Marketing Mix (Research Paper) Essay

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 topline 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, and 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. .