The rational economic theory of pricing states,

“ We all have an internal price that we are willing to pay for a product. It is our Willingness To Pay (WTP) We have one such number,  for each product. We buy a product as long as its price is just below our willingness to pay”.

For all practical purposes, they assume that this is a static and magical number. It differs from customer to customer, no explanation why,  but the marketers are told to deal with it.

Hence there are methods and experiments that try to elicit what this distribution is across customers. Once you gather enough data on how many customers are willing to buy your product at different price points, you will have the demand curve.

Linear Demand Curve: Quantity  = Constant – (Slope) * Price
(sign is negative except in one very special case)

So we see the many different ways marketers go about asking customers about their willingness to pay, be it for a soda or a webapp.

One common and incorrect method is through surveys that ask for our attitudinal willingness to pay, that is what is our intention

  1. On a scale of 1 to 10, how likely are you to buy …
  2. Of the following 5 prices, please indicate the price above which you will not buy
  3. For a product that delivers x, y and z how much more will you pay.

These fail to take into account that actual customer behavior is much different from their stated intention. For instance, the context they are in while answering the survey is different from their buying context.

In general these studies overestimate the price customers are willing to pay.

Then there are experimental method that is popular with web startups. These include,

  1. Showing different prices to different customers and measure conversion
  2. Show multiple versions, present them in different order and use visual nudges to find one with higher conversion

These are variations of what brands like P&G and Unilever used to do in the offline world. These are definitely better than survey based approach but still fail to uncover true customer willingness to pay.

The experimental price points could still be way off. It also cannot find whether the customer will really end up buying the product at the stated price given everything else that are competing for their wallet.

If you are a web startup and want to use experiments to find demand curve, there is one very simple and straightforward method  I recommend (see research reference here).

This is most suited for web startups, especially those that allow a free trial period or free version (freemium). This is best done during your long beta period or with those freeloaders who steadfastly remain on the free version even after an year.

  1. Let the users signup for free and use the product for a period of time.Then, next time they use the product put up an ultimatum question to them
    • You have been using the product for 3 months. We want you to upgrade to paid version. Please think of a price from 1 to 20 that you are willing to pay for continued use of the product.
    • While you do that we will pick a random number R from 1 to 20 as well.
    • If your price P is less than R: For example you picked 4 and we picked 13. Sorry, we have to let you go. It has been great journey and we are happy to have delivered you value over the past 3 months.
    • If your price P is more than R: For example you picked $14 and we picked $7. Congratulations, you only need to pay $7 to use our product.
  2. Customers are most likely to reveal their true willingness to pay for your product. If they were to say a number below their true value they risk not using the product. If there were to say a higher number then they risk paying it. So the right option for them to state the price at which they will continue to be delighted to use your product.
  3. Once you collect enough data points you have an almost perfect demand curve, way more accurate than what is done through A/B testing.
  4. Not to mention this is also a great way to find segment-version fit (if you have only one version, then Product-Market fit).

Once you know the demand curve, you know the price that maximizes your profit (or prices for different segments).

But all these are methods that continue to treat willingness to pay as an immovable and given number, something that marketer cannot control. Despite the research and analytics, all these methods ignore the value to the different customer segments and fail to ask the question, “What job do I want my target segment to hire my product for?”

You as a marketer have more control than you have been told by traditional economists in setting your prices. I recommend you start here and take control of your pricing strategy.