Network effects in online dating
The more the merrier?
Platform Papers is a blog about platform competition and Big Tech. The blog is linked to platformpapers.com, an online repository that collects and organizes academic research on platform competition.
Written by Jessica Fong.
It’s often thought that digital platforms owe much of their success to the magic of network effects, in which the value that a user derives from the platform depends on the number of other users on the platform. However, the nature of these effects can vary, and their impact on the platform can be either positive or negative, depending on the type. Cross-side network effects are typically thought to be positive: the value of the platform grows as the number of users on the opposite side of the market increases. Think of a marketplace like eBay: as more sellers join and list products, the platform becomes more attractive to buyers because they have a greater variety of items to choose from. Similarly, as more buyers join, sellers see greater potential for sales, making the platform more appealing to them as well.
On the other hand, same-side network effects, which refer to the impact of having more users on the same side of the market, are generally considered to be negative. For example, in a job search platform, more job seekers might mean stiffer competition for each job posted, potentially discouraging new job seekers from joining.
“Online dating platforms provide a very interesting setting to study network effects.”
Online dating platforms provide a very interesting setting to study network effects. There is an ongoing debate about whether having more users on the other side of the market is beneficial for platform users. On the one hand, a larger pool of potential matches means there is a higher likelihood of finding a compatible partner, which is a classic example of positive cross-side network effects. On the other hand, having too many choices can not only be overwhelming but can also make it difficult for users to commit to any single match due to the "grass is greener on the other side" mentality (for an example, see here).
Measuring cross-side network effects can be tricky because the sides of the market are inherently connected; if more women join a dating platform, more men are likely to join as well, making it challenging to isolate the effect of more women from the effect of more men. In a recent paper published in Marketing Science, I measure how the number of same-side and cross-side users impact user behavior by implementing a field experiment that is designed to solve this challenge. I collaborated with an online dating platform, where users view other users’ profiles one at a time and decide whether to “like” the current profile before moving on the next. Here, matches occur only if two users mutually “like” each other.
The experiment I conducted addresses the problem of disentangling an increase in one side from a simultaneous increase in the other by shifting users' beliefs (via information) about their market and competition sizes. It does so without changing the actual number of platform users, which is often costly or perhaps infeasible to experimentally manipulate. Users in the experiment saw a message like “There are at least 3,500 men and 4,300 women nearby”. However, different users saw different values of the number of men and women. The key feature of this experiment is that the numbers were randomly assigned (but are close to the true value); this randomization ensures the numbers were believable to the users, but also included variation that was not correlated with other characteristics that might correlate with the user’s behavior on the platform, such as their gender or location. This variation is what enabled me to measure the incremental impact of beliefs about the number of men and women on the platform. I can then observe how this treatment impacts whether the individual uses the platform (i.e., views other users’ profiles) and their behavior on the platform (i.e., which users they “like”).
The study yields the following main results:
More choice attracts new users but deters experienced users. When the number of potential matches increases, users who had viewed more profiles and sent fewer “likes” before the experiment are significantly less likely to use the platform, while new users are more likely to use the platform. These effects sum up to an average effect of more choice reducing platform usage.
More choice makes users more selective. They "like" fewer profiles overall, and they tend to "like" more attractive users’ profiles.
More competition makes users less selective. They tend to “like” less attractive users’ profiles.
Why might more choice impact platform usage? Result #1 rejects the idea that choice overload, where too many options makes decision-making difficult and leads to less motivation to choose, is the sole explanation. It doesn’t explain why new users are more likely to use the platform. Here are some explanations that better fit the experiment’s findings:
“Those who are pickier might expect to exert even more effort and thus be less likely to participate.”
First, although more options means that there is a better match somewhere out there, it might take more effort to find them, which ultimately might make search not worth the effort. Those who are pickier might expect to exert even more effort and thus be less likely to participate.
Second, an influx of new platform users can signal something about the types of those users. For example, a surge in new user profiles might indicate a recent marketing campaign or a trend that attracts a particular demographic, which influences user expectations about the quality and compatibility of these matches. Experienced users may notice the increase in new users, while new users may not, which can explain their different behaviors.
A likely explanation for how the number of potential matches and competitors impacts who users “like” is that it impacts users’ beliefs about how likely they are to find a match. Users may believe that when they have more competition, their profiles are less visible to other users. As a result, they “like” profiles that are more likely to lead to a match. On the other hand, more potential matches means more opportunities to match, so users are more willing to take a chance on someone who is less likely to “like” them back.
“Although this study focuses on one online dating platform, it demonstrates that network effects are more nuanced than “more users = more value.” Rather, the size and direction of network effects can depend on users’ beliefs and expectations and are subject to behavioral biases.”
The findings from this study provide a case study of an instance in which the standard assumption that users derive more value from the number of users on the other side of the market is not true. Or at least, users do not expect to derive more value. Although this study focuses on one online dating platform, it demonstrates that network effects are more nuanced than “more users = more value.” Rather, the size and direction of network effects can depend on users’ beliefs and expectations and are subject to behavioral biases. In addition, for platforms to grow effectively, they must balance cross-side and same-side network effects, which implies understanding how each of these forces impact user behavior. The experiment design used in this study provides a cost-effective way for platforms to measure these effects.
This blog is based on Jessica’s research, which is published in Marketing Science and is included in the Platform Papers references dashboard:
Fong, J. (2024). Effects of market size and competition in two-sided markets: Evidence from online dating. Marketing Science.
Platform Papers is curated and maintained by Joost Rietveld.