One strategy I came across in my interviews is email polling. This involves sending say 1000 emails/flyers A/B testing different prices to a list of potential customers and measuring the acceptance rates.
There are two versions of this test:
- Ask people which price they will buy at.
- Test which price people will buy at.
… then select the price at which profit is highest.
One benefit of these approaches is that there is a control experiment if the poll is done in a truly randomized fashion. That is there are no external effects that affects one set but not the other.
One problem unique to the first approach is that the price that people say they will buy at is different from the price at which they actually will.
This is a typical A/B test on pricing. There are several issues with this technique:
- The population that buys at the website is different from those that respond to the survey. The survey is sent to a population that was selected for different reasons from those that eventually purchase the device. What is worse is that it is usually sent to a list of people who showed an early interest for some reason. This is commonly known as sampling bias. The population the price is based on is different from the people who would actually purchase the product causing a loss.
- The survey is usually sent out once and the price is set. The optimal price is likely to change during the lifecycle of the product.
Early adopters behave differently from the main market. As the product gets accepted, it may become more obvious that it is valuable and people may be willing to pay more.
It is also possible that the manufacturer may discover that he can reach a sufficiently huge population at a lower price as the product becomes more mainstream. Perhaps they can become higher volume producers.
One pricing strategy is to testing with different prices to find the volume that trades at those prices.
This is a more advanced technique than most in that there is an attempt to understand consumer behaviour.
However, the one big flaw with this technique is the lack of a control experiment.
Many factors other than price can affect the decision to buy. Some of these may occur more often after the price was changed.
Consider the impact of a ad released after price was changed. This will impact the volume. It also induces noise into the pricing experiment.
On one hand, if the manufacturer were experimenting with a higher price when an ad went public, it will look better than it actually is for profit causing the manufacturer to correct price upward. On the other hand if the manufacturer were testing a lower price, the volume would look higher than normal causing the manufacturer to correct price downward.
A commonly used strategy is to charge a (say) 50% margin relative to an estimate of variable costs. The idea is that assuming that the expected volume of transactions is achieved, the margin covers the fixed costs.
The biggest advantage of this method is that the margin is positive. Some of the pricing strategies discussed earlier in this blog don’t have this advantage.
The biggest disadvantage is that profit is not objectively being maximized.
Better pricing decisions can be made by a system that can learn from consumer choice.
Volume increases as price is reduced. So, can a big part of the market be captured by reducing margin to nearly zero?
Admittedly growth can be high with this strategy. The rationale can’t be to distribute fixed costs over more quantity because by the definition of this strategy, profit is not being maximized. A few things to be considered include:
- Competitors could also adopt this strategy. This will lead to a price war with the average customer getting away with a bargain. Its probably better to outperform competition on costs or the value proposition and set prices to the optimal price.
- If the plan is to dominate the market with more efficiency (lower costs) or by more features. The “insanely low” pricing could be used to get market attention quickly. Its probably better to give customers a free introductory offer and then allow prices to float to the optimal price.
- If the plan is to eventually raise margins, this is essentially a siege on the competition. It is best executed by someone that can outlast the competition. Obviously there is no argument for the optimal price here.
Allowing consumer choice to decide early will help guide product development before large investments are made.
Access to capital is an opportunity to discover consumer preferences instead of a “capture the market move”.
The theory of being a better business than the rest of the market can be tested with optimal pricing.
Consumers are usually a more efficient source of funding than investors.
When start-ups price low to capture the market, they lose an opportunity to verify their value propositions. Price is just one part of the value equation. Cost efficiency and the Value Proposition are the others. It is better to hear that the product needs to be improved rather than wait till a lot of investment has gone into capturing the market to and prices have to be raised.
Successful investors like Warren Buffet stayed away from the insurance industry for 2 out of 3 years to come roaring back when prices were higher.
Why not find something similar and price close to it with a small premium for features or a discount for the lack of a brand name?
Each customer experience is unique. Even though Starbucks was brewing coffee which was a relatively simple and arguably undifferentiated product in 1971, they could charge a different price. In 1971, this was because they could create a novel experience. Today Starbucks earns a premium for familiarity.
The uniqueness of customer experience and product justifies a price differential.
Even the most competitive environment like online retail justifies different prices for different retailers.
Pricing competitively is probably a good strategy in the absence of customer demand information such as when a store starts business.
Flying blind is comparable to ignoring customer data from actual purchases in the longer run.
Only an optimal pricing strategy can avoid the loss relative an informed pricing strategy.
When an inventor/businessman has translated an idea into a product. One of the most valuable questions is: “What should my price be?”.
There are many ways to answer this question:
- Price competitively relative to competition.
- Maximize growth with zero margin.
- Set a random margin.
- Price / Test / RePrice and repeat.
- Email/Coupon based polling.
Each of these methods has flaws in that the price is never set to methodically maximize profit given all the available information. By definition, only the optimal price can do this.
I will discuss each of these methods in future posts.
The optimal price is a function of only two variables:
- Price-Sensitivity: The rate at which customers leave as price is raised. This is elaborated below.
- Marginal Cost: The cost paid by the firm to deliver the additional item to the customer. This can usually be calculated by carefully tabulating costs. This should typically include:
- Cost of Goods sold
- shipping and handling: Shipping should include the costs of raw materials such as packaging, postage, and the cost of time to package goods.
- Taxes: This must include the sales tax and any tariffs.
Notice that the fixed costs don’t enter the calculation of the optimal price because it doesn’t change with price or quantity.
The figure shows a case of underpricing. When price is raised, the green shaded region of profit gained from price increase is larger for the existing customer base than the blue shaded region of profit sacrificed per lost customer.
The converse is true in a case of overpricing. When price is lowered, a larger gain is realized from increasing customer base than the reduction in profit from the existing customer base.
At the optimal price, the profit sacrificed matches the profit gained for small price and volume changes.