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Using Game Theory to Optimize the Pace of New Technology Adoption

Innovators of new technology systems requiring users to combine both hardware and software components often face delays in adoption of their new systems.  Users will not buy the hardware until enough software or content is available, while at the same time software providers will not provide content until enough users have adopted the new system.  This book examines the dynamics of this adoption process and provides methods for optimizing the pace of adoption of new technology systems.     Read more...

Price discrimination may be defined as selling the same thing to different people for different prices. Price discrimination can take many forms, such as volume discounts, price premiums, or market segmentation. Suppliers regularly use many different forms of price discrimination, which people generally don’t object to.

Some suppliers use dynamic pricing, a sub-category of price discrimination, in which prices change over time with market conditions. Consumers have been used to the fact that prices for airline tickets and hotel rooms change constantly, and that different people end up paying different prices for a seat on the plane. While uncomfortable with the practice, consumers have generally come to accept this type of dynamic pricing (what choice do they have?).

Over the past few years, dynamic pricing has become more widely used by sellers as a means of supplementing shrinking margins in an increasingly competitive world. As more information becomes easily available in digital form, pricing algorithms used to support dynamic pricing systems have been able to draw upon more and more information to hone prices and increase profits.

A more controversial sub-category of dynamic pricing is personalized pricing, which uses personal information on each customer to tailor prices specifically to that customer.

This analysis examines the different types of price discrimination, how they increase profits, why they are becoming increasingly prevalent, and some emergent issues surrounding their use.


Price discrimination occurs when a seller sells his product to different people at different prices. There are three basic categories of price discrimination.

1. First Degree Price Discrimination, or Personalized Pricing, occurs when a seller charges a different price to each buyer. This type of price discrimination requires the seller to have personal information about each buyer, such as his income or purchasing history.

2. Second Degree Price Discrimination occurs when sellers offer quantity discounts, that is they charge people who buy higher volumes lower unit prices.

3. Third Degree Price Discrimination, or Market Segmentation, occurs when sellers charge different prices to different groups of consumers. Examples include student or senior discounts, hardcover vs. paperback books, regular vs. premium gasoline, Canadian vs. US drug prices.

Dynamic Pricing occurs when sellers change their prices as market conditions (supply or demand) change. There are two general categories of dynamic pricing.

1. Revenue Management is driven by changes in demand and is commonly used by airlines and hotels to price seats and rooms.

2. Supply-Driven Dynamic Pricing is driven by changes in supply, and is commonly used in ecommerce and retail, such as when sellers cut prices to match rivals with lower prices or increase prices to account for rivals running out of stock.

Generally speaking, dynamic pricing involves a time element, where prices can change from minute-to-minute as market conditions change. Second and third degree price discrimination generally don’t change over time (so frequently), so they don’t generally fall into the category of dynamic pricing. However, there can be an overlap between dynamic pricing and price discrimination in the case of personalized pricing, if prices are tailored to each buyer using both personal information as well as market information. Figure 1 provides a visual relationship between price discrimination and dynamic pricing.

Figure 1

discrim v dynamic

Is Price Discrimination Legal?

Price discrimination is legal, unless it is used either (i) to decrease competition or (ii) to discriminate against protected classes. More explicitly, price discrimination is illegal when:

• It’s used to try to decrease competition, “such as tying the lower prices to the purchase of other goods or services.” From Price Discrimination Law and Legal Definition:

Price discrimination is made illegal under the Sherman Antitrust Act. 15 U.S.C. §2, the Clayton Act, 15 U.S.C. §13, and by the Robinson-Patman Act, 15 U.S.C. §§13-13b, 21a, when engaged in for the purpose of lessening competition, such as tying the lower prices to the purchase of other goods or services.

• It’s used to discriminate against protected classes, defined by Wikipedia as

Race – Civil Rights Act of 1964

Color – Civil Rights Act of 1964

Religion – Civil Rights Act of 1964

National origin – Civil Rights Act of 1964

Age (40 and over) – Age Discrimination in Employment Act of 1967

Sex – Equal Pay Act of 1963 and Civil Rights Act of 1964

The Equal Employment Opportunity Commission interprets 'sex' to include discrimination based on sexual orientation and gender identity

Pregnancy – Pregnancy Discrimination Act

Citizenship – Immigration Reform and Control Act

Familial status – Civil Rights Act of 1968 Title VIII: Housing cannot discriminate for having children, with an exception for senior housing

Disability status – Rehabilitation Act of 1973 and Americans with Disabilities Act of 1990

Veteran status – Vietnam Era Veterans' Readjustment Assistance Act of 1974 and Uniformed Services Employment and Reemployment Rights Act

Genetic information – Genetic Information Nondiscrimination Act

Why Are Variable Pricing Schemes So Controversial?

