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Home » The Secret of How to Cheat in Pricing and Get Away with It

The Secret of How to Cheat in Pricing and Get Away with It

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How to creatively individualize pricing to what each customer is willing to pay to maximize revenues.

If you’ve bought a product recently, and you’re like most people, your first instinct was to go online to check prices to get the best deal possible; but how can you tell if you got the best possible price? After you did your research, imagine if the seller had a telepathic ability, and you were able to tell exactly what each of their customers (including you) — individually — was willing to pay for a product or service and then offered that product or service to each customer separately, at the maximum price that each customer would be willing to pay for it: no more, and no less.

Historically, this theoretical optimal pricing strategy was attainable only by monopolies. Now, it is becoming increasingly closer to reality for even the most competitive of markets, including travel and food. The reason this pricing strategy is still theoretical is that no one can know for sure what a human being is willing to pay as price due to multiple factors that play into the purchasing decision, including cognitive bias and circumstances beyond any individual capability of knowing. Or is it?

In this article, I’ll be discussing how combining technology and big data with creative pricing is gradually closing the gap on this theoretical maximum pricing strategy.

Importance of Pricing Strategy

Consider the following scenario: Say Dunkin, which operates 12,400 restaurants, were to raise the price of coffee — of which they sell 2 billion cups a year — by $0.15. What would that do to their bottom line? If units sold were to remain the same after such an increase, this could add around 10% of net income straight to Dunkin’s bottom line. However, the volume of units is a function of price, and we cannot assume that the number of units sold will remain constant with such a price increase. There are too many factors that should be considered to determine how a price increase would influence consumer purchasing behavior and its impact on sales. This includes factors such as the price of coffee at Dunkin’s competitors, the price of substitutes, brand loyalty, consumer perception, and much more.

From airline tickets to mobile phones, pricing is the most sensitive variable with an impact on the profitability and growth of companies. This is why, when we develop scenarios for future projections for our client’s organization, pricing (in conjunction with units sold volume) is always a required variable in Monte Carlo simulations or sensitivity analysis.

Welcome to the realm of price personalization: differentiation, discrimination, and individualization that lead to dynamic price setting.

Price Personalization, Discrimination, and Individualization

Last year, I was looking for a rental car for my trip to Athens during the peak holiday season of August. If you’re like me, you wouldn’t settle for one price quote. Instead, you’d be trying multiple websites, including aggregators, metasearch engines, and agents like Expedia, Momondo, and Kayak, and the home pages of rental car companies like Hertz, Avis, and National.

I finally found an acceptable deal and booked a car for myself. Since we were two families traveling together, I gave the source to my friend who tried to book the same car but — to my surprise — he got the same car from the same rental company, only 30% less! At first, I thought that might be because the price went down since I booked it due to market fluctuation; so, I went in to recheck the car price, but my price for the same car for the same period appeared unchanged!

I knew that sellers continually change their prices in response to market conditions and may use shoppers’ personal information to charge different people different prices depending on factors such as ZIP codes, search histories, etc. But I never imagined that the price could vary by as much as 30% in a competitive market like car rentals and airline tickets. So how do they get away with it? Welcome to price discrimination at scale in a competitive market.

Price Discrimination

Price discrimination — also called price differentiation — occurs when a firm sells the same product at different prices, either to the same or to other consumers. The study of this strategy comes naturally when dealing with monopolies, as these seek to sell additional output to consumers without lowering the price of the units that are already being sold to maximize their profits.

Traditionally, micro economists define three levels of price discrimination:

  1. First Degree Price Discrimination, also referred to as perfect discrimination, is when you conduct every sale at the maximum perceived price that a buyer is willing to pay and has the highest payoff to companies that are able to achieve this.
  2. Second Degree Price Discrimination, sometimes referred to as nonlinear pricing, occurs when a company offers consumers a quantity discount. The amount paid per unit of sale depends on the number of units purchased in a specific period of time, similar to volume discounts. Thus, the term nonlinear, as volume discount is applied based on the consumers’ choice of volume purchase.
  3. Third Degree Price Discrimination often referred to as price segmentation (this is the most common approach), is where different consumer groups are typically identified and segmented. Then, they’re priced according to each segment in that market to maximize the price paid by each segment. So, naturally, higher prices are paid by those segments where the elasticity of demand is lower and vice versa.

