Comment by Sephr | Hacker News Reader

Comment by Sephr | Hacker News Reader

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Hi, I'm Eli, the lead engineer behind this project. We're on a mission to put an end to cookie banners so we built a new kind of consent manager at Transcend to achieve this goal. In this post, you can read about the process that went into building a secure firewall for our consent manager.

This product is currently in closed beta, so I am sure you may have many questions. I will try to answer any questions you have in the comments.

With Transcend Consent Manager, site owners can:

• Block, quarantine, & replay tracking events cross-session or in-place (e.g. a same-session DOM mutation is replayed or a fetch() promise is resolved).

• Override requests to use alternative domains or enforce privacy rules on parameters, skipping the SDK. (e.g. we can automatically enforce Facebook's LDU parameters, Google Consent Mode, Google Ads RDP, YouTube Privacy Enhanced Mode, etc. with no site changes)

• Privately sync consent & quarantine data across a first-party set of hosts without data ever leaving the browser.

Most importantly, this enables sites to ask for consent later in the user journey, eliminating cookie banners.

The problem that I see with your approach is that you are heavily biasing the collected samples towards "successful" flows.

If you have two funnels A and B and users using path A convert slightly more than users using path B, but path A is way more hidden. If you end up only showing the collect prompt after the conversion, you will get a lot of data about path B, and some about path A. It's really hard to tell the efficiency of each path, you now that. more people come from path B, but you don't one which one is best.

I hate cookie banner, but from a data collector perspective, since they are the first thing people interact with, you are argue that they minimize sampling bias. You approach adds a significant one, which makes all the number you can get from it hard to trust.

This consent flow solves one specific problem very well, which is Return on Ad Spend. This addresses the data needs of one stakeholder, which is the marketer in charge of the ad and marketing budget. Of the dollars they're spending, which ones result in sales? That's the one question they want to ask from their data, and this tool lets them answer that.

Every other stakeholder I can think of will want to know details about a failure or drop-off. Some stakeholders, like User Experience, will only want to know about failures. This tool does not meet their needs.

My experience is that the user base of Google Analytics skews heavily towards marketers, and there is a decent-size market of current GA users that only use GA for the subset of data provided by this tool.

It doesn't necessarily solve RoAS tracking either. If you only have information on some % of your purchasers* and you optimize against that small, self-selecting sample, you may entirely miss other groups of higher spenders who just don't want to be tracked.

It might help answer the question "which campaigns are performing better relative to each other" as long as we assume that no campaigns are more likely to affect opt-in rates than others. It may even be an improvement over up-front consent seeking, if the theory is that a better checkout UX with less upfront annoyances results in better opt-in rates later on. But it doesn't really answer the bigger question of "is this the best place to spend my marketing dollars" vs, say, investing in campaigns/platforms that may have greater reach but less transparent funnels (comparing a Google Ad to a Superbowl TV ad, for example).

If you use this in a vacuum without any statistical modeling thought, you end up optimizing against algorithmic limitations rather than real customers. At the end of the day you're drastically reducing your sample size, and not in a representative, random manner. Your opt-in users are behaviorally different from the majority of people, and it wouldn't be a good idea to make big decisions based on their actions alone. They are essentially a cheaper focus group, with all the pros and cons of such.

(*Probably a very low percentage, if the recent iOS tracking opt-in is any indication. One analysis says <15% of users opt-in: https://www.flurry.com/blog/ios-14-5-opt-in-rate-att-restric...)

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