Resource Type: Behavior Tendencies: , Related Tools: , , Domains: ,

Uber recently revamped its mobile app used by riders. Very interestingly, most changes are based on popular behavioral tendencies. Let’s use the ICAR Behavior Analysis Framework to classify the changes and their impact. Note: this is not meant to be an exhaustive analysis of the new UBER app, but rather I’m highlighting some interesting aspects of it in the context of an ICAR behavior analysis.

ICAR FrameworkDiscover the complete (and powerful) ICAR Behavior Analysis framework here (or click on the image to see an overview). You can use that framework to both analyze existing situations and plan product and services changes.

Incentives

Non-Monetary

  • Increased Ease: the app only asks for the destination, not the pick-up
  • Increased Ease: your contacts using UBER can be entered as destinations (if they agree via push notifications), so no more back-and-forth by text on where to meet
  • Less waiting: UBER will AI and calendar integration to suggest likely destinations you just have to confirm, and no longer type it
  • Increased Ease: the app will now direct riders to the closest pick-up spot that is convenient and provide walking directions to it. Note this may backfire: while the actual experience should be improved by meeting where the app directs you (no more driver circling around, etc.), for the persons who value no walking above all else, this may lead to decreased satisfaction and even create reactance if there is no option to set a “no walking” preference.
  • Save Time and Increased Ease: order delivery meals during the trip to arrive at your destination

Choice Architecture

Data & Info presentation

  • There are now much more refined animations used within the app, displaying the same info as previously displayed. This could go both ways, mostly depending on the wait time experienced until a ride is allocated, as this could lead users to feel like the animations are there to entertain them while waiting, and UBER should concentrate on cutting wait times, not polish animations.

Choice ergonomics and usability

  • Prices are now displayed on the same screen for all ride options, so you can compare them without back-and-forth between screens within the app.

Repetition

Reinforcers

Positive Reinforcers
  • Increased Partially Randomized Rewards: there is now an UBER feed that is displaying tips from Yelp and Foursquare. Both apps provide such rewards. I always advise my own clients to try and create such rewards within their customer journeys, but their product or service has to be amenable to that, and it’s not nearly always the case. What’s interesting about the new UBER app is that they looked for ways to create PRRs, couldn’t find one within their own experience journey, and then basically incorporated PRRs from other products in their journey.
Negative Reinforcers
  • Increased switching costs: for the contacts as destination feature to work, they must use UBER as well. Use a competitor and you lose that feature (only works because UBER benefits from huge network effects).
  • Increased switching costs: all the AI-based features, assuming they do improve in accuracy over time, makes switching to another riding app more costly as well, as you either lose AI-based features entirely or you start with an untrained AI.

Interested in more Behavioral Tendencies & Tools? Find them all here

2017-01-18T22:07:07+00:00

About the Author:

Julien Le Nestour
Applied behavioral scientist & international consultant — I am using the results and latest advances from the behavioral sciences—specifically behavioral economics—to help companies solve strategic issues. I am working with both start-ups and Fortune 500 groups, and across industries, though I have specific domain knowledge in banking, asset management, B2B and consumer IT, SAAS and e-commerce industries.

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