A proposal to improve the pricing of surges


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Reduce fares in adjacent areas to attract more drivers where demand is high

In January 2020, a fatal shooting at an intersection in downtown Seattle sent passers-by scurrying for safety and a rush of requests for Uber and Lyft rides. Company algorithms have responded to the price spike with skyrocketing fares – a $ 35 trip suddenly cost over $ 200.

The technicians finally intervened and deactivated the automatic price increase. But less dramatic cases of price hikes have become a daily reality for customers who carpool when demand increases in one location – the end of a public event (before COVID-19) or the arrival of multiple flights to. the airport. As unpopular as they are, price increases are based on a fundamental economic principle: when supply and demand go haywire, raising or lowering prices will bring them back to equilibrium.

A paper Posted in Management science Omar Besbes of Columbia, Francisco Castro of UCLA Anderson, and Ilan Lobel of New York University are looking to offer a more efficient pricing approach that could better handle spikes in demand while increasing the profits for ridesharing companies.

The document suggests that companies respond to an outbreak by adjusting prices over much more of the city, and not just where the outbreak is occurring. By strategically increasing and lower prices, even far from the initial increase in demand, the model sends more drivers where they are needed and generates higher revenues than simply increasing prices, the authors say.

This holistic approach, the paper suggests, ends up offering lower prices at the peak location compared to a more local approach. The holistic approach brings more supply to the peak location, and when there is more supply, there is less need to increase prices.

Dealing with sudden and unpredictable changes in demand creates a problem that companies face despite their industry. Take, for example, call centers and hospitals: How do you make sure you have enough emergency room duty attendants or nurses to handle callers or patients? Plan too much and profits suffer; too little, and customers cannot get the service they want.

The carpooling model – drivers as independent contractors that Uber and Lyft simply offer rates for a given location and price – means that price is the main lever these companies have in adjusting supply. Hospitals and call centers, of course, actually employ workers and can schedule shifts to meet estimated demand for services.

So, rising pricing offers a simple solution to dealing with an unexpected increase in demand. Faced with a sudden influx of people looking for groceries, an algorithm automatically increases prices, sometimes multiplying them by two, three or more. Higher fares attract more drivers and encourage more price-sensitive riders to seek alternative means of transport until supply and demand balance.

As Uber and Lyft operate over large geographic areas, attracting drivers to one area can create shortages in others. Or, as the authors put it: “Prices set in one region of a city have an impact on demand and supply in that region, but can also have an impact on supply in other regions.”

With this observation, the authors divided the city into several regions around the source of the initial surge. In the area closest to the surge, prices increase sharply (but less than in conventional surge pricing) to encourage drivers to flood the area. A little further away is an area close enough to the center that drivers enjoy higher surge tariffs with little travel time.

Just outside of that region is another ring that’s just far enough away from the wave that drivers don’t automatically find it attractive to move. Here, additional persuasion is needed. The rideshare company sets prices here to discourage drivers from staying, usually lowering them to the point that no driver will want to respond to a request for a ride. Staying in the area no longer makes sense and drivers are looking for more profitable locations.

To prevent these drivers from traveling a way from the surge, the model also lowers the tariffs in the next surrounding area, so that area also becomes unattractive. Beyond that, the rates can remain unchanged because the drivers will be too far from the surge to move.

The result is that drivers who might be reluctant to head for the surge are discouraged from staying put and effectively prevented from heading any further in search of fares. Their only option is to join those flooding the surge area.

According to the document, when a large number of drivers react to an increase, ridesharing companies may see a “significant” increase in their revenues. “The platform can leave money on the table if it ignores the overall impact of its space pricing policy and supply response,” Castro said.

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About Chris McCarter

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