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Posts Tagged ‘e-Hailing’

Surge pricing is a variant of dynamic pricing (also known as variable pricing). The dynamics of prices means that prices can now change much more frequently and vary across customers, time and place at ever higher resolution; a price surge or hike at peak moments in demand can be described as an outcome of dynamic pricing. Surge pricing received great attention due to Uber’s application of this strategy, and not least because of the controversial way that Uber implemented it. But dynamic pricing, and surge pricing within it, is a growing field with various forms of applications in different domains.

A price surge is generally attributed to a surge in demand. In the case of Uber, when the number of customer requests for rides (‘hailing’) critically exceeds the number of drivers available in a given geographic area, Uber enforces a ‘surge multiplier’ of the normal (relatively low) price or tariff (e.g., two times the normal price). The multiplier remains in effect for a period of time until demand can be reasonably met. The advantages, as explained by Uber, are that through this price treatment (1) drivers can be encouraged to join the pool of active drivers (i.e., ready to receive requests on Uber app), as well as  pulling drivers from adjacent areas; and (2) priority can be given to a smaller group of those customers who are in greater need of prompt service and are willing to pay the higher price. Consequently, waiting times for customers willing to pay the price premium will be shorter.  (Note: Lyft is applying a similar approach.)

There are some noteworthy aspects to the modern surge pricing. A basic tenet of economic theory says that when demand surpasses the supply of a good or service, its price will rise until a match is reached between the levels of demand and supply so as to ‘clear the market’. Yet the neo-classic economic theory also assumes that the equilibrium price applies to all consumers (and suppliers) in the market for a length of time that the stable equilibrium prevails; it does not account well for temporary ‘shocks’. Proponents of surge pricing argue that this pricing strategy is an appropriate correction to a market failure caused by short-term ‘shocks’ due to unusual events in particular places. There is room in economic theory for more complex situations that allow for price differentials such as seasonality effects or gaps between geographic regions (e.g., urban versus rural, central versus peripheral). Still, seasonal prices are the same “across-the-board” for all; and regions of different geographic markets are usually well separated. On the other hand, in surge pricing, and in dynamic pricing more broadly, it is possible through advanced technology to isolate and fit a price to a very specific group of consumers in a given time and space.

One of the concerns with surge pricing in ride e-hailing is that the method could take advantage of consumers-riders when they have little choice, cannot afford to wait too long (e.g., hurry to get to a meeting or to the airport) or cannot afford a price several times higher than normal (e.g., multipliers of more than 5x). The problem becomes more acute as surge pricing seems to ‘kick in’ at worst times for riders, when they are in distress [1a](e.g., in heavy rain, late at night after a party). The method seems to screen potential riders not based on how badly they need the service but on how much they are willing to pay. The method may fix a problem for the service platform provider more than for its customers. Suppose hundreds of people are coming out at the same time from a hall after a live music concert. If the surge multiplier shown in the app at the time the prospect rider wants to be driven home is too high because of the emerging peak in demand, he or she is advised to wait somewhat longer until it slides down again. How long should riders wait for the multiplier to come down? Often enough, so it is reported, it takes just a few minutes (e.g., minor traffic fluctuations). But in more stubborn situations the rider may be able to catch a standard taxi by the time the multiplier declines, or if the weather permits, walk some distance where one can hail a taxi or get onto another mode of public transport.

Another pitfall is reduced predictability of the occurrence of surge pricing. Consumers know when seasons start and end and can learn when to expect lower and higher prices  accordingly (though it used to be easier thirty years ago). In public transport, peak hours (e.g., morning, afternoon) are usually declared in advance, wherein  travel tariffs could be elevated during those periods. Since surge pricing is based on real-time information available to the service platform provider, it is harder to predict the occasions when surge pricing will be activated, and furthermore the extent of price increase. Relatedly, drastic price changes (e.g., due to high frequency of updates, strong fluctuations) tends to increase the uncertainty for service users [1b].

