1) Offering Transparency — When someone I log into a new web service through Twitter and all my Twitter data is populated, the data flow is pretty intuitive. Compare that to the data flows for online advertising. As a consumer, its impossible to understand where the data enters our labyrinth of redirects and market mechanisms and where it exits to target an actual ad.

2) Offering Choice & Control —  It’s my choice whether I want to have my data ported into a specific new service. I, Greg Hills, tend to consent. There is of course a tradeoff between anonymity and relevance. But within specific use cases, like signing up for Plancast.com, the cost to my privacy is clearly defined and the benefit of volunteering information is immediately apparent.

Advertisers fail to convince consumers of the benefits of relevance since they argue in the abstract rather than in the context of specific use cases. Urging someone to sacrifice privacy for the sake of Relevance is like urging someone to embrace hedge funds for the sake of efficiency in the capital markets. The benefit is too abstract to seem compelling.

Also, Privacy is considered practically sancrosanct when presented in the abstract. Going against Privacy in general is like going against Motherhood in general. It’s a difficult position to argue.

But when you present the trade-off in the context of a circumscribed web experience, people are willing to sacrifice anonymity for relevance. They’ll put in their ZIP code to get the weather. They’ll volunteer information about the high school they attended so that long lost friends can get back in touch.

When the benefit is made clear, and the privacy cost is limited to a specific web domain or service, consumers will consistently opt for relevance. The challenge for advertisers is to make the cost and benefits clear so that consumers can make informed choices.

3) Creating Trust — Consumers trust what they know. Social networks and content providers are consumer-facing brands, which is a big advantage. The plethora of companies in the ad tech landscape are barely recognizable to people in the industry, forget about consumers. Why would you trust someone you’ve never heard of?

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So, what lessons can we take as advertisers?

1) We need to create a trusted, recognizable brand entity that represents data-hungry advertisers. I think we’re making great progress with the development of universal behavioral targeting icons.

2a) Users are more likely to sacrifice anonymity when presented with clear benefits. Simplifying data flows would make the whole process less scary and allow consumers to associate privacy costs with corresponding relevancy benefits. For this reason, I’m very bullish on log-in’s becoming the building block for the data landscape of the future.

2b) We’re seeing an infusion of venture capital into data exchanges and it seems like a foregone conclusion that data exchanges will play a foundational role in the ad ecosystem. Let’s think hard about the fact the publisher’s interest in not having the value of their audience diluted through data exchanges is diametrically opposed to the consumer’s interest in knowing when they are being observed by advertisers on publisher sites, as well as how and when that data is later being used to target ads. Is this sustainable?

4) A culture of distributed opt-in leads to more innovation than a culture of centralized opt-out.

5) Consumer preferences vary greatly but privacy guidelines tend to set the baseline at the level of the most conservative consumer. A culture of opt-in where individuals express preferences for specific experiences empowers consumers to define their own web experience. They can then make their own decisions regarding the anonymity vs. relevance trade off. Giving consumers power through opt-in culture creates freer flowing data than an opt-out culture.

A hybrid model is a big step forward for the “Gold Standard” of audience measurement tools, but it still doesn’t address the new challenges faced by media planners. As the “People, not Pages” approach gains greater acceptance, media planners will be less focused on choosing one media property over another. Instead of choosing between Maxim.com and AskMen.com, planners will choose between two ad networks offering the same segment targeting men 18-25 with an interest in fashion. But what is the reach and composition for each of the two ad networks offering that same segment? Right now, there is no independent 3rd party audience verification tool for targeted media. This is fine for direct response marketers, where targeting is simply a means to a very measurable end: online response rates. But for brand marketers who define success as reach and frequency against a key demographic, independent audience verification is key. Focusing on “People, Not Pages” allows marketers to be much more accurate and efficient, but marketers need assurance that they are getting what they pay for.

How do you see this issue being resolved? Through survey companies like Vizu and InsightExpress? Measurement companies like comScore? Pure-play data vendors like BlueKai? Data/media vendors like Aperture? I’m curious to hear your thoughts.