knows how to accurately measure the potential value of these many new ways to spend one’s marketing dollars. To grow revenue and profits, corporate executives need to understand the types of marketing investments that are most likely to produce viable, long-term revenue growth. That is, what combination of marketing and advertising investments will generate the greatest sales growth and/or maximize profits? Eureka! Marketing mix modeling might provide some answers to these challenging problems.

What exactly is marketing mix modeling? The term is widely used and applied indiscriminately to a broad range of marketing models used to evaluate different components of marketing plans, such as advertising, promotion, packaging, media weight levels, sales force numbers, etc. These models can be of many types, but multiple regression is the workhorse of most marketing mix modeling.

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 or both. 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. If the president of a company knows that sales will go up $10 million for every $1 million he spends on a particular advertising campaign, he can quickly determine if additional advertising investment makes economic sense.

But, in a broader sense, a deep understanding of the variables that drive sales and profits upwards is essential to determining an optimal strategy for the corporation. So, marketing mix modeling can assist in making specific marketing decisions and tradeoffs, but it can also create a broad platform of knowledge to guide strategic planning.

From a conceptual perspective, there are two main strategies to pursue in marketing mix modeling. One is longitudinal; the other is cross-sectional or side-by-side analysis. In longitudinal analyses, the corporation looks at sales and profits over a number of time periods (months, quarters, years), compared to the marketing inputs in each of those time periods. In the cross-sectional approach, the corporation’s various sales territories each receive different marketing inputs at the same time, or these inputs are systematically varied across the sales territories, and are compared to the sales and profit outcomes. Both methods are sound, and both have their place. Often, some combination of the two methods is the most efficient.

Regardless of method, marketing mix modeling can be successful only if accurate and highly specific data are available upon which the modeling can be based. The greatest barrier to successful modeling is always a lack of relevant, specific, accurate data. So the first step in any modeling effort is designing the data warehouse that will support the modeling.

The next step is collecting and cleaning all of the historical data and entering it into the data warehouse, and then cleaning and entering new data on a continuing basis. Clean, accurate, highly specific data is absolutely essential to successful modeling. The data must be specific to individual brands and product lines, not the company as a whole.

Attempting to model at the corporate (or aggregate) level rarely works because what’s going on in one part of the company is canceling out or confounding what is going on elsewhere in the company. Here are some types of data to consider when developing the data warehouse:

• Economic data. Employment and unemployment, discretionary income, inflation rates, gross domestic product, interest rates, energy costs, etc. An understanding of the effects of general economic variables is vital to building sound models.

• Industry data. What are the trends in the specific industry? Is the market for the product or service growing? What is the rate of growth? Is international trade affecting the industry? Are important geographic differences evident within the industry?

• Product category data. What are the trends in the specific product category? For example, is the refrigerated soy milk category growing? At what rate? How does this growth vary by geographic region? What are the trends by brand?

• Product lines and SKUs (Stock Keeping Units). What is the history of each major brand within the category? What new products or new SKUs have been introduced, and when, for each major brand? What is the history of private label brands and SKUs in the category?

• Pricing data. A history of average prices for each SKU in the category is essential. Pricing is almost always an important variable.

• Distribution levels. What is the history of distribution levels for each product and SKU? What is the quality of that distribution? Average number of shelf facings per SKU?

• Retail depletions. It’s essential to have a clean measure of sales to end-users, undistorted by fluctuations in inventories. Factory shipments are worthless for modeling purposes, in most instances. Retail take-away (or retail depletions) in dollars and in units (ounces, pounds, cases, etc.) is the most common measure of sales to consumers. The goal is to accurately measure sales to ultimate users (the people the marketing efforts are focused upon).

• Advertising measures. Money spent on media advertising is seldom useful by itself. The media advertising must be translated into television GRP (gross rating point) equivalents, or some other common “currency.” That is, the print advertising, the radio advertising, the online advertising, etc. must all be converted into common units of measure (typically, television GRP equivalents). The money spent by specific media type (adjusted for comparative effectiveness) is another way of weighting media inputs as variables. All of this is apt to prove worthless, however, if copy-testing scores are not included for each of the ads. A media plan of 100 GRPs per week might have no effect if a weak commercial is run, but might have great effect if a terrific commercial is aired. Likewise, the exact media schedule is important, and the length of time each commercial is on the air must be considered because of wear-out effects.

• Consumer promotion. Consumer (or end-user) promotions come in many forms, but the primary characteristic of these promotions (as compared to advertising) is the immediacy of the effects. Promotions are designed to have powerful, short-term effects on sales. Temporary price reductions, cents-off coupons, and “buy one/get one free” are examples of common consumer promotions. These promotions must be understood, measured and incorporated into the models. If not fully comprehended, the promotion effects could easily overwhelm the modeling effort.