With a 1% improvement in price optimization boosting operating profits by an average of 11%, it’s clear that finding the right price for your Software-as-a-Service product is critical for success.
But to avoid under or overpricing your service, you need to know how customers perceive its value. Many firms struggle with how to figure this out and simply mimic the prices of their competitors. But if you simple copy what your competitors are doing, you risk losing out on significant revenue for your unique service.
While the best way to arrive at an optimal price is continual testing with real customers, price laddering offers a way for companies to research what their price point(s) should be in order to maximize revenue.
Products or services are worth as much as customers value and are willing to pay for them. Accordingly, price laddering involves asking potential customers about their likelihood of buying a product or service at one or more price points.
To start, respondents are given a single product or service and asked to state their likelihood of purchasing it at a specific price on a 10-point scale with 10 being extremely likely. For those who respond with a likelihood that is less than desired (usually 7 or lower), users are asked to reconsider their likelihood of purchasing as a lower price. Typically, no more than three price points are used in a single test. The results can then be used to calculate the ‘take rate’ for each price point tested.
Traditionally when testing different price points, companies needed to coordinate costly focus groups and repeat the price sensitivity experiment for every price point. But by exposing respondents to a single concept at more than one price, businesses can take advantage of the fewer resources price laddering requires and reiterate testing throughout the product’s lifecycle.
When conducting a price laddering test, you need to ensure that a price point is chosen, above and below which respondents will be tested, and that the prices (or rungs of the ladder) are spaced relatively equally.
As an example, a company may want to test $79.95 a month for their subscription service. In this case, they could begin testing at a price of $99.95. Respondents whose intention of purchasing is less than 7 would then be re-tested with a price of $79.95. For those that still answer less than 7, they would be presented with a price of $59.95.
If 20% of all those surveyed indicate they would be likely to subscribe for the plan at $99.95 a month, the remaining 80 recipients would then be retested at the next lowest price. If 10% of that group showed intent to purchase of 7 or over, the take rate for the price point of $79.95 would be the total take rate of both rungs, which in this case would be 28%. This is based on the assumption that, given a customer’s likelihood to buy a product at one price, their intention to purchase will most likely remain the same with lower price points.
Complexity arises when different market segments are attracted to different features, which often results in a tiered pricing structure. However, even with multi-tiered pricing structures, companies can better know their product’s value by using price laddering.
Price laddering is a great tool to ensure your product’s price roughly aligns with its perceived value to prospective customers. However, keep in mind that you can only be sure of what prices work best by actually testing with real customers. Accordingly, price laddering should be used in conjunction with all the data you have available when making decisions about your service’s price, especially your real sales data.