The extent of price surge or hike is a particular source of confusion. Users are notified before hailing a Uber driver if surge is on, and a surge multiplier in effect at that time should appear on the screen. The multiplier keeps being updated on the platform. It is sensible, however, for the multiplier to stay fixed for an individual rider after the service is ordered. Thus the rider can make a decision based on a known price level for the duration of the ride (or an estimate of the cost to expect). Otherwise, the rider may be exposed to a rising price rate while being driven to destination — but the rider should also benefit if the multiplier starts to slide down (or entering another area where surge is off). The first scenario resembles more a situation of bidding whereas the latter scenario looks more like gambling. Stories and complaints from Uber users reveal recurring surprises and unclarity about the cost of rides (e.g., claims the multiplier was 9x, a ride of 20 minutes that cost several hundreds of dollars, a claim the multiplier dropped but the total price did not go down in accordance). Users may not pay attention sufficiently to the multiplier before hailing a ride, do not comprehend how the pricing method works, or they simply lose track of the cost of the ride (i.e., the charge is automatic and appears later on the user’s account).

Discontent of customers may also be raised by a sharp contrast experienced between the relatively low normal price rate (e.g., compared with a standard taxi) and the high prices produced by surge multipliers [1c].  A counter argument contends that the price hikes or surges allow for low rates at normal times by subsidising them [2]. More confusion about Uber’s pricing algorithm could stem from reports on additional factors that the company might use as input (e.g., people are more receptive of surge prices when the battery of their mobile phone is low, and customers are more willing to accept a rounded multiplier number than a close non-rounded figure just below or above it (MarketWatch.com, 28 December 2017).

  • Not even a strategy of surge pricing appears to be completely immune to attempts of manipulation. It was revealed in 2019 that drivers with Uber (and also Lyft’s) have tried to game the surge mechanism. The ‘trick’ is to turn off the app at a given time in a coordinated manner among drivers, let the surge multiplier rise, and then turn on the app again to gain quickly enough from the higher rate as long as it prevails. The method seems to have been used especially at airports in anticipation of incoming passengers, based on the knowledge of drivers of several flights scheduled to land during a short interval. The motivation for taking this action: the drivers claim they are not paid enough at normal times by the platform operators (BusinessInsider, 14 June 2019).

Uptal Dholakia, a professor of marketing at Rice University (also see [1]), suggested four remedies to the kinds of problems described above. First, he advised to set a cap (maximum) on surge multipliers and notify customers more clearly about them (greater transparency). In addition, he recommended curbing the volatility of price fluctuations and communicating better the benefits of the method (e.g., reduced waiting times). Dholakia also raised an issue about a negative connotation of the term ‘surge’ that perhaps should be replaced in customer communications [3].

Various forms of dynamic pricing, including surge pricing, are already utilised in multiple domains. It is noted, for instance, that the strategy of Uber was not initiated to resolve problems of traffic congestion; ‘surge’ may be activated as its result but the purpose is to resolve the interruptions that congestion may cause to the service. For dealing with traffic congestion and overload in roads, other types of surge pricing are being used by public authorities. First, a fast lane is dedicated on a highway or autoroute (e.g., entering a large city) for a fee — the amount of ‘surge’ fee is determined by the density of traffic on the other regular lanes. Drivers who wish to arrive faster should pay this fee that is displayed on a signboard as one approaches entry to the lane (a few moments are allowed to decide whether to stay or abort). Second, a congestion fee, which could actually be a variable surge fee, may be imposed on non-residents who seek to enter the municipal area of a city at certain hours of the day.

As indicated earlier, public transportation systems in large cities may charge a higher tariff during peak or rush hours. The time periods that a raised tariff applies are usually declared in advance (i.e., they are fixed). Peak and off-peak rates may apply to different types of travel fares. The scheme is employed to encourage passengers who do not really need to travel at those hours to change their schedule and not further load the mass transportation system. There is of course a downside to this approach for passengers who must travel on those hours, such as for getting to work (employers who cover travel expenses should set the amount according to the cost of the more expensive rate). Using surge pricing in this case would mean that passengers cannot tell for certain and in advance when a higher tariff applies, but the scale of ‘surge prices’ can be pre-set with a limited number of ‘steps’, and thus reduce resentment and opposition.

Other types of dynamic (variable) pricing involve strong technological and data capabilities, including demand at an aggregate level and customer preferences and behaviour (search, purchase) at the individual level. A company like Amazon.com keeps updating its prices around the clock based on data of demand for products sold on its e-commerce platform. A more specific type of dynamic pricing entails the customisation of prices quoted to individual users-customers (i.e., different prices for the same book title offered to different customers). The approach maintains that a higher price could be set, for instance, for books in a category in which the customer purchases books more frequently and even based on search for titles in categories of interest. This form of price customisation is debatable because it aims to absorb a greater portion of the consumer’s value surplus (i.e., how much value a consumer assigns to a product above its monetary price requested by the seller), raising concerns of unfairness and discrimination. The risk to sellers is of making products less worthwhile to consumers to buy at the higher customised prices. (Note: Amazon was publicly blamed of using some form of price customisation in the early 2000s after customers discovered they had paid different prices from their friends; however the practice has not been banned and it is suspected to be in use by companies in different domains.)

