The incentives of online dating service companies are ridiculously misaligned with their users’. (For users who are looking for a monogamous, long-term relationship.)
A “match” between two users that results in them both leaving the platform for good is a super-negative outcome with respect to the metrics that the company is probably optimizing for. They probably use machine learning models to decide which “candidates” to show a user at any given time, and they are incentivized to train these models to avoid matches that cause users to leave their platform for good. (And these models may be way better at predicting such matches than any human).
I think this is looking at obvious incentives, and ignoring long-term incentives. It seems likely that owners/funders of platforms have both data and models of customer lifecycles and variability, including those who are looking to hook-up and those who are looking for long-term partners (and those in-between and outside—I suspect there is a large category of “lookey-lous”, who pay but never actually meet anyone), and the interactions and shifts between those.
Assuming that most people eventually exit, it’s FAR better if they exit via a match on the platform—that likely influences many others to take it seriously.
Assuming that most people eventually exit, it’s FAR better if they exit via a match on the platform—that likely influences many others to take it seriously.
Why is this true? Is there any word-of-mouth benefit for e.g. Tinder at this point, which plausibly outweighs the misaligned incentives ofer points out?
I don’t know much about their business and customer modeling specifically. In other subscription-based information businesses, a WHOLE LOT of weight is put on word of mouth (including reviews and commentary on social media), and it’s remarkably quantifiable how valuable that is. For the cases I know of, the leaders are VERY cognizant of the Goodhart problem that the easiest-to-measure things encourage churn, at the expense of long-term satisfaction.
[Online dating services related]
The incentives of online dating service companies are ridiculously misaligned with their users’. (For users who are looking for a monogamous, long-term relationship.)
A “match” between two users that results in them both leaving the platform for good is a super-negative outcome with respect to the metrics that the company is probably optimizing for. They probably use machine learning models to decide which “candidates” to show a user at any given time, and they are incentivized to train these models to avoid matches that cause users to leave their platform for good. (And these models may be way better at predicting such matches than any human).
I think this is looking at obvious incentives, and ignoring long-term incentives. It seems likely that owners/funders of platforms have both data and models of customer lifecycles and variability, including those who are looking to hook-up and those who are looking for long-term partners (and those in-between and outside—I suspect there is a large category of “lookey-lous”, who pay but never actually meet anyone), and the interactions and shifts between those.
Assuming that most people eventually exit, it’s FAR better if they exit via a match on the platform—that likely influences many others to take it seriously.
Why is this true? Is there any word-of-mouth benefit for e.g. Tinder at this point, which plausibly outweighs the misaligned incentives ofer points out?
I don’t know much about their business and customer modeling specifically. In other subscription-based information businesses, a WHOLE LOT of weight is put on word of mouth (including reviews and commentary on social media), and it’s remarkably quantifiable how valuable that is. For the cases I know of, the leaders are VERY cognizant of the Goodhart problem that the easiest-to-measure things encourage churn, at the expense of long-term satisfaction.