Ad Exposure Strategy Process to Maximize ROI of Advertising

"I know I waste of half my advertising money...the only problem is I don't know which half."

This is a well-known quote within the advertising industry, and many feel that it is actually true. Here, we introduce to you a'real life' case study of how SPI came up with an analytical solution for a company trying to come up with a strategy to maximize the ROI of their ad investments over the short-term.

Company Y, which has a food brand X, wanted to implement an ad exposure strategy that would maximize short-term ROI of ad activities to boost sales. Brand X's sales had been declining due to the launch of a competitive product which had a lower price. And a review and revision of the Brand X's ad activities had become a big issue for the company.

The first thing SPI did was to construct a marketing-mix model using regression analysis, with sales and marketing activity data as the key inputs. The model we constructed clarified that the ROI on TV investments was the most efficient among all media investments (refer to the following graph).

 

Based on the results of this marketing-mix model analysis, SPI came up with a hypothesis that there was a relationship between TV ads and other marketing activities or external factors. To verify this hypothesis, we then conducted another analysis using a methodology called 'Neural Network' modeling. 'Neural Networks' is a modeling methodology that was developed that tries to simulate the neuron network of a human brain. This analysis helps to measure the interaction or synergy effects between variables, as well as the 'non-linear' effects of variables.

From the results, we found that there was a very interesting relationship between TV ads and the temperature of the weather (refer to the following graph).

 

Even though Brand X is sold better during the hotter seasons, TV ad exposure was more efficient (had a higher ROI) the cooler the temperature was outside. And we could also see that the sales increases are weakened if the amount of TV ad GRP invested exceeds a certain critical point.

We surmised that the launch of the competitive brand was influencing the relationship between TV ads and the temperature for Brand X. Our hypothesis was that ads for Brand X during the hotter seasons was stimulating loyal users sales only, while 'floating' consumers were stimulated to trial the new competitive brand.

According to the results of our analysis, SPI came up with the strategy to reduce TV ad exposure during the hotter seasons and shift that amount to the cooler seasons. We also were able to determine the optimal TV GRP volume that ensured the highest ROI on the investments.

Finally, we also conducted further verification of hypotheses for other media derived from this analysis, and continuously work with Company Y to further improve the ROI of their ad investments.

Author: Yusuke Saito, Senior Analyst

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