  • Take for example the air travel sector: Airlines may use any of these methods of variable pricing: (a) Offering the same seat on the aircraft at different price levels (‘sub-classes’) depending on the timing of reservation before the scheduled flight: the earlier a reservation is made, the lower the price; (b) Changing fares for flights to different destinations based on fluctuations in demand for each destination and time of flight; (c) There are claims that airlines also adjust upwards the fares on flights to destinations that prospect travellers check more frequently in the online reservation system.

More companies in additional sectors are expected to join by applying varied forms of dynamic pricing. Retailers with physical stores are expected foremost to use dynamic pricing more extensively to tackle the growing challenges they face particularly from Amazon.com in the Western world (e.g., supermarkets will employ digital price displays that will allow them to change prices more continuously during the day and week according to visitor traffic levels). Restaurants may set higher prices during more busy hours at their premises, and hotels are likely to vary their room rates more intensively, taking into consideration not only seasonal fluctuations but also special events like conferences, festivals and fairs (e.g., see “The Death of Prices”, Axios, 30 April 2019).

Dynamic pricing, and surge pricing in particular, is the new reality in pricing policy, with applications getting increasingly pervasive. As technological and analytical capabilities only improve, the pricing models and techniques are likely to be enhanced and become furthermore sophisticated. Moreover, methods of artificial intelligence will improve in learning patterns of market and consumer behaviour, expected to enable companies to set prices with greater specificity and accuracy. At the same time, businesses need to take greater caution not to deter their customers by causing excessive confusion and aggravation. The question then becomes: What bases of discrimination — among consumers, at different times, and in different locations — would be considered fair and legitimate? This promises to be a major challenge for both enterprises that set prices and for the consumers who have to judge and respond to the dynamic prices.

Ron Ventura, Ph.D. (Marketing)

Notes:

[1a-c] “Uber’s Surge Pricing: Why Everyone Hates It?”, Uptal M. Dholakia, Government Technology (magazine’s online portal), 27 January 2016

[2] “Frustrated by Surge Pricing? Here’s How It Benefits You in the Long Run”, Knowledge @Wharton (Management), 5 January 2016. A talk with Ruben Lobel and Kaitlin Daniels at Wharton Management School at the University of Pennsylvania.

[3] “Everyone Hates Uber’s Surge Pricing — Here’s How to Fix It”, Uptal M. Dholakia, Harvard Business Review (Online), 21 December 2015

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An inspection of the mobile app-based services run by companies like Uber, Gett (formerly GetTaxi), Lyft or Hailo raises important issues about the distinction between taxi and non-taxi ridesharing transportation for individuals. Developments of the past two years seem to have broken the boundaries of driving-for-pay. A utility that started as an option for hailing licensed taxis by a mobile app is turning into an unruly “business” of private drivers. Uber in particular is the source of disruption in the ‘private transport’ sector that is causing much public controversy and protest by professional drivers.

The convenience in hailing a taxi using a mobile app when needed from anywhere a passenger may be in town, instead of hand-hailing in the street until a free taxi shows up is clear and undisputable. Through technology a taxi driver in vicinity is notified and can arrive without further human briefing to the passenger to pick him or her up for the ride — it can save precious time and nervous waiting. It may also help drivers reduce wasted time and fuel whilst wandering between “jobs” until a new customer is found. The applications in market were aimed originally to connect between consumers-passengers and drivers of taxis, black cabs and limousines.

A crucial aspect to notice about those mobile e-hailing applications is that they allow drivers to take customers independently of a present employer or a fleet they may belong to. Briefly, an app of this type incorporates geo-location, reservation management and credit payment functionalities so that a driver is related only with a passenger on one side and the technology company operating the app on the other side. No cash is actually passing between the three parties involved. This arrangement soon proved to have a potential to involve a larger variety of employed and non-employed, trade-licensed and non-licensed drivers.

