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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

by lmkg 5y ago

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.

lostinthefield 5y ago
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-restricted-app-tracking-transparency-worldwide-us-daily-latest-update/ https://www.flurry.com/blog/ios-14-5-opt-in-rate-att-restric...)

by lmkg 5y ago

lostinthefield 5y ago

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-restricted-app-tracking-transparency-worldwide-us-daily-latest-update/ https://www.flurry.com/blog/ios-14-5-opt-in-rate-att-restric...)