Good examples of price discrimination are public transportation, utilities, cable, etc. To optimize price discrimination, firms will have to control and prevent reselling, and they will also have to sort consumers depending on their willingness to pay. The former does not generally imply complications, but sorting consumers is a more complex process. There are several approaches that sellers can use to tap into these levels in competitive markets.

Auctions as Price Optimization Mechanisms

Auctions are one method for tapping into the maximum of what individuals are willing to pay. The auction mechanism of eBay is set up so that every bidder submits their maximum price — also known as a Vickrey auction, in which it is optimal for the bidder to reveal their maximum price. However, eBay does not sell its own products; it is a marketplace where buyers and sellers trade, similar to most auctions that occur worldwide (from Sotheby’s to cattle, horses, and stock markets). Furthermore, if you take the auction and bidding contest approach, just because a price was paid in an auction does not imply that the final and winning bidder would not have paid a higher purchase price for the goods received. The highest price was offered because no one else in the pool of bidders was willing to pay more.

Micro economists argue that a key requirement to achieving the First Degree of price discrimination is that the seller must have market power that is monopolistic in nature. However, the auction example at scale proves otherwise.

One little-known auction business model is called a bidding fee auction or penny auction; this is a type of all-pay auction adopted by the likes of DealDash and Quibids. The model works and achieves a greater price than the theoretical first-degree price discrimination for commodity products. These sites can command a much higher price than the actual market price. Based on the Nash equilibrium in game theory, the theoretically expected pay-off in an all-pay auction should be zero. However, a paper in the Journal of Economic Behavior shows that over-bidding on an all-pay option is very common, which is why these auction sites are profitable.

This shows that price maximization can sometimes be achieved by adopting creative pricing methods without needing to be a monopoly in your market.

Optimized and Personalized Pricing in Competitive Markets

You may think that price personalization only happens online — as in only when the seller has information about you or your device that is stored in the cloud in order to personalize your price. However, creative methods achieve similar effects without you being conscious of them.

Consider for a minute what happens when you walk into a flea market or try to buy a car or a house. Sellers always gauge you by looking at the car you drive or the clothes you wear before naming their price. This concept — which is closest to price discrimination — if adopted at scale, can help companies that operate in competitive or commoditized goods and services achieve the same and beat their competitors, assuming, of course, that the profits are also being maximized.

But what if you didn’t know who the buyer was? Merchants and retailers have found other creative ways to increase price personalization. For example, if you walk into Starbucks, you’ll notice the price of black Freshly Brewed Coffee, depending on where you live, is around $2. But what happens if you add milk to that? The coffee gets a fancy name, and the price almost doubles (depending on how fancy the name is). The addition of some milk to your coffee adds $1–2 dollars may be a mystery to you, but that’s a creative method of price discrimination. Adding options to the base product could easily double the price, from coffee and mobile phones to luxury cars. In 2022, the price of an entry — no option — level Porsche 911 was just below the $100,000 mark. But if you went for the full option, Turbo S convertible, the price jumped to $220,000.

While pricing was difficult to achieve in a commoditized market in the past, the rules have changed through personalization. Nowadays, large corporations with access to big data and complex analytics can make that distinction, and many corporations are already doing it. In particular, the travel industry has come a long way in achieving almost perfect price discrimination.

Modeling allows companies to use pricing as a powerful profit lever, often underdeveloped. Price Optimization Models can tailor pricing for customer segments by simulating how targeted customers will respond to price changes with data-driven scenarios. Given the complexity of pricing and thousands of items in highly dynamic market conditions, modeling results and insights help to forecast demand, develop pricing and promotion strategies, control inventory levels, and improve customer satisfaction. Furthermore, Price Optimization Models should factor in three critical pricing elements: pricing strategy, the value of the product to both buyer and seller, and tactics that manage all elements affecting profitability.

Be a Price Leader and Not a Price Manipulator

While advancement in technology makes price optimization feasible and accessible, businesses should not make prices their main strategy for growth. Price optimization should only be adopted as a strategy for maximizing the potential revenue from a saturated product or service in a highly competitive market.

It is recommended that businesses focus on maximizing optionality as a key driver for sustainable growth. In that case, adding price personalization to an already winning offer can only accelerate profits.


About Us. (2019). Dunkin’.

Even More Joy: 2019 Impact Report. (2019). Dunkin’.

Gneezy, U., & Smorodinsky, R. (2006). All-pay auctions — an experimental study.

Journal of Economic Behavior & Organization61(2), 255–275.

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