For taxi companies or syndicates the problem is twofold. Professional drivers often depend on local fleet operators for their license, car and livelihood because of the companies’ strong influence over issuance of new taxi licenses. A license and an authorised (properly marked) taxi vehicle are harder to get without affiliation to a fleet. The new app utility may open a window for changing the balance of power between companies and upset professional drivers. Furthermore, affiliated taxi drivers may occasionally take passengers reached via the mobile app without going through the fleet, and perhaps even without notifying the dispatcher. The fleet operator risks here a loss of control, authority and income (grabbed instead by the tech company — Uber for example collects 20% of the fare).

Some co-operation between the mobile technology companies and local taxi operators and associations may still exist (e.g., authorised taxi vehicles in Israel can be seen carrying a sticker of GetTaxi) but that is less likely now, especially after Uber expanded the scope of drivers it would work with. That is, Uber opened the gate for private drivers, anyone who has a driving license and a car, to provide “private ridesharing” transportation. Subsequently, frictions with taxi fleet operators have expanded into hostile struggles with unions and professional associations of taxi drivers as well as city and state authorities.

Uber, Gett, Lyft, Hailo, and others similar to them, are designated as transportation network companies ; the networks are constructed, accessed and managed by means of mobile applications. The challenge mounted primarily by Uber is the expansion of the network to allow non-formal drivers to transport other passengers in their private cars for payment. The schemes of Uber for this “ridesharing” portion of their network are known in different countries as UberX, UberPop or UberPool. However, the descriptive title of ‘ridesharing’ for this activity is contentious.

Many probably know car-pooling from their time in college or university: students group together to transport to or from campus in the car of one of the group members and share the cost of fuel between them. Frequently the participants are friends or acquaintances (e.g., classmates, sharing the rental of a house) but at the very least they are connected by affiliation to the same institution. Due to rise in cost of fuel, traffic congestion and air pollution, car-pooling has become more prevalent also among working peers employed in the same organization (as among colleagues whose work is in City A and live in City B). We may see another form of saving on transportation by a small group of people who rideshare a taxi (e.g., for an evening out at a restaurant or on the way back home, dropping each at a separate address). These are usually voluntary and informal arrangements where people related to some degree either ride in the car of one of the group members or hire together a taxi with a professional driver. Uber tries to emulate both and yet enables none of these arrangements.

The term ‘ridesharing’ seems rather ambiguous the way Uber claims to implement it. Uber, Lyft, and others like them, present themselves as platforms for “peer-to-peer transportation”, not as passenger services. But who are the peers when ridesharing with Uber’s schemes mentioned above? There is no guarantee that the driver and passenger are “peers” or acquaintances of any kind nor is there any requirement that a group of people “share” the ride with Uber’s driver. The driver is not formally required to have the same destination as the passenger nor does the passenger’s destination need to be on the driver’s route anywhere else. In real terms, passengers are actually hiring a driver known to them only via the mobile app with none of the assurances that normally accompany the hiring of a licensed taxi driver. Administrative and legal authorities in various countries noticed that this operation occurs in grey area and started to suspend or ban such questionable schemes by Uber in different cities or countries.

  • Given the capital Uber has raised thus far it is valued as of May 2015 at $40bn. Uber operates in more than 250 cities in nearly 60 countries, though in some locations part of its activity is suspended due to legal disputes.

In the Netherlands Uber tried to argue that its UberPop scheme is a car-pool service as opposed to a taxi service. However, the Trade and Industry Appeal Tribunal in the country rejected this defence claim because it failed the legal requirement of taxi drivers to have a special license. The ruling on 8th December 2014 determined that “drivers who transport people for payment without a license are breaking the law“. UberPop was also banned a day after by a court judge in Spain because drivers lacked official authorisation to offer driving services. It should be noted that the cost of the ride is not estimated and agreed by the co-passengers with the driver to share but it is a fare determined by a third-party tech company. Moreover, people in a private, non-formal arrangement also usually do not deal with each other by credit cards. The claim of Uber sounds naive and unrealistic or simply a case of pretence.

By the end of 2014 Uber was dealing with additional legal restrictions and bans from the US (e.g., Portland, Orgeon; Nevada; and even in Uber’s hometown San-Francisco) and Canada (Toronto), through Europe (e.g., Paris, France; Berlin and Hamburg in Germany) to India (New Delhi) and Thailand in Asia. A map chart by The Telegraph depicts all the places where Uber is operating and where its activity has been banned or curtailed. The UberPop service is currently under scrutiny in Paris for failing a law passed late last year that regulates the services of chauffeured cars vis-à-vis taxis; Uber’s office in Paris was raided by police in March, confiscating mobile phones and documents (*).