People often object to price discrimination and/or dynamic pricing on the basis of ethical and/or privacy concerns. Volume discounts and market segmentation are generally not so controversial. However, people don’t think it’s fair when prices increase because it’s raining or snowing outside or because they live in a house in a nice part of town. It also makes people anxious when they realize that sellers know which genre of books they like or what their favorite restaurant is.

The younger generations who have grown up with the internet seem more comfortable with exchanging information about themselves in exchange for better matches of product offerings or lower prices. As Sandy Skrovan reports in “Despite expectations of personalization, most shoppers don't want to share data,”

In breaking down the responses of our latest survey, Retail Dive discovered that men are more open than women to sharing their personal data with retailers, as well as consumers under the age of 45 — as long as there’s something in it for them.

For consumers who don't mind handing out personal data to their favorite retailer, the primary reasons for doing so are twofold: 61% say they want loyalty program points and rewards in return, and 61% expect discounts and other special offers.

But trading information for a more personalized shopping experience doesn’t hold as much appeal as retailers may think. Less than a third of shoppers willing to share information (31%) say they would do so for personalized recommendations.

Challenges to Implementing or Profiting from Differential Pricing

Sellers’ face several challenges to their ability to implement and/or profit from differential pricing. As The Obama White House reports in “Big Data and Differential Pricing”),

  • Willingness to Pay: Sellers must figure out what customers are willing to pay. This can be a complex problem, even for companies with lots of data and computing power.
  • Competition often limits a company’s ability to raise prices, even if it knows that one customer might be willing to pay more than another.
  • Arbitrage: Sellers must be able to prevent resale by customers seeking to exploit price differences.
  • Customer Alienation: A seller must be careful not to alienate customers who may view personalized pricing as inherently unfair.
  • Worst Scenario from Social Standpoint: differential pricing seems most likely to be harmful when implemented through complex or opaque pricing schemes designed to prey on unsophisticated buyers.
  • Best Scenario from Social Standpoint: In a competitive market with transparent pricing, the benefits, in terms of enabling lower-willingness-to-pay customers buy products, are likely to outweigh the costs of potentially harming unwitting buyers.

Why now?

Airlines and hotels have been using dynamic pricing with consumers for decades. Why is it that just now over the past few years more sellers are considering using it? Using pricing algorithms to create differential pricing for customers requires sellers to have access to lots of data about buyers’ purchasing habits. And even then, there used to be several relatively restrictive conditions under which a dynamic pricing scheme would enable sellers to increase profits. In “Answer these 2 questions to know if dynamic pricing will work for your business,” Alex provides 5 constraints that have traditionally determined whether or not sellers could profit from dynamic pricing:

1. Relatively fixed capacity

2. Relatively predictable demand

3. Perishable inventory: A limit time to sell the good before the good is not longer available. For example a seat on a particular plane is only available until that plane takes off.

4. Fixed costs that are relatively significant compared to variable costs

5. Demand that varies by time, such as by hour of the day, day of the week, or month of the year

Industries that have traditionally met these requirements include, for example, airlines, hotels, car rental agencies, and vacation resorts.

However, two recent changes have made the use of dynamic pricing potentially profitable for many more types of sellers:

(i) The advent of the digital economy, and with it, big data, and

(ii) The ability of sellers to use big data analytics to generate meaningful information from vast amounts of data.

As the Obama White House notes, it is now possible for many more sellers to track or get (buy) access to:

  • A user’s location via mapping software
  • A user’s browser and search history
  • Whom and what users “like” on social networks like Facebook
  • The songs and videos users have streamed
  • A user’s retail purchase history
  • The contents of a user’s online reviews and blog posts.

Furthermore, as James F. Peltz notes in “Why 'dynamic' pricing based on real-time supply and demand is rapidly spreading,”

Advances in computer power, demand-tracking sensors, software with pricing algorithms and other technologies have made it easier for companies and government agencies to forecast how demand for their products and services will change and how quickly.

Because of the ability of sellers to access and analyze more information on more different types of customers, Alex suggests that the five traditional constraints on when discriminatory pricing might be profitable reduces down to only two:

1. Does demand vary by time?

2. Will consumers accept dynamic pricing?

So Alex suggests that as long a seller’s demand varies by time and he won’t lose too many potential customers who don’t like the use of dynamic pricing, then implementing a discriminatory pricing scheme has the potential to increase that seller’s profits.

