MODELING THE AUDIENCE’S BANNER AD EXPOSURE FOR INTERNET ADVERTISING PLANNING

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Executive Summary

The authors note the differences between conventional TV advertising and internet advertising when it comes to ad measurement. One of the main differences is that internet advertising is not bound to a schedule that defines an ad’s insertion into a broadcast. Thus traditional reach & frequency exposure models that aim to insert a certain amount of ads across a specific number of vehicles such that the ad is exposed to an audience once per insertion at most may not be the best to apply to online ads. It is argued that one of the main reasons why traditional models do not work online is that web users control their own navigation and can potentially be exposed to the same ad insertion more than once.

Hence, they proposed the use of a different exposure model based on Negative Binomial Distribution (NBD) which aims to represent user exposure rates to parts of specific sites where an ad is placed rather than the site as a whole. The model integrates well with web analytics as it relies on clickstream data for calibration.

The authors went on to conduct an actual study using data collected from panel-based software installed on the computers of 1012 users and applied the NBD model to it. They set out to determine whether the NBD model was effective in determining ad exposure for a given month as well as predictive abilities for a future month by creating 1000 ad schedules for a single ad placed in the top 100 subdomains visited by the sample. They concluded that the model’s predicted outcomes and the actual outcome from the user data were nearly identical. Thus, they were able to determine the optimal ad "schedule” for a particular set of ads for maximum exposure in the most visited subdomains on the internet.

Review

For people responsible for online advertising budgets, this is a model that is worth looking into given its applicability to specific parts of a site rather than the site as a whole. If you utilize tactics such as subdomains and microsites with their own unique addresses, this is a worthy read. Insofar as its application to web analytics, it only makes sense that web analytics is an integral part of how and where online advertising dollars are spent given the sophistication of the tools & practitioners out there today. As an example, a model that optimizes reach and exposure can be coupled with web analytics by assigning dollar values to each creative element or determining the average order size/revenue per element to determine per-ad effectiveness and campaigns can be optimized on the fly as necessary. Non-revenue based metrics can also be linked to each element to allow for things like visitor segmentation, determine likelihood of repeat visits, etc. This can then inform future decisions on whether to tweak creative, change its placement on a particular site, or choose an entirely different ad space.

It is equally important to take a look at your own traffic. Web analytics tools can tell you a lot about referred traffic to your site. The study carried out by the authors in this article based their ad schedules on the 100 most visited subdomains from their sample panel. However, there are some sites out there that don’t necessarily get traffic from the most popular sources. Always keep this in mind – you know your site and who visits!

Like other math-based models out there, there are limitations. The authors state that this should only be applied to online marketing campaigns (versus multi-channel campaigns), ad executions that run for the exact same amount of time, and that it is an exposure model for banner ads only. There is clearly room for more research to be carried out to fill the gaps but this is a very good start. It will be interesting to see whether this model can be carried over into tactics like email or mobile.

This article is recommended for web analytics practitioners, agency account managers, or anyone who influences how and where external online ad dollars for marketing campaigns are spent.