Waze: Redefining Carpooling
In spite of rising costs of car-ownership (>$8,000/year on average) and parking ($3,000/year on average), 80% of Americans still drive alone when commuting to and from work. This has led to a tremendous increase in road congestion, especially in major cities, resulting in 20-50% of additional time spent on roads during peak traffic hours.
Given this context, carpooling seems to make a lot of sense in tackling both costs to the individual and in addressing traffic congestion issues, with potential for environmental benefit. Additional incentives to carpool include monetization of an empty seat in the car and use of HOV lanes in some cities to get to work faster, resulting in cheaper and faster commuting.
But in spite of these incentives, carpooling hasn’t taken off in the US. In fact, carpooling has declined significantly as a mode of commute over the last few decades, as car ownership has increased. This is a result of the natural tendency of putting the individual’s convenience over the collective good (environmental benefit of reduced emissions and easing of congestion) and the concern of riding with a stranger. The only way a carpooling solution will work, is if it directly addresses the individual incentives of the members of the community without a significant cost of inconvenience to the individual.
A lot of products have tried their hand at carpooling solutions in the past. Not many of these have succeeded in their attempts, due primarily to the lack of driver density (leading to very limited potential of finding a relevant carpool partner and high detours for drivers which makes carpooling more expensive and inconvenient) and ease of finding a relevant a co-rider sometimes having to score through numerous ride requests (on Craiglist or Facebook groups for example), to find one that is relevant to your commute patterns.
These are the issues that RideWith, by Waze is looking to address head-on. Through an extremely sophisticated ‘matching’ algorithm that computes a value index for efficient matches based on the cost of detours, time-matching, reputation scores, etc. designed by renowned marketplace designer Michael Schwarz, that aims to only provide matches relevant to you as a user, based on your inferred commute patterns including commute routes and times (accessed from data acquired by Google), RideWith significantly reduces the noise in the user’s list of potential ride matches. By tapping into the existing strong network of Waze users, whose Waze versions will be upgraded to include this new ‘carpool’ functionality, RideWith smartly pre-populates one side of the 2-sided platform, to provide a reliable service through strong indirect network effects. With the demonstrated successes of ride-sharing platforms such as Uber, safety concerns associated with riding with a stranger in a car is becoming less of an issue. RideWith captures the value it creates, by charging a commission on each ride that is completed through the platform, which has in-app payment integration. One can argue about the risk of dis-intermediation, once a user has found a reliable carpool partner, but given the erratic and dynamic schedules of most workers today, RideWith looks to capture value pm;y only when one cannot reliably rely on a single carpool partner.
RideWith has been refined continuously over the last year based on feedback received through an internal ‘dogfood’ beta program with Googlers in the Bay Area as testers. RideWith is still in the initial stages, with only a small pilot currently launched in Israel that is being used to test out various hypotheses and refine the product to achieve a better product-market fit. But this product has great potential to change consumer behavior and get vehicles off the road. RideWith also aims to stay on the positive side of existing regulations, by ensuring the payments made are within the limits specified by the IRS. Some concerns still exist on the insurance and liability fronts, which RideWith needs to resolve before a more widespread adoption, but I am confident about the winning ability of this revolutionary product.
RideWith sounds really interesting! I was thinking of ways to do something similar a few years ago after learning about “slugging” in the DC area. In order to take advantage of HOV lanes with significantly less traffic into and out of DC, commuters will travel to designated “slug” stops where motorists will pick them up and take them to predefined destinations downtown. In the evenings, commuters line back up at designated spots downtown and are dropped off again at their starting points in the suburbs.
I’m glad to see that someone is trying to make something that there is clearly a demand for easier to manage. I am curious to see if, what seems like, a paid service like RideWith can compete with the free “slug” option in DC. Either way, I’m sure there’s a place for it in other, less established, carpooling areas.
Great post! Very interesting move from Waze and excited to see how it plays out – with an existing user base of ~50 million they should have a great platform for scaling fast.
Is interesting how car pooling is gaining users in the U.S. and could greatly help to reduce traffic and improve transportation efficiency. While the optimum transportation system would be a really good public transport, this clearly set us in the right direction.
More interestingly, I believe this market could be a huge new source of revenues for google if they monetize it correctly. With over 1 billion trips a day in the US, a $1 dollar fee per trip could easily match or even surpass the revenues of its core business.(http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/subject_areas/national_household_travel_survey/daily_travel.html)
I love the concept, and with Waze’s competencies in growth, I think they can definitely make this successful. I do have a concern about safety and privacy – users are generally ok with giving away endless data about their actions and movements to Google, but generally with the assumption that it will only be exploited by Google’s advertising and internal apps. This is the first time I’m aware of that this data is being shared with another user (the carpoolers you match with) and I think this has the potential to raise security and privacy concerns among Google & Waze’s users. They will have to be very careful about what information is shared across the two sides of the marketplace.