Uber takes YOU for a Ride- Uses Machine Learning to Maximize the Fare

How desperate are you to get to your destination?  Very? Then be ready to pay a higher price for your Uber ride.
In the recent article in The Economic Times “Uber starts charging what it thinks you’re willing to pay, spoke about the new pricing mechanism of Uber, which they call “Route-based pricing”. Here, the pricing is arrived at using machine-learning algorithms to estimate how much a customer is willing to shell out for a ride. Uber calculates riders’ propensity for paying a higher price for a particular route at a certain time of day.
As long as the pricing depended only on routes at fixed times, it appears no different from a form of surge pricing, which is a well-accepted smart market clearing mechanism. But when the target is an individual’s desperation for a ride, things look far more sinister.
When you install the Uber app on your mobile devices, you voluntarily give Uber the access to all of your data- which includes your gender, social media profile, calendar, travel history etc. So Uber theoretically knows everything about you.  You could be a young woman waiting to get back home from a late night party in New Delhi, or you could be someone rushing to a hospital to visit your close ones or someone getting late to an official meeting- Uber could easily know that using Machine Learning and the data that you have willingly handed over. It can understand your level of desperation and prices the ride just below the level you are willing to pay. While you will the take ride, be sure have been taken for a ride!
In short, you end up paying more for the same ride than some other rider who is not as desperate as you are.
This in economics is called “First Degree Price Discrimination”.  The price is no longer determined by the Invisible Hand, which found the equilibrium between nameless and faceless multitudes of buyers and sellers- here this is the correct price but only for you, matched to your level of desperation.

Market of Many vs. Market of One

Earlier, we’ve all heard of Uber’s surge pricing tactic, which simply meant, during the times of high or excessive demand for rides (when there are more riders than drivers), Uber increases its normal fare.  At least, surge pricing was for the entire market (a group of customers).  We all hid behind the “Market of Many” till date, but this new “Route-based pricing” targets an individual rider, which is like a “Micro-Surge Pricing” leading to the “Market of One”.
Apparently, Uber drivers have been complaining about the widening gap between the fare a rider pays and what the driver receives. But why should they complain? What was their contribution in arriving at the “Route-based price”?  The value-addition in this case was from Uber’s data-driven machine learning algorithms that senses the customers’ willingness to pay.

The concept of personalized services was always understood for social welfare such as personalized medicine. But, in this world of data and machine learning, personalization has reached new heights. This new age personalization entails using our data against us for opportunistic pricing tactics, by platforms such as Uber. And this has nothing to do with societal welfare.
If Uber goes this route, others will follow.  Welcome to the dark side of this data-driven world!

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