Market Responses to Sellers’ Use of Dynamic Pricing

Several different responses by consumers and service providers have emerged to thwart attempts by sellers to use dynamic pricing.

Consumer Responses

A. Erase Cookies

Sellers and service providers install cookies on your computer in order to track your online activities and collect personal information. Perhaps the easiest way to hide personal data from buyers is to erase any cookies that sellers have installed on your computer.

B. Hide Your Identity.

In “3 Tricks to Help You Snag the Best Deals Online,” Ismat Sarah Mangla suggests that buyers “be a secret shopper.”

Be a secret shopper

You could delete your browser’s cookies—“clear browsing history” in the settings menu—before you shop. But this may erase info that could help you in the pricing wars (a shoe e-tailer, for example, may market better deals to someone who often shops at Zappos).

So first try opening a “private” window on Firefox or an “incognito” window on Chrome or turn on “private browsing” in Safari, all of which let you surf without saving cookies. That way, you can compare the prices a retailer offers when it doesn’t know who you are with those it offers when it does.

Also, use multiple browsers or devices…

Similarly, in “Online price discrimination exists. Here’s how it’s evolving,” Daniel Herman suggest that “consumers can use a VPN service that will camouflage their online activities.”

C. Play Hard to Get

Ismat Sarah Mangla also proposes that consumers “play hard to get,” where “hesitating on a purchase shows your willingness to go elsewhere and may get a retailer to sweeten the pot.”

D. Use Friends and Family

Daniel Herman suggests that “comparing the price of products with your friends and family could help the consumers to come up with a solid benchmark.”

New Products and Services

Several different services have emerged online to help consumers find and respond to prices on the internet.

A. Monitor Prices

Several providers offer services that enable buyers to monitor prices on the internet, including “Our free Amazon price tracker monitors millions of products and alerts you when prices drop, helping you decide when to buy.”

The Tracktor: “unearthing the internet’s best prices”

Price!pinx “allows you to get price drop alerts for any product on almost any retailer's website”

B. Price Comparison Search Engines

There are many price comparison search engines on the internet. The Consumer Reports top pick is PriceGrabber.

C. Price Comparison Apps

There are also many price comparison apps available. PC Mag’s top pick is the Amazon app.

Issues Surrounding the Use of Dynamic Pricing

How to Win over Consumers

We’ve seen that many consumers don’t like dynamic pricing and that a seller who uses dynamic pricing runs the risk of facing a backlash by customers. However, there are a couple of things that sellers can do to appease customers and establish trust while using dynamic pricing.

A. Provide Transparency

One of the things people don’t like about dynamic pricing is it’s lack of transparency, in two different respects.

First, sellers often are not up-front with customers in admitting that they use dynamic pricing. This leads customers to lose trust in the sellers, because they think the sellers are trying to cheat them into paying higher prices.

And second, even if customers know that sellers are using dynamic pricing, they don’t like not knowing where they stand relative to other buyers. More specifically, the behavioral economics literature (see, for example, Timothy J. Richards, Jura Liaukonyte, and Nadia A. Streletskaya, “Personalized Pricing and Price Fairness”) shows if people think they’re paying less than most other people, then they’re okay with that, but they don’t think it’s fair if they think they’re paying more than most other people. The term for this phenomenon is "self-interested inequity aversion."

The implications here are that sellers can try to regain trust with consumers (i) by being open with them about the fact that they’re using dynamic pricing, (ii) by specifying how prices are determined, and (iii) by perhaps providing a price distribution so people can see where their personalized price fits within the distribution.

B. Provide Personalized Experiences

Another thing sellers who use dynamic pricing can do to regain the confidence of consumers is to appeal to the “75 percent of shoppers [who] want a personalized experience” (Angelica Valentine “Will online retailers use personalized pricing for good or evil?”). More specifically, sellers could use dynamic pricing within a broader scheme to provide personalized experiences to customers. That is, sellers could create personalized value for customers that’s worth the personalized price.

Slippery Slope

The use of personalized pricing creates a slippery slope for sellers. When is personalized pricing too personalized?

  • Is it ok to charge someone wearing nice clothes a higher price for a restaurant entree?
  • Is it ok to charge someone wearing blue clothes a higher price for new blue garments?
  • Is it ok to charge someone who has bought all an author’s titles a higher price for that author’s newest release?
  • Is it ok for Apple to charge a customer who has an iPhone, an iPad, and AppleTV a higher price for his iMac?

