우버 가치 제안 - ubeo gachi jean

As a multi-sided platform business, Uber will benefit from segmenting both sides: the riders as well as the drivers. Depending on the purpose, Uber likely uses classic market segmentation as well as micro segmentation. Segmentation data can be used for various purposes, including targeting users with more specific/personalised offers, stimulating more frequent use, developing new products, etc.

Traditional segmentation methods

Let’s look at some ideas how Uber might apply traditional segmentation methods to their customers (riders) and their drivers.

Rider segmentation:

• Geographic:
  • Home location, destinations and frequent destinations, user location tracking
  • Urban / rural
• Demographic:
  • Age / age range
  • Gender
  • Life events (e.g. commuting to a new job until own car)
  • Occupation (e.g. frequent duty travel)
• Behavioural:
  • Loyalty: switchers, soft/hard loyals
  • Benefits sought: cost efficiency, convenience, etc
  • User status: Non-user, potential user, first-time user, regular user

Driver segmentation: Here are some categories that drivers could be segmented in. Again, the details of what Uber would use for which purpose remain confidential internal data. Here some plausible examples:

• Demographic: age, socio-economic status, family status, residency status / visa type
• Geographic: by city, suburb
• Geo-demographic: see above example
• Behavioural: preferred work hours & patterns
• Occupation: whether or not the driver has a(nother) occupation and what type of occupation and education
• Pro level: full-time driver with previous driving occupation or other
• Offerings served: UberX, Uber Eats, UberX Share, Uber Black, etc; part-time vs full-time (>30h/week), etc
• Check here how a consultancy segmented Uber drivers [pdf] (but note that Uber does not have all data listed therein)

Rest assured that Uber uses far more than the above traditional macro segments. And that’s what we are going to look at next.

Other

Here are some specific examples of data analyses that give us an insight of what type of data Uber has and what it could be used for.

Uber uses this insight for public relations (though critics could use it for exactly the opposite interpretation) but it can also be well used for targeting prospective drivers [source: Uber, retrieved 2018, link no longer active].

What’s more, it could be used to form a hypothesis. It could go from “In London, nearly a third of driver-partners live in areas where unemployment rates are highest” to something like “In large cities, an ‘overproportional’ share of driver-partners come from areas where unemployment rates are the highest”. It can then be verified for other cities and be used for various purposes.

Take the example of getting new drivers on board. So when Uber sends out the local start-up / scout teams that try to get drivers on board, they can use such insights for digital (i.e. digital ads targeting of respective suburb profiles) as well as direct the local teams to the right neighborhoods.

Another example for micro-segmentation is the Austin case study [2015]. It lets Uber conclude that “… people are relying on Uber to connect them to other modes of transportation.” Here, Uber tracks trips by proximity to train stations to conclude that “nearly 60% of trips are one-way, meaning people are relying on Uber to connect them to other modes of transportation.” Again, an interesting insight that can be used for various purposes.

It can be used for behavioural segmentation in that location and/or to form a broader hypothesis that could be verified, refined and applied to many similar cities and situations.

Among other purposes, it could be used for predictive routing of idle drivers in order to have an advantage over taxis who do not possess this info (some experienced drivers may have noticed certain patterns or developed a gut feeling but may not wish to share in order to benefit themselves from it).

As you can see, there are different types of benefits that segmentation / analysis of data can provide to Uber and other platform business models.

Note, how this is different to what you have seen above in the intro, i.e. traditional segmentation approaches. It shows how savvy innovators can use competitor’s habit of sticking with what’s known to gain an advantage.