Fitbit is Peer Pressuring People into Buying Their Product and It’s Working
I’m not a huge fan of consumer wearables. In fact, I think most of them are kind of stupid, because they rarely have a unique use case and the redundancy drives me crazy. Like why do I need to wear something on my wrist to count my steps when my phone does that for me already and I take my phone everywhere? But then, all of my friends had Fitbits and things changed. Suddenly, there were multiple discussions on our group texts about who was winning the step count for the week and inside jokes about the struggle to always be moving (even in the bathroom). And, I’ll admit, I felt left out. So much so, that I am considering buying one.
The fact that Fitbit was (almost) able to convert me is a testament to the strength of their direct network effects, which is illustrated in the graph below by their exponential growth in two years from 1 million units sold in 2012 to over 10 million in 2014.
With 70% of the fitness wearable market, Fitbit grows stronger with every new user for two reasons. First, as aforementioned, more users translates to more new users via word of mouth recommendations and invitations to compete or reach goals together. This is extremely likely to happen with a product like Fitbit, where the density of users is important because people tend to compete with close friends and family. Second, more users provides Fitbit with more data. This data can be sold to other fitness brands to help them segment the market and sell their products. Data can also be used internally by Fitbit to create new and better products, which they have already begun to do by introducing a whole product line of devices to meet different needs.
Fitbit reinforces its direct network effects with indirect ones via an open API. Now, they can create as much value as possible with other existing fitness platforms. This is a great way to convert users, because switching costs are no longer an obstacle for them. The ease of integration makes Fitbit not only attractive in B2C markets, but B2B as well with entire companies signing up as clients for their employees.
It’s true that Fitbit will face competition from companies like Apple with more money to spend on R&D, but Fitbit’s market position and strong network effects will make it difficult for others to take the lead. As we learned, in an industry with strong network effects, such as this one, the only way to increase utility is to increase quality. Therefore, the best strategy a new entrant can take to beat Fitbit is to provide a more superior product. However, with the many years of data Fitbit has at its disposal as a first mover, it will be hard for a competitor to outmaneuver them.
Student comments on Fitbit is Peer Pressuring People into Buying Their Product and It’s Working
This is an interesting example of how important it is to consider how direct network effects map onto users’ social “graphs”, and of how different products can have different critical thresholds for their network effects.
To the first point, as you mention, Fitbit tries to use competition with friends and family as a key motivator for people to buy their product and continue interacting with it. However, individual users are unlikely to feel the direct network effects until they actually have friends and family using the product. In other words, if 10 random people in the country are using Fitbit, they’re unlikely to be my connections, so I have little motivation there to adopt the product. Even at 10,000 random people, I might still have no motivation if my friends and family aren’t represented in that pool, but perhaps connections that are 1 or 2 degrees away from me have adopted the product and are slowly spreading the network effects towards my networks. So at this point, it’s entirely possible that the direct network effects have zero impact on my decision to buy, while another user might see a disproportionate number of their network using the product, and thus feel very strong direct network effects. This parallels the case with Facebook’s growth: when Facebook was just a Harvard undergrad product, there was no reason for somebody from Stanford to adopt it.
This also influences the second point, which is that different products have different thresholds for their direct network effects. For crowd-sourced knowledge platforms, the impact of an additional user who contributes an additional article or review (e.g. on Wikipedia or Yelp) directly benefits everyone else. For something like the telephone, the effect of a random additional user joining is weaker but still potentially valuable, to the extent that I sometimes have to reach out to “random” people or businesses. And finally, for something like Fitbit or Facebook, a random new user probably has close to zero effect on most people outside of that user’s immediate network, and so the threshold needed for direct network effects to occur is probably higher.
I wonder to what extent FitBit really benefits from network effects, in that I’m not sure if the product actually gets better with more users.
Fitness goals are deeply personal, and the one size fits all goal of 10,000 steps would also seem to be inappropriate for many users.
So I wonder how vocal most users are about reaching their goal, and even if they did share, if that would actually motivate others more or not.
Each person has their own circumstances, and I’d venture to say it would be more effective for a user to compete with his or herself versus competing with others.
Fitbit has been around for quite some time, so they seem to have an edge in terms of market share. But as the incumbent I am interested to see how they should and shift from product-centric, to platform-centric.
The wearables space is so crowded, especially with Xiao Mi’s insanely low price and grabbing 50% of the market share in just one year (just after 2014 where the graph doesn’t go), it seems like the product is becoming a commodity. One could even argue that Xiao Mi actually has a bigger network than Fitbit.
Fitbit has it’s own database of data that it allows researchers to use, and this may be an area where they can turn to a platform that allows either researchers, marketers, or other health/fitness focused professionals to plug into their database to extract insights.
I would agree FitBit is capitalizing on network effects, but I question the solidity and longevity of these effects. I question how many people regularly use the FitBit over longer periods of time, after the initial novelty wears off. To really have lasting network effects, FitBit would need to be something enduring, and involve ingraining a habit in the end user. For example, how many people really track their sleep patterns with this product? Once you have the data on how much you are sleeping, will you do anything with that information?
I think the network effects we are seeing with FitBit are people wanting to wear the product and show off, rather than actually use it. If this is the case, once the novelty wears off and it’s a thing of the past, the initial network effects will disappear.
Interesting take on a product that was initially designed to be very personal (asking you for sensitive data such as your weight or tracking pretty much everything you do day to day). First, I have to disagree slightly with your opening: I don’t think it’s redundant to your phone (after all, you don’t play squash or basketball with your phone on you, and that is exactly when you “clock in” the most steps); in fact, part of its beauty, relies on the fact that it is highly COMPATIBLE with your phone, making tracking of virtually everything so easy (weight, move goals, sleep, calorie balance etc). Second, even though I agree that network effects played a large role in effectively CREATING this market, I would argue they are not company-specific but rather industry-wide. Fitbit created the market for Jawbone and others to enter. Given the number of apps compatible with all available health tracking products, as well as the general ease of use of the software associated with each of them, I think that the switching costs for customers are relatively low. That suggests to me that the initial network effect that perhaps fueled the growth are not strong enough to keep it going in itself.
I think that FitBit has network effects similar to other fashion product in the sense that seeing other people wear it creates brand awareness and social desirability. Moreover having more and more people buy it creates a situation where the investment in the technology increases and other brands compete for the attention of the growing bigger market.