Personalizing Advertising based on a User’s Click-Through Rate

According to Wikipedia:

Click-through rate or CTR is a way of measuring the success of an online advertising campaign. A CTR is obtained by dividing the number of users who clicked on an ad on a web page by the number of times the ad was delivered (impressions). For example, if a banner ad was delivered 100 times (impressions delivered) and one person clicked on it (clicks recorded), then the resulting CTR would be 1 percent.

The click-through r…

According to Wikipedia:

Click-through rate or CTR is a way of measuring the success of an online advertising campaign. A CTR is obtained by dividing the number of users who clicked on an ad on a web page by the number of times the ad was delivered (impressions). For example, if a banner ad was delivered 100 times (impressions delivered) and one person clicked on it (clicks recorded), then the resulting CTR would be 1 percent.

The click-through rate measure is the key enabler for the pay-per-click (PPC) advertising model, where advertisers only pay when a user actually clicks on an advertisement to visit the advertisers’ website. A testament to the success of the PPC model is that it accounts for the overwhelming majority of Google’s $20B+ annual revenue.

Most of the attention to CTR has been ad-centric. The quality of an ad–or, rather, of how well an ad is targeted–is largely measured based on its click-through rate, average over all of the users to whom it is presented.

Google, in particular, requires advertisers with a low CTR to place a higher bid per click. The relative ranking of ads reflects a product of the bid and the CTR. This product can be interpreted in one of two ways: either combination of the advertister’s and users’ interest, or as the expected revenue that the ad will generate for Google.

But there is a different way to look at CTR. Instead of looking at the aggregate behavior for an ad across all users, why not look at the aggregate behavior for an user across all ads?

For example, consider a user who never clicks on ads. Perhaps the user is using an ad blocker that the search engine cannot detect, or the user may simply be ignoring the ads. At the other extreme, there are users who click on ads at higher than average rates (though some may be bots committing click fraud).

Of course, all user behavior is averaged in calculating the CTR for an ad. But a user-centric view suggests a a couple of advertising personalization strategies:

Don’t bother showing ads to a user who never clicks on them, since there is no value in doing so. If ad display alone is valuable in influencing users, then there should be a cost-per-impression component, though that would require a reliable way to determine that the user actually sees the ad.
Calibrate the threshold for ad quality (i.e., the minimum CTR across all users) to a user’s propensity to click on ads. Doing so could reduce the annoyance of people with high thresholds while increasing the ad revenue from people with low ones.

It’s clear that enough people click on ads to keep search engines in business, and that the easy availability of ad blockers (AdBlock for display ads, CustomizeGoogle for Google’s PPC ads) has not made a dent in this revenue stream. Nonetheless, showing ads to users who don’t click on them degrades user experience without generating revenue for anyone–a lose/lose.

Personalizing advertising based on a user’s demonstrated inclination to click on ads feels like a no-brainer. Has anyone tried it?

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