If you visit even the most primitive market in the most primitive 3rd world country, the sellers will have developed pricing effectiveness solutions that rival the best computer solutions a large company could hope for. You walk up to the stall and begin looking at the fancy hand-carved coconut and the seller tries to gauge your interest. Then he says something to further determine how much you might pay, he looks at your clothing and knows you are not a local. Finally, he gets you to ask him “how much”?
At this point, he quotes a price based not on his “cost plus expenses” but rather a price he considers ridiculously high but he would love to accept. In response, you counteroffer with a price that you consider ridiculously low. And now he knows, how much you value the product i.e. your “willingness-to-pay”. From just this little bit of information and what he sees, he knows how much he can squeeze out of you to maximize his profit. But none of those tactics work in modern-day e-commerce. So how do modern companies determine willingness-to-pay?
Revenue Management College Style
Back in the old days when I was in college, I took a Senior level “Management Engineering” class. In addition to learning things like “time value of money,” we studied pricing. For instance, suppose you have a plane that holds 400 passengers going from NY to LA.
You have 50 people willing to pay $1,000 for a ticket, you have 100 people willing to pay $800 for a ticket, you have 200 people willing to pay $600 for a ticket, you have 350 people willing to pay $400 for a ticket, and you have 1000 people willing to pay $200 for a ticket. How are you going to price your tickets?
If you sold tickets for $1,000 each you would sell 50 of them and make $50,000 but you would have 350 empty seats. That sounds pretty wasteful. At the opposite end of the spectrum, you could fill every seat (i.e. sell 400 tickets at $200) and still have plenty of people left over willing to buy at that price so you could consider running a second flight and you would earn $80,000 per flight (this is the pricing model some discount airlines follow). Or you could sell 350 tickets at $400 for $140,000. But what if you could determine how much people were willing to pay and charge them different prices?
| Ticket Demand | Tickets Sold | Price/Ticket | Total |
| 50 | 50 | $1,000 | $50,000 |
| 100 | 100 | $800 | $80,000 |
| 200 | 200 | $600 | $120,000 |
| 350 | Only 50 Tickets Left | $400 | $20,000 |
| 1000 | Sorry, No Tickets Left | $200 | $0 |
| Total | $270,000 |
In this example, if we can charge them each what they are willing to pay in a “tiered pricing model” we earn almost twice the amount of the next best option for a single pricing model. So as we can see, simply getting the highest price for your product or selling as many as possible, is not the way to optimize profits. It is getting everyone to pay the highest amount they are individually willing to pay.
Revenue Management Through Tiered Pricing
One of the key factors in optimizing profit was “tiered pricing”. Airlines are experts at this. They sell seats in 1st class for those who are willing to pay the most for a little bit of extra space and a few other perks. Then they sell a “Business Class” ticket and finally economy class.
But they even take it one step further. If you buy well in advance you get a discount and the airline gets to use your money for 3 or 6 months, plus they have assured a basic load on the plane (these are the people only willing to pay a low price). As time passes, the airline raises its prices so that during the period of peak demand prices are the highest. As the time of the flight gets closer one of two things happens, the flight gets close to being full or there are still quite a few seats left.
If the flight is almost full, the airline once again raises prices because if you need to get a last-minute flight you are willing to pay almost anything. But if there are lots of seats left, the airline might lower prices and list it as a special on a travel website in an effort to fill those seats with bargain hunters. The airline knows an empty seat will earn them nothing and so receiving something for the seat is better than nothing. So it is highly likely that the passenger sitting next to you on the plane paid a different price than you did, even though you will get exactly the same service. In modern-day jargon, this is called “pricing effectiveness”.
Pricing Effectiveness
One of the hottest new trends today is developing pricing effectiveness solutions. Good pricing effectiveness comes from determining a person’s willingness to pay. Companies specialize in “revenue management” by tracking a customer’s willingness-to-pay and helping a company to optimize its revenue stream based on that willingness. For instance, in the case of that airline passenger, one person absolutely needed to get somewhere at the last minute and therefore he was willing to pay almost anything for a seat on that plane. And the other person was looking for a bargain and only flew because the flight was cheap. Just like the vender in the stall in the 3rd world, if you could determine the person’s desire level, theoretically you could charge the first person a much higher rate while simultaneously charging the second person much less. Unfortunately for companies (but fortunately for consumers) companies are not mind-readers so they can’t squeeze the absolute last dollar out of you… Or can they?
What about eBay?
In an auction scenario that is exactly what happens, the person with the highest willingness-to-pay wins the auction. Another way of saying that is the person who wants or needs it the most gets it. That is why eBay has been so successful, it allows sellers to reach larger markets but also to get their product to the person who wants it the most.
But what about products that aren’t auctioned? The advent of high-speed computers and data mining has allowed companies to input massive amounts of sales data and use that data to calculate a customer’s willingness-to-pay. One of the factors that makes it tricky is that companies outside the auction environment only have “win-only” data. That means that they only know how much they actually sold the product for. They don’t know if the customer would have been willing to pay more. But by using data-mining techniques and massive amounts of price data (based on a product that has been marketed at different prices) they can plug it into fast computers and determine what prices people are willing to pay and thus optimize revenue management.
See Also:
- The Top 4 Most Successful European Airlines
- Monetary Mayhem From Myanmar
- Trends: Argentina’s Finances Going Bust Again?
- Risk Management in Trading
- The Greatest Wealth Transfer in History
Recommended by Amazon:
- Revenue Management– In Revenue Management, Robert G. Cross answers this question with his ground-breaking approach to revitalizing businesses: focusing on the revenue side of the ledger instead of the cost side. The antithesis of slash-and-burn methods that left companies with empty profits and dissatisfied stockholders, Revenue Management overturns conventional thinking on marketing strategies and offers the key to initiating and sustaining growth.
- Pricing and Revenue Optimization With methods proven in the MBA courses taught by the author at Columbia and Stanford Business Schools, this book presents the basic concepts of pricing and revenue optimization in a form accessible to MBA students, MS students, and advanced undergraduates. In addition, managers will find a practical approach to the issue of pricing and revenue optimization invaluable.
- The Theory and Practice of Revenue Management
- Networks, Crowds, and Markets: Reasoning About a Highly Connected World
- Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites
Image courtesy of Stuart Miles / FreeDigitalPhotos.net
