How do we estimate the short-term and long-term impact of advertising on sales? How do we find out what is the optimal level of advertising in a particular market?

C3Research uses a sophisticated time series modeling approach to tease apart the short-term sales impulse as well as the long-term brand equity build up due to advertising. Optimal levels of advertising are modeled using competitive advertising data. Here is how the modeling works:

The model. We first examine the sales and advertising data to assess predictable trends. Next, we set up a vector auto regression model (a "fancy" version of regression in which multiple equations are linked). We primarily deal with three series: sales performance, marketing budget, and competitors' marketing budget. The model is set up in such a way that we can determine cause and effect in a time sequence. For example, advertising today leads to sales tomorrow. As sales go up, advertising spending goes up. In the model, we simulate "shocks" to derive short-term and long-term effects.

Optimal Spending. This modeling approach can be used to determine optimal levels of advertising required in each competitive market for obtaining both short-term results and long-term market build up.

Competitive Effect. This modeling approach can be used to identify whether a brand's marketing activities are resulting in primary sales effect, primary demand effect, or competitive effect. A primary sales effect is indicated when a brand's marketing activities increases its own sales but does not affect the competitors' sales. In the case of a primary demand effect, however, the brand's advertising may increase its own sales as well as those of some competitors, fueling primary demand. A competitive effect is indicated when a brand's advertising increases its own sales but decreases those of its competitors. This analysis is useful in understanding the competitive and industry structure.