In face of the rising criticism in Europe Uber adamantly argues that it is a technology company, not a transport company; thereby it does not own the vehicles nor hire any of the drivers who engage in its schemes, including UberPop. In the view of Uber, its mobile technological platform only helps in mediating between the drivers and passengers. Yet the European Commission is not hurrying to accept this argument, underlying Uber’s own complaints that national laws in Europe unjustly constrain its competition with taxi services. Uber may not directly transport people but its digital platform has an impact on transportation, a spokesperson for the EC commented in response. Indeed, can Uber defend itself as merely a technology company without taking any responsibility for the effects of its mobile app’s activity on the physical transportation services?  This matter is now under examination by the EC as part of an overall study of the taxi and chauffeur service industry.


The brand (corporate-root) name “Uber” (originally Über in German) is problematic on two counts. The company transmits through its name that it owns a superior transportation network. First, it is an arrogant claim that may be perceived as provocative particularly by licensed taxi drivers for being contested by Uber’s network of private drivers who operate above them and the rules of transportation service that confine them. Second, the name is quite insensitive because of the associations it may bring to mind that carry a strong negative connotation from the 1930s and 1940s. In addition, the attempt of Uber to justify themselves as only a technology company not responsible for the operation of transportation itself alerts one to think that the company is not inasmuch “Über Alles” (above all) as it is “Über Chuchem” (over-smart in Yiddish). The choice of name was not clever. It is already wide-spread around the world, but the name is tainted and may levy a price in future.

Uber and competing technology companies succeeded in introducing alternative private transport solutions for a reason: consumers who have become too frequently unsatisfied and even frustrated with the service delivered by licensed taxi drivers were open to the new type of “ridesharing” solutions. It may be triggered by complaints on low in-time availability, high cost and lack of reliability (e.g., not taking a shorter route, attempt to evade turning-on the taxi-meter). Sometimes it could be a feeling that the driver did not feel obliged enough to be kind to the passenger. Surely, many taxi drivers are honest, reliable and friendly, but the image of their service is tarnished by those who are less so. Obviously. the use of an e-hailing app is only part of the story here.

The main goal is protecting the quality and safety of taxi rides and other private chauffeur transport services. The business model offered by Uber is threatening to cause more damage than improve the situation — one cannot let these services be operated without oversight by official professional transportation agencies. There are some major concerns to address: (a) assuring the driving qualifications of the private drivers (though seeing how some authorised taxi drivers behave on the road makes one wonder also about their qualifications and the traffic rules they abide to…); (b) approving the physical fitness of the private driver; or (c) certifying the technical fitness of the private car used. Uber has already been charged with not making proper checks on the drivers who join them. Uber, though not alone, cannot be left to set its own rules for drivers.

The issue of cost and how ride-fares are measured is receiving special attention. Uber in particular has been charged with surge pricing (i.e., enacting a higher rate at peak hours when taxis are more difficult to get), a conduct other companies distance themselves from. More generally, the GPS-based assessment of fares is a subject of debate — is it accurate enough and can it be allowed in place of approved taxi-meters? According to Transport for London (TfL), fare calculations on smartphones are sufficiently divergent from taxi-meters’ for smartphones not to constitute a conventional metering equipment. Cab drivers in London went on strike to protest to TfL the special status of Uber, yet in a strange twist this stand may be used to protect Uber as a non-taxi service — TfL promised to investigate and resolve the conundrum. The fares at Uber are normally 15-20% lower than with licensed taxis. Passengers have to trade-off the expected saving against uncertain outcomes (e.g., reliability, safety) when riding with unauthorised private drivers. The decision may be different however if drivers applying Uber’s app were approved by official agencies.

A complex problem developed in multiple countries and is going to be difficult to sort out. In the short-term, taxi associations may collectively set-up or continue contracts with local mobile technology companies for operating sponsored e-hailing apps and guarantee 5-10% discounts to their users-passengers. In the long-term, it will be necessary to amend wounded relations between taxi drivers and consumers, and to consider if and how to accommodate different classes of authorised-licensed drivers, all kept under oversight of official professional agencies. Meanwhile, the mobile tech companies who probably sense the difficulties in the transport sector are already looking for new frontiers, to expand the use of their apps in other services (e.g., deliveries).

Ron Ventura, Ph.D. (Marketing)

(*) Uber Gets Reprieve in Paris in Fight on Low-Cost Service, International New-York Times (with Reuters), 1 April 2015.

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