Closing Case 2

Personalized Pricing (also known as Dynamic Pricing)

The Problem

Today, consumers are accustomed to standardized pricing, which means that when a product is sold via multiple channels, the cost should not vary by more than the difference in shipping, taxation, and distribution costs. If the price is higher for a product at a certain retailer, then customers can easily use the Internet to compare prices and features among a huge number of retailers to purchase that product from another retailer. In theory, charging all consumers the same price is ineffective for merchants, because some customers would have been willing to pay more, and others who opted not to buy would have bought at a lower price. Economic theory states that personalized pricingalso called dynamic pricingcan save companies this lost revenue.

Personalized pricing is the practice of pricing items at a point determined by a particular customer's perceived ability to pay. This practice has been in existence as long as commerce itself. Consider shopkeepers in the past, who were experts on evaluating prospective customers based on a wide variety of signalshow they spoke, how they dressed, how they acted (e.g., did they look the shopkeeper straight in the eyes), how courteous they were, how much of a hurry they were in, and many other variables. In that way, the shopkeeper could quickly decide on a price for that customer at that time. The optimal outcome of personalized pricing for the merchant is maximizing the price that each customer will pay. This situation is difficult to achieve because the means of determining the maximum amount a customer will pay has not yet been developed.

Or has it? The Solution (for the Merchant) Some online retailers (e-tailers) are attempting to return to the strategies of yesterday's shopkeeper. They are analyzing the data , that all consumers generate with online activities and transactions to set different prices for different customers. E-tailers can now virtually assess each customer who visits their Web site.

Specifically, when a customer accesses an e-tailer's site, the merchant may know where the customer is located based on his or her Internet Protocol address. Merchants also may know the customer's ZIP code.

In that case they can determine the customer's socioeconomic status based on data from the most recent federal census. When merchants combine these data with cookies, they can learn a significant amount about individual customers, including some of the Web sites the customers have visited, how regularly they have visited them, how long they stayed on those sites, and which products they inspected and purchased. Further, cookies store information that customers volunteer in online forms for example, shipping addresses and other profile data. Based on these data, merchants can predict the products a customer is interested in purchasing, when he or she is likely to purchase them, and, critically, the price he or she would be willing to pay. By analyzing customer data, a merchant can estimate customers' reservation pricethe maximum amount they would be willing to pay for a specific product, before they had reservations about buying itand then charge them that amount. Currently, many retailers use these data to target individual shoppers with personalized offers and promotions.

The analyses that enable retailers to send customized offers to customers also enable them to determine personalized pricing. In addition, with e-commerce, merchants can easily adjust prices for different customers simply by changing them in the system in real time. They therefore avoid the hassle of physically changing the prices on thousands of products. Let's review some prominent examples of personalized pricing. According to The Wall Street Journal, Orbitz used its knowledge of its customers' demographics to charge certain customers more for hotels. Orbitz discovered that users who browsed on Apple Mac computers were willing to pay up to 30 percent more for a hotel than Windows users. Delta Airlines uses personalized pricing to charge frequent flyers more than they charge infrequent travelers.

The reason was that people who travel often are probably doing so out of necessity, most likely for business purposes. They therefore are willing (however begrudgingly) to pay more to get where they need to be. Safeway, the grocery chain, sends offers to select customers who have exhibited certain purchasing patterns. For example, one woman received an offer for discounted eggs because the data revealed that her household purchased quantities of high-protein items. With personalized pricing, Safeway can afford to sell the eggs to this customer at a lower price because the store will charge less health-conscious customers more for the same product.

In 2000, Amazon was found to be charging its regular customers higher prices for some products, after one shopper deleted the cookies on his computer that identified him as a regular Amazon customer and noted that the price of a DVD dropped. Amazon, which attributed the differences in price to a random price test, refunded customers who had paid higher prices. In addition, Amazon CEO Jeff Bezos asserted the company never will test prices based on customer demographics.

Staples shows customers different prices based on a range of characteristics that could be discovered about the user. For instance, one customer saw a lower price for a computer on the company's Web site than another customer who lived a few miles further from the store. Staples' reasoning was that if someone is already close to the store, then he or she may be enticed to make a quick drive to purchase the discounted item.

The National Football League (NFL; ) is allowing teams to use personalized pricing for tickets.

The Seattle Seahawks became the first team to employ the practice. The team worked out a deal with ticket brokers to redistribute 4,000 season tickets. Half of the tickets were sold as individual tickets to fans on the Blue Pride wait list. The remaining tickets are subject to personalized pricing the higher the demand at the moment of purchase, the higher the ticket price.

Liftopia uses personalized pricing in the ski business. In the past, a day on the slopes cost the same regardless of when it was purchased. Liftopia works with ski resorts to crunch data on historical and real-time supply and demand to vary pricing. Customers who buy tickets early receive discounts. Liftopia raises prices as the ticket's date approaches.

The company's pricing appeals to skiers who might not visit a resort based on the rising cost of lift tickets. At Mammoth Mountain Ski Area in California, for example, Liftopia's personalized pricing contributed to a 15 percent increase in advance lift ticket sales during the 2014-2015 ski season. Companies such as Wiser, Dunnhumby , and Blue Yonder offer software and data solutions to retailers that employ personalized pricing, where prices change over time in response to variables such as inventory, demand, and/or the prices offered by competitors. Blue Yonder claims it can optimize prices not only according to the region but also to the channel in which the customer is interacting with the retailer.

The Results Most companies remain hesitant to utilize personalized pricing because it remains to be seen whether consumers will accept the practice. Typically, when consumers hear about the practice, they react negatively, and companies employing the practice experience customer dissatisfaction. It is not easy for consumers to detect when they are being targeted with personalized prices. For luxury brands in particular, personalized pricing could be damaging because it raises questions concerning the intrinsic value of their products. For example, brands such as Louis Vuitton do not discount their products so as not to undermine the consumer perception of their value. Consumers of luxury goods frequently associate higher prices with higher quality, and vice versa.

THIS FOR INFORMATION TECHNOLOGY MANEGEMENT COURSE, PLEASE ANSWER BOTH IN DETAILS, QUESTIONS:

1) Pricing can be dynamic based on factors not related to an individual. For example, Liftopia varies pricing based on the time a lift ticket is purchased such that tickets purchased in advance are discounted more. Describe another market / industry where pricing of a product is based on when the purchase is made and consumers have accepted this practice.

2) Lets assume an energy utility company personalizes prices based on each residential customers ability or willingness to pay based on median income of the zip code, ownership status of the residence, assessed property value for local taxes, and energy consumption habits. As such, customers in poorer neighborhoods, renting inexpensive homes, and using less energy relative to the size of the home (e.g., kWh / ft2 ) pay lower rates than customers in richer neighborhoods, owning high priced homes, and consuming more energy relative to the size of the home. defend this practice of personalized pricing against standardized pricing (i.e., everybody pays the same rate) under any three of the four ethical frameworks. Please note that defending this position does not imply that you subscribe to it, it merely demonstrates your ability to apply the ethical frameworks to this case from this perspective.