Intelligent Customer Discovery (aka Look-alike modeling)

[kaprasad]

Problem
In our understanding of Turtledove we believe it only supports 2 types of advertising: Retargeting and contextual. This will incentivize advertisers to follow one of the following strategies:

• Create ads that will follow people around the web.
• Take a shot gun approach with contextual and buy low CPM ads to bombard users with ads.

According to Nielson, retargeting is one of the most disliked forms of advertising among consumers (link). Another report (link) shows that 79% of users think they are being tracked due to retargeting ads. Even though retargeting is an important use case for a lot of brands, it represents a small percentage of total data-driven advertising spend (think about the last time you went to tide.com).

That’s because most sophisticated advertisers have already progressed from retargeting. For the most part, they want to leverage their own first-party data, much of which has been volunteered to them by long-term consumers, through loyalty programs, etc. This data can be used to model the characteristics of their most loyal customers, and find where those same characteristics may be present elsewhere in the market – where are their next 100k most loyal customers?

Therefore, we believe the focus should be on allowing brands to find users who might be interested in buying their products. There are several key methodologies that turtledove doesn’t address:

1. Audience Modelling: We should be trying to preserve sophisticated targeting methodologies, that leverages a brand’s valuable first party data to find the next 100k users most likely to be interested in a product, show it to them with reasonable frequency, and pay a healthy price. Audience modeling helps consumers discover new products, and it allows small brands to get traction and cut through the noise with the consumers that are likely to be interested in their new products.
2. Audience Intersection: Another methodology that helps brands find new users is using combination of audiences. Brands use sophisticated models to understand the type of users they want to reach. If they are limited to a single interest-based segments, they will end up wasting money on buying ads they don’t need. A good example here is: A small real estate firm is looking for highly affluent individuals with an interest in real estate in a specific DMA. They try to target "interested in real estate and finance" in the Denver DMA that uses 2 ands, and a geo target. Without these intersections they will need to spend precious marketing dollars just to figure out what works.

Publisher and User Impact
Today publishers of all sizes can realize value in the ad space on their sites because advertisers believe that they can find their customers on these sites. Data driven advertising especially helps bring value to publishers with a smaller footprint (local news, sport blogs, etc). However as described above, brands leverage tools beyond just site visits and context to figure out where they can reach their potential customers. If advertisers lose the ability to easily discover new customers, they will not know how to effectively value publisher inventory. Since advertisers still need to reach their customers, this will either lead to them taking a shotgun approach and pay less per ad or move their budgets to a few big publishers (CNN, NYT, ESPN etc). To make up for the lost revenue, publishers will either have to show more ads per page or erect paywalls, neither of which are ideal or economically feasible outcomes for the end user or the long-term future of the internet.

[michaelkleber]

Hi Kanishk,

I think you have the wrong idea about the TURTLEDOVE proposal. While it could be used for "ads that will follow people around the web", it is definitionally about offering you a way to build and then target an interest group — and to quote the explainer,

An "interest group" is a collection of people whom an advertiser or their ad network believes will be interested in seeing some type of ad.

Over in WICG/floc#12 you brought up the example of "in-market auto." This seems like an interest group to me: If you know some behaviors that lead you to believe a person might be in-market for a car, then when you observe a person engaged in those behaviors, you could add them to your in-market-car interest group. As an ad network, you would see TURTLEDOVE-style requests for ads targeting your in-market-car interest group, and you could pick what ad to serve to people in that group. (This is similar to the RunningShoeReviews.com example from the explainer.)

In this context, the use case "find the next 100k users" is very interesting, and I think one that @benjaminsavage brought up on the last call of the Web Advertising Business Group (sorry, I forget whether you were on last week's call). Building a look-alike audience seems like a very aggregation-friendly goal: you're looking for aggregate observations about your current customers, and then using those observations to build a large group of people to show ads to.

It seems to me that something should be able to meet this need count be built on top of the aggregation infrastructure we've been talking about for other APIs.

I'd be happy to extend TURTLEDOVE to offer more ways to build interest groups. The navigator.joinAdInterestGroup() in the current explainer is the simplest possible building tool, suitable for when you can identify probably-interest people based observed behavior on a single web site. Other ideas for ways to build audiences based on aggregation, not tracking individuals, are very welcome.

[benjaminsavage]

Thanks for tagging me here @michaelkleber. I am very interested in the use-case of "find the next 100k users" that @kaprasad has brought up. Helping businesses find new customers that will love their products is one of the best things ads can do.

As you mention above, I believe there definitely ought to be a privacy preserving solution for this use-case if we only concern ourselves with understanding the aggregate properties of the current customers of a business.

I just wrote up a proposal for how we might be able to extend Chrome's aggregation infrastructure to potentially serve this use-case. Take a look and let me know what you think:

Privacy Preserving Lookalike Audience Targeting

[BasileLeparmentier]

Hi,

Following these discussion, we put in place a proposal / explainer on interest groups. The idea is to create audiences using interest groups as building blocks in order to support many advertising use cases. Everything is done based on aggregation.
The proposal can be found here
https://github.com/BasileLeparmentier/SPARROW/blob/master/Interest_groups_audiences_new_building_blocks.md

What do you think about this proposal, meant to be further extended?
Best,
Basile

[michaelkleber]

So far I've been trying to understand the auction side of SPARROW more than the interest-group-building side! But as I said above, I'd be happy to extend TURTLEDOVE to offer more ways to build interest groups.

[BasileLeparmentier]

Hi Michael,

Did you have the time to have a look at it? It's I think quite quick and shouldn't be too controversial (I hope) and can be applied to TURTLEDOVE, SPARROW or another IG based mechanism.

I would really appreciate your feedback on this.
Best,
Basile

[michaelkleber]

Hi Basile, sorry I haven't had time to pursue this yet.

The part of your proposal (and what @kaprasad mentioned in the original post) about allowing unions, intersections, and complements of interest groups seems reasonable, as does the part about trees. These do get into some subtle questions in how it interacts with the minimum size of an audience you can target — note that there is some extra care needed since one interest group could have a bidding function which always outbids another interest group, so the size of an audience you're targeting is less obvious than you might think. But that can probably be solved.

The "mixed ownership" of the data behind these meta-interest-groups does raise some UX questions, though. Here's one example of why. Suppose I want to get out of an interest group, as in TURTLEDOVE's goal "People who wish to sever their association with any interest group with which they are associated can do so and can expect to stop seeing ads targeting the group." If it's a meta-interest-group, then it's not enough that I remove myself from that group, or even all groups owned by the same advertiser. It seems like I would need to get out of all of the underlying single-domain interest groups as well, and probably all groups on the same domains as those. Again, this can probably be solved, but the implications of letting some other domain build on top of one of your interest groups are probably larger than you expect.

Finally you talk about using the aggregated reporting API to report on various statistics for members of an interest group. We'd need some serious work to figure out whether this could be used as a way to circumvent the differential privacy protections that aggregate reporting otherwise offers. So all I can say on that question is that I'm definitely not sure.