Better Real-time Behavioral Targeting for Online Advertising

The enabling technologies exist for online advertisers to employ proven, sophisticated, predictive behavioral targeting techniques in their advertising campaigns. Yet only a small minority of online advertising – primarily RTB platforms and the more sophisticated exchanges – are leveraging these techniques, and even the most sophisticated of these pale in comparison to the level of analytic targeting that traditional marketers have been using for decades.

It’s quite surprising.

The quantitative benefits of classical database marketing techniques are well understood. By targeting the segments most likely to respond to an offer or least likely to default on a credit card or loan, marketers have been dramatically increasing returns on investment for decades.

These same fundamental targeting techniques have been proven to increase click-through and conversion rates for online advertising initiatives as well.  And online campaigns can often be even more targeted than offline campaigns, due to the rich online behavioral data that can be stored and mined, and the opportunities for dynamic personalization of the creative (ad) that are not possible in the offline world.

Yet, even so, behavioral targeting is far less common for online advertising initiatives than for conventional marketing initiatives. The result of this lack of targeting is that much of the spend on online advertising is wasted. eMarketer reports that only 14% of online display ads are targeted, and comScore estimates that as much as 80% of impressions are shown to the wrong audience.

Studies prove that database marketing techniques can dramatically reduce this waste by targeting impressions to the right audience at the right time and in the right place. One research study that analyzed 7 days worth of advertising click-through data logs from a commercial search engine found an increase in click-through rates (CTRs) of 670% with advertising that segmented users according to their behavior through the use of classical clustering algorithms. The research also found that by utilizing advanced user representation and user segmentation algorithms, CTRs can be further improved to more than 1,000% . And, an advertising data company reports that by utilizing simple re-targeting strategies they successfully increased click-through rates by 130%, and by also using customized re-targeting creative, total return on advertising spend increased from 114% (with static ads) to 1,459% .

So, why isn’t behavioral targeting more prevalent with online advertising? Part of the reason is that the default ad serving technologies are limited in the amount of behavioral targeting they can provide. Even so, all of the enabling technology is readily available to allow sophisticated behavioral targeting for display and rich media advertising.

There exists a rich set of criteria and data that can be used to customize creative, and to segment and target users, including:

• Cookie values: Cookies allow marketers to record visitor behavior and actions, and identify previous visitors for re-targeting. By writing relevant session details to a cookie, then reading the cookie values when users return to the site, marketers can easily re-target users. What’s more, by writing the cookie data to log files which are parsed and loaded into the marketers’ data warehouse, marketers can build sophisticated statistical marketing models to drive their targeting efforts, and connect a visitor by cookie value to its rich store of information on the user in their database.

• Geo-location data: This data includes the location of the visitor (including mobile geo-location for mobile device users), as well as connection type, ISP, and other geo-location specific information. Geo-location based targeting can have significant impact on a campaign: One study reports that 50% of users who are shown a location-aware ad on a mobile device will take some action, compared with a .2% CTR for a conventional banner ad on a website .

• HTTP header values, request URL (including query argument values), form field contents, language preference, the browser type and version and other settings can all be used as input to the statistical models, for targeting, and as criteria for customizing the creative.

The appropriate creative can be programmatically selected by the marketer to deliver highly contextual, customized advertising in response to each request, based on the attributes of the request, the information in the cookie file, and the marketers’ targeting algorithms. What’s more, creative can be programmatically customized in real-time – for example by dynamically adjusting the JavaScript in the ad – based on the data associated with the request and the targeting algorithms, further targeting each display and increasing CTRs.

One last piece of technology is needed to complete the picture: Pixel Tracking.

Internet browsers prevent scripts served from one domain from accessing data on another domain. This technical limitation would prevent an advertiser from performing most activities required in order to execute behavioral targeting campaigns. Fortunately, there is a common workaround that involves placing a 1×1 clear pixel image (or any other image) on specific web pages (or ads) that an advertiser wishes to track. Whenever a user renders a page, views an ad, plays a clip or triggers some other event, the client application generates a request to the advertiser’s server enabling the server to take notice of the activity. As the user agent (such as a browser or rich media player) makes the request it can pass along information about the user, as well as specific data (such as cookie values, language preference, etc.) that can be discerned from the request data, and the transaction and its details can be recorded in log files. All data can be recorded without personally identifiable information about the user, providing an anonymous set of data.

By utilizing these technologies for pixel tracking, reading and setting cookie values, detecting user settings, and logging activity details to log files, marketers can build sophisticated behavioral targeting algorithms far more advanced than the current state of today’s norms, and successfully serve much more highly customized and targeted ads to each visitor.

While the most sophisticated platforms in the advertising eco-system are making progress in raising the bar on behavioral targeting, there seems to be some pretty compelling business opportunities that have yet to be tapped.

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