MARKETING MIX ANALYSIS

Why Marketing-Mix Analysis?

The marketing ROI has been a topic of considerable debate between proponents of brand management and those of marketing accountability. As the brand management discipline works to leverage marketing investments to meet the challenge of an increasingly fragmented media audience, financial stakeholders of corporations demand a greater visibility into these investments. The prolific usage of scanner data based marketing mix modeling methods of marketing ROI measurement has generated additional pressure on brand-managers to demonstrate ROI on their marketing investments. Marketing expenditures in the US have grown exponentially over the past several years. If marketing were an industry it would be one of the largest, (1/10th of the US GDP at just over US$ 1 Trillion). In several industries, especially consumer goods, marketing represents more than half of total COGS.

Application

Marketing-Mix Analysis output can be used to optimize Marketing  Spending. Marketing budget can be optimally distributed across marketing tactics by iteratively adding marketing dollars to each tactic that maximizes total ROI. It is important to remember that ROI of a marketing tactic is not constant but changes as investment levels are changed. This is because of the nonlinear relationship between most marketing tactics and sales. Marketing optimization will always distribute the next marketing dollar to that tactic that will yield the highest total ROI. It is also important to remember that tactics like trade promotions that are usually included as a linear impact on sales cannot be included in the optimization, since the linear relationship will result in the ROI for that tactic never decreasing for any level of spending. Also most marketing-mix models in industry and academia use a preset nonlinear form like logarithmic, exponential decay or s-curve. The actual shape of the relationship for different marketing tactics and sales may differ from tactic to tactic and the correct approach is to empirically determine the correct shape by iteratively testing various logical shapes.

Evaluating the effectiveness of marketing activities is an important task in the market strategy for any consumer product. Measuring the effectiveness enables marketers to     determine the return on marketing investment, but more importantly, it also enables them to ascertain if one marketing channel is over-saturated, so that resources can be more efficiently deployed in under-saturated channels using optimization techniques. 

Methodology

Marketing Mix Analysis is typically carried out using Linear Regression Modeling. Nonlinear and lagged effects are included using techniques like Adstock transformations. Typical Marketing-Mix output includes a decomposition of total annual sales into contributions from each marketing component, a.k.a Contribution pie-chart.

Marketing Mix Analysis decompose total sales into two components:

1. Base Sales: This is the natural demand for the product driven by economic factors like pricing, long-term trends, seasonality, and also brand qualitative factors like awareness and loyalty.

2. Incremental Sales: Incremental sales are the component of sales driven by marketing and promotional activities. This component can be further decomposed into sales due to each marketing component like Television or Radio Advertising, Magazine/Print Advertising, Coupons, Direct Mail, Internet, Feature or Display Promotions and Temporary Price Reductions. Some of these activities have short-term returns (Coupons, Promotions), while others have longer term returns (TV, Radio, Magazine/Print).

Marketing budgets optimized using marketing-mix models may tend too much towards efficiency because marketing-mix models measure only the short-term effects of marketing. Longer term effects of marketing are reflected in its brand equity. The impact of marketing spend on brand equity is usually not captured by marketing-mix models. One reason is that the longer duration that marketing takes to impact brand perception extends beyond the simultaneous or at best weeks ahead impact of marketing on sales that these models measure. The other reason is that temporary fluctuation in sales due to economic and social conditions do not necessarily mean that marketing has been ineffective in building brand equity. On the contrary, it is very possible that in the short term sales and market-share could deteriorate, but brand equity could actually be higher. This higher equity should in the long run help the brand recover sales and market-share.

[This content has been posted at http://en.wikipedia.org/wiki/Marketing-mix_models under GNU Free Documentation License (GFDL).]