The Economics behind Uber's Surge Pricing
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Case Code: MKTG370 Case Length: 12 Pages Period: 2015-2017 Pub Date: 2017 Teaching Note: Available |
Price: Rs.400 Organization: Uber Technologies Inc. (Uber) Industry: Transportation Service Countries: United States Themes: Managerial Economics, Business Economics, Economics of Strategy, Consumer Behavior, Marketing Management |
Abstract Case Intro 1 Case Intro 2 Excerpts
Introduction
In June 2017, e-hailing ride share service Uber Technologies Inc. (Uber) came under fire for taking too long to turn off its "surge pricing" feature after a deadly terror attack in the heart of London. App users took to social media to complain about the inflated prices, saying that Uber
should have reduced prices immediately as people tried to get away from the area in the aftermath of the rampage. Uber’s controversial surge pricing model, which was designed to lure drivers to areas with high demand, was typically suspended during disasters and emergencies. But the company was criticized for not acting quickly enough in this case. "Big fan of Uber but bitterly disappointed in profiting from a terrorist attack. £7 Knightsbridge to Victoria. Charging £40,"wrote a user on Twitter, accusing the company of being disrespectful to the situation. However,
Tom Elvidge, general manger of Uber in London, said the company had suspended surge pricing within an hour of the attack and would refund all fares for riders in the affected areas following the attack.
Founded in 2009, Uber seamlessly connected rider to drivers through a smartphone app that used GPS technology. Uber's surge pricing, also called dynamic pricing, had been one of the most controversial aspects of the company's business model. The idea behind the surge pricing was that during periods of excessive demand when there were more riders than drivers, Uber increased its normal prices to encourage drivers to flood the zone. The idea was to put more cars on the road when and where they were most needed. However, surge pricing had long been Uber's Achilles' heel as customers felt it was complicated and equated it with price gouging. They felt that it took advantage of users in unfortunate situations. Going forward, Uber reportedly planned to use machine learning to improve its predictive algorithm for surge pricing, as a result of which the problem was expected to diminish with time...
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