Here’s what the system looks like: Participating ad-delivery companies and websites will put special logos (the “Power i”) in or near ads that are targeted based on user behavior. This logo will lead users to detailed info about who targeted the ad, how the information was collected and used, and how the user can opt-out of future targeting using an opt-out cookie in the user’s browser.
These labels will be implemented on the right ads through code that travels through the ad-delivery chain. A growing coalition of ad-delivery companies, the Network Advertising Initiative (NAI), will perform annual compliance reviews to confirm that internal ad-company processes actually match their public promises.
With these first steps nearly in place, the focus now should turn to measuring how well the system is working. Here are three things that, for me, will indicate initial success in self-regulation: The consumer experience is great; failures are visible; and outliers are shunned.
1. The consumer experience must be great.
Targeting companies are offering an opt-out, not an opt-in, so the burden is on the industry to make the consumer experience superb. This means informing consumers sincerely and objectively, and effectuating their intent as easily and effectively as possible.
Based on our experience at PrivacyChoice, three usability principles are critical: Consumers should be able to achieve their intent with a minimum of effort and clicks; choices made should be durable or easy to refresh, even if the user takes a relatively normal action of deleting their cookies; and consumers should be able to see and set preferences not only for the companies delivering a particular ad, but also for any and all targeting companies, including all those deployed on a site or a webpage.
Ultimately the most important success metric is consumer satisfaction with the process, captured methodically. Did the user find it clear and easy to use? Was the information provided too little, too much or just right?
2. Failures must be visible.
Ad targeting processes happen on the back-end, invisible to the average consumer. This makes the NAI’s oversight function critical to confidence in self-regulation.
With the NAI soon to have over 70 member companies, it would be surprising if we didn’t see at least a few missteps (only one member company had a disclosed compliance issue in 2009, although no formal resolution was published). The NAI should handle these transparently, both when they are identified and when they are resolved.
Fear is an effective motivator. Besides, guidelines are interpreted and become precedent in the compliance reviews, making transparency even more appropriate.
3. Outliers must be shunned.
The ad-targeting ecosystem is deal-intensive; it thrives on data sharing transactions among companies that utilize or provide some piece of the ad targeting puzzle. Deal opportunities will drive good behavior like nothing else. If compliant companies refuse to transact with non-compliant members, the world gets a lot smaller for the outliers.
Even if antitrust concerns mean this can’t be an express NAI requirement, it should be a common-sense practice, as well as a promise to advertisers. In a successful self-regulatory system, data should be considered contaminated if not collected through compliant processes.
More than any other company (or even the NAI itself), Google can shun outliers in a way that could ensure success for the new privacy framework. Google can start by tightening the filter on third party participants in its ad exchange to include only compliant companies. Google also can require and automate better consumer privacy disclosure for the websites in its vast ad network. Google’s partners will make it a priority if Google requires it.
The launch of the new self-regulatory system is only the first stage of a broader privacy framework for online profile-based marketing. Harder challenges, like how user choices are presented and made in a mobile environment or social network, still lay ahead. Self-regulatory success will come from an approach that is user-focused, transparent and consistently reinforced between participants, particularly those with the most influence.