Master your SaaS Pricing: Top tips to measure your market's willingness to pay

Whether you’re launching a new SaaS or want to update your current offerings, conducting a willingness to pay survey can be very valuable.

While it won’t guarantee that prospects will pay, it helps refine your pricing and validates that you’re in the right ballpark.

This article focuses on quantitative methods for measuring willingness to pay, while emphasizing that these methods should complement, not replace, the importance of testing your pricing directly with new prospects.

The Van Westendorp Price Sensitivity Meter

The Van Westendorp Price Sensitivity Meter (PSM) is a well-established tool for assessing willingness to pay.

This method uses four key questions to map a price range that reflects the consumer perceptions of the product:

1. At what price would you question the product’s quality because it's too low?

2. At what price do you start considering the product a bargain?

3. At what price does the product begin to feel expensive?

4. At what price is the product too expensive?

These questions help generate a price range where your SaaS product is perceived as both valuable and accessible.

The intersections of responses create a price sensitivity curve that indicates the optimal price point, an acceptable price range, and even a range that signals potential customer churn due to perceived high cost.

This method can be particularly effective for consumer products where price perception is crucial, such as SaaS tools targeting price-sensitive markets.

Example of a chart that summarizes the results of a Van Westendorp Price Sensitivity Meter

In a Van Westendorp price sensitivity analysis, you can use charts to determine a suitable price range for your product (see example above). This range is defined by:

• The Point of Marginal Cheapness: This is the price at which the number of people who consider the product too cheap equals the number of people who find it expensive.

• The Point of Marginal Expensiveness: This is the price below which the number of people who view the product as a bargain exceeds the number of people who consider it too expensive.

This analysis provides a valuable range in which you can position your product’s price points.

Pros:

• Simple to launch and interpret.

• Provides a clear range of acceptable prices based on perceived value, which can guide strategic pricing decisions.

Cons:

• Tends to capture what people want to pay rather than what they might actually pay in a real transaction.

• Susceptible to hypothetical bias: Respondents often state higher valuations than their true willingness to pay. For instance, if they indicate $100, they might only be willing to pay $50-$60 in reality.

• Doesn’t take into account other parameters like set of features,…

The Bidding Game Method (Becker-DeGroot-Marschak)

The Bidding Game Method, also known as the Becker-DeGroot-Marschak (BDM) method, aims to reduce the hypothetical bias found in traditional willingness to pay surveys by introducing skin in the game. Or, put another way, it is supposed to create incentive-compatible environments that encourage honest and true feedback.

Here’s the setup: respondents state their maximum willingness to pay, then a random price is generated. If this random price is lower than their stated amount, they can purchase the product at the random price; if it’s higher, they miss out.

The concept is that if people bid too high, they are likely to get the item, but at an overly expensive price, discouraging high bids. Conversely, if they bid too low, they risk not getting the item, pushing them to bid higher. This balance encourages bidders to aim for a fair perceived price.

Pros:

• Effectively minimizes hypothetical bias through a risk-reward mechanism that forces respondents to carefully consider their true valuation.

Cons:

• Complex to administer and often impractical for SaaS, particularly in subscription models where pricing dynamics differ from one-time purchases.

• The format can feel game-like and artificial, which may lead to less reliable results if not executed correctly.

  
  

The BDM method works best in markets where it’s important to know the highest price customers are willing to pay. For SaaS, the BDM approach might feel too complex, but it can still be useful when paired with real-life trials or promotions that mimic bidding situations.

The Multiple Price List (Gabor-Granger) Method

The Gabor-Granger method is a straightforward approach that simplifies decision-making by presenting respondents with a list of predefined prices. Participants indicate at each price point whether they would purchase the product or keep their money, providing a clear picture of acceptable price ranges.

Conducting this experiment is quite straightforward. You start by creating a survey that lists various prices for your product. For each price point, ask respondents if they would choose to buy the product at that price or prefer to keep their money.

Pros:

• Simplifies decision-making process by offering fixed prices, reducing the cognitive load on respondents.

• Efficient for testing multiple price points quickly, which is valuable in early-stage pricing explorations.

Cons:

• Like many methods, it still suffers from hypothetical bias since respondents are making decisions without the pressure of real financial commitment.

This method is particularly advantageous when you have a spectrum of potential price points but are unsure which to pursue. It helps to validate these points against customer willingness to purchase at those levels.

The Discrete-Choice method adapted to willingness to pay studies

The Discrete-Choice method, also known as Discrete-Choice Experiments (DCE) is interesting when you want to get insights on pricing offers that are defined by several attributes - price, features, and even product variants.

Respondents are presented with scenarios combining these elements and asked to choose the most appealing option according to their needs.

The discrete-choice method enables to compare pricing offers with several attributes (price, feature set,…).

Pros:

• Simulates real-world decision-making by integrating both pricing and product features, closely mirroring actual purchase considerations, which is particularly adapted to SaaS businesses

• Provides deeper insights into which features drive value perception and willingness to pay.

Cons:

• More complex to design, administer, and analyze compared to simpler pricing surveys, requiring a more sophisticated analytical approach.

Discrete-Choice modeling is invaluable when SaaS pricing involves multiple tiers or feature sets, allowing you to identify the most valued combinations.

Conclusion: Finding the Right Approach for Your SaaS

If your features breakdown per plan are set, and you want to test price sensitivity, methods like Van Westendorp can work.

While those methods provide valuable insights on willingness to pay, they often fall short of capturing the complete value perception of your SaaS product, which includes features and functionality.

So if you’re exploring feature-price combinations, a Discrete-Choice approach will give you insights on your pricing offers as a whole.

Also, bear in mind that price is a perception, and it requires both quantitative data and qualitative insights.

To truly validate your new pricing, test your new pricing offers with new customers with the help of your account executive team. This will concretely validate that your new pricing offer does match your target’s willingness to pay.

Following that validation, you will be more confident to allocate ressources to adapt your product and sales processes to this new pricing.

By investing time in both quantitative analysis and qualitative scenarios, you can fine-tune your pricing strategy and ensure you don’t leave money on the table.