Personalized Pricing (also known as Dynamic Pricing)

Today, consumers are accustomed to standardized pricing, which means that when a product is sold through 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?

Some online retailers (e-tailers) are attempting to return to the strategies of yesterday's shopkeeper. They are analyzing the data (see Chapter 5) 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 website. 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 (see Chapter 4), they can learn a significant amount about individual customers, including some of the websites the customers have visited, how regularly they have visited them, how long they stayed on those sites, and which products they inspected and purchased. Furthermore, cookies store information that customers volunteer in online formsfor 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. And, 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.

Delta Airlines (www.delta.com) uses personalized pricing to raise ticket prices for frequent flyers. The rationale was that frequent flyers are probably doing so because they have to travel, usually for business trips. They therefore are willing (however unethusiastically) to pay more than infrequent travelers.

The Safeway supermarket sends offers to certain shoppers based on their buying history. For example, one customer received a coupon for eggs because the data revealed that she bought regular quantities of high-protein items. With personalized pricing, Safeway can afford to sell the eggs to this customer at a discount because the store will charge less health-conscious shoppers more for the same product.

In 2000, Amazon (www.amazon.com) 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. Amazon CEO Jeff Bezos also asserted the company never will test prices based on customer demographics.

Staples (www.staples.com) shows customers different prices based on a range of characteristics that could be discovered about the user. For example, one shopper saw a lower price for a computer on the retailer's website than another person who lived farther from the store. Staples's reasoning was that if someone lives close to the store, then he or she may be lured in to make a quick purchase of the discounted item.

The National Football League (NFL; www.nfl.com) lets teams use personalized pricing for tickets. The Seattle Seahawks (www.seahawks.com) became the first team to employ the practice. The team partnered with ticket brokers to redistribute 4,000 season seats. Half of the tickets were sold individually to fans on the Blue Pride wait list. The rest of the tickets are sold based on personalized pricingthe higher the demand at the moment of purchase, the higher the price.

Companies such as Wiser (www.wiser.com), Dunnhumby (www.dunnhumby.com), and Blue Yonder (www.blue-yonder.com) 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 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.

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.

Questions:

1 How would you feel if you knew that you were being subjected to personalized pricing?

2 Describe the advantages of personalized pricing for merchants.

3 Does personalized pricing provide any advantages for customers? Provide examples to support your answer.

4 Discuss the contributions of information technology to the practices of personalized pricing.