Algorithm Interactions

In 2016, a group of scholars, Le Chen, Alan Mislove, and Christo Wilson, published a study on the use of dynamic pricing algorithms by sellers on Amazon, “An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace.” Aside from Amazon, who uses dynamic pricing, only a small portion, 2.4%, of the non-Amazon sellers currently uses dynamic pricing. However, based on all the other information from other media sources, it is clear that the use of dynamic pricing by both online and off-line sellers has been increasing.

It appears that the most common algorithms used are price-matching algorithms, in which sellers either

1. Match or beat the lowest price offered by competitors, or

2. Set a price that incorporates a premium over the lowest price,

where the price premium algorithm is used by sellers who have established a reputation for quality, such as a large sales history with 5-star ratings.

With more and more sellers utilizing dynamic pricing algorithms, it won’t be long before we start seeing instances of product pricing gone awry. Algorithms by different sellers will increasingly interact (bid against one another) and come up with unexpected – not to mention unintended – results.

The two most obvious unintended outcomes – race to the bottom, and race to the top –have already been documented.

A. Race-to-the-Bottom

During the 2014 Christmas season, various Amazon third-party sellers didn’t have a very Merry Christmas. Sellers who employed a dynamic pricing algorithm created by RepricerExpress found themselves on-the-hook when the pricing algorithm “erroneously changed the price of thousands of items to as little as one penny or one cent.” Many sellers found themselves liable for huge numbers of sales to customers at a penny apiece. Some sellers had understanding customers who agreed to invalidate the sales. However, other vendors lost hundreds of thousands of dollars when customers forced the vendors to fulfill their orders. Chris Baraniuk provides some detail in “The bad things that happen when algorithms run online shops.”

…in what Green calls a “race to the bottom”. Two retailers selling the same thing on Amazon’s marketplace will re-price their product against their competitor, but the re-pricing can occasionally continue unabated until absurdly low or high price points are reached.

This was what blighted the Christmas sales of a string of Amazon marketplace sellers in December last year, when automated re-pricing software RepricerExpress erroneously changed the price of thousands of items to as little as one penny or one cent. The glitch, which happened two weeks before Christmas, hit sellers running small retail businesses extremely hard.

B. Race-to-the-Top

In another case of a battle of algorithms, Michael Eisen documented what happened with two algorithms using a price premium strategy tried to out-bid each other. From “Amazon’s $23,698,655.93 book about flies”:

Figure 2

lawrence prices1

Source: Michael Eisen (April 22, 2011), “Amazon’s $23,698,655.93 book about flies”

Both profnath and bordeebook were clearly using automatic pricing – employing algorithms that didn’t have a built-in sanity check on the prices they produced. But the two retailers were clearly employing different strategies.

The behavior of profnath is easy to deconstruct. They presumably have a new copy of the book, and want to make sure theirs is the lowest priced – but only by a tiny bit ($9.98 compared to $10.00). Why though would bordeebook want to make sure theirs is always more expensive? Since the prices of all the sellers are posted, this would seem to guarantee they would get no sales. But maybe this isn’t right – they have a huge volume of positive feedback – far more than most others. And some buyers might choose to pay a few extra dollars for the level of confidence in the transaction this might impart. Nonetheless this seems like a fairly risky thing to rely on – most people probably don’t behave that way – and meanwhile you’ve got a book sitting on the shelf collecting dust. Unless, of course, you don’t actually have the book....

My preferred explanation for bordeebook’s pricing is that they do not actually possess the book. Rather, they noticed that someone else listed a copy for sale, and so they put it up as well – relying on their better feedback record to attract buyers. But, of course, if someone actually orders the book, they have to get it – so they have to set their price significantly higher – say 1.27059 times higher – than the price they’d have to pay to get the book elsewhere.

C. Emergent Results

Sam Schechner reports in “Why Do Gas Station Prices Constantly Change? Blame the Algorithm” another instance in which pricing algorithms did the unexpected.

Algorithmic pricing works well in the retail gasoline market, because it is a high-volume commodity that is relatively uniform, leading station owners in competitive markets to squeeze every penny.

One client called to complain the software was malfunctioning. A competitor across the street had slashed prices in a promotion, but the algorithm responded by raising prices. There wasn’t a bug. Instead, the software was monitoring the real-time data and saw an influx of customers, presumably because of the long wait across the street.

“It could tell that no matter how it increased prices, people kept coming in,” said Mr. Derakhshan.

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