There are many reasons Uber has been so successful. This article takes a look at some of the most commonly-mentioned reasons for Uber’s ridiculous growth, followed by a bit of a deeper look at a very large factor that is fairly easy to copy and, surprisingly, rarely gets a mention among the many “How Uber Grew So Fast” articles littering the web.
Common Themes when Talking About Uber’s Success
Firstly, here’s a handful we gathered from browsing through a couple of dozen “Uber’s Success” articles:
- Timing, and particularly “Early mover advantage”, having launched well before most of their various ride-sharing competitors. (founded in March 2009)
- Low additional cost for each new customer added. (ie. servicing 10,000 customers doesn’t cost them much more than servicing 1,000)
- Speed of expansion, driven by low need for physical presence. (they are now running in 100s of cities, across every continent outside of Antarctica)
- Simple referral marketing that results in low cost per new customer acquisition.
- Very strong lobbying & PR. (in one city in the US alone, they reportedly spent $475k in lobbying across a period of 5 months)
- Riding the wave of being the most well-known company in a movement (‘the sharing economy’).
- Clever positioning: A monopoly-beater, something that makes your life easier.
- Excellent app; strong focus on user experience. (at the time of writing there are 86 people listed on LinkedIn within their Product Design team)
- Universal applicability – almost everyone is a potential Uber customer.
- Cost: It’s cheap, relative to the nearest equivalent services.
Most of us would be able to list the above with a bit of thought, and each bullet point there has been written about many times over. In addition, many are tough to copy: “Early mover advantage” isn’t available to everyone; Lobbying and PR have a very high barrier to entry.
The Glaring Success Factor Nobody Talks About
Aside from the above, there is one very big lever to Uber’s success that is not often mentioned. It is simple, it is crucial to their success, and – unlike many of the above – any business can copy it. Every Uber customer has seen it as many times as they’ve used the service. It is simply this:
Those simple stars, combined with capturing open text feedback, are core to Uber’s success.
“The Stars” & Uber’s Strategy
At a strategic level, the review stars are the cornerstone of much of the following for Uber:
- Product quality & service quality assurance.
- Human Resource & talent management.
- Brand health.
The ‘rating collection’ is such a mundane part of the journey that most users will not even think about it, but for Uber it is core to their success. So vital they gather it twice for every single trip: Once for the passenger, once for the driver. And, if you forget to do it at the end of a journey, it will pop up again next time you open up the app.
Because of this simple function, and the data gathered from it, Uber can fix issues at all 5 levels of their service: Platform-wide, City- or Country-wide, Car-model-specific, Driver-specific, Trip-specific. It allows them to react to temporary problems, and to work to fix long-term issues.
Tactical use of review star data
At a tactical level, mining this data allows them to carry out lots of vital activity:
- Understand which of their drivers are good/bad. And understand which of their drivers refer good and bad drivers.
- Measure whether previously ‘bad’ drivers improve after intervention/additional training.
- Understand which ‘bad’ drivers have the potential to improve with training. (through historical analysis of the data)
- Spot patterns in service issues that can be removed across the whole Uber platform.
- Spot drops in satisfaction at a city-wide/country-wide level, to understand where issues are due to traffic or outside circumstance.
- Set expectations for customers: “Oh dear, this guy’s a 4.4. Ah well, I need to get home quick…” or “Hmm, 4.2. I’m booking for my kids, I think I’ll cancel & rebook.”
- Reward their best drivers: “This driver’s a consistent 5, let’s give them a bonus” (which, of course, they do)
- Pick drivers to help them launch in new regions, instantly starting with high satisfaction ratings among customers, and helping initial word of mouth to drive more bookings, and therefore create a larger market for additional drivers.
- Understand which types of trips frustrate/please their drivers more (“Our drivers seem very frustrated generally with short trips, let’s up the minimum charge to nudge customers away from that”).
- Stop their best customers from leaving: You normally rate ‘5’, but suddenly you drop to ‘2’ a couple of times? Even if you don’t provide any commentary, Uber can offer you a credit, or reach out to figure out the exact problem.
Following these principles with your business
You’ve no doubt realised much of this can apply to almost any online business. Of the above, from our data cross millions of transactions, we believe the following 4 elements of the above apply universally to anyone with more than a few dozen orders or bookings each month:
- Gather as many satisfaction scores as you can. Many review systems gather data on 5-20% of transactions – getting past 30% and beyond is transformational.
- Tie satisfaction data to individual customers where possible, along with their purchase history.
- Actually act on poor reviews at an individual level.
- Monitor for patterns: Do negative reviews come up time and time again for the same products? Or on the same devices? Or mentioning the same issues? Drill into that and fix those ‘pattern’ issues.
Much of this can be done without the need for third party tools, but if you’re interested you can see a little of how we do some of the above with Satalytics just here: