Jan Gunash

It’s simple but not easy. If you asked 10 people what they’d pay for the same product, you’d likely get 10 different answers.

There isn’t a straightforward equation because humans are inherently difficult to predict. If you charge too little, you’ll lose out on potential revenue; if you charge too much, you’ll likely lose customers.

I’ve broken down what WTP is in a previous article, so today we’ll discuss how to determine someone’s WTP.

Most people say customer surveys are a great way to gauge WTP. While I agree that they can indicate interest and provide some idea of what people are willing to pay, what people say they will pay and what they actually pay are often different. However, we don’t always have the luxury of selling a complex product quickly without first collecting some data, so let’s explore additional ways to gather this information alongside surveys.

In addition to customer interviews and surveys, you can use conjoint analysis to systematically determine how much people value specific features or technical specifications in your product. There are a few ways to do this:

Full-profile conjoint analysis: Let people choose from a full product description to “build their own product.”

Max-diff conjoint analysis: Let people rank features from best to worst.

Choice-based conjoint analysis: Let people choose from multiple hypothetical product profiles and require them to make trade-offs.

Companies that offer a “free” version of their product can gather valuable information on how initial users utilize features. They can then use that data to identify what people find valuable and charge for—or “paywall”—their most popular elements.

If you’re trying to optimize your price with existing customers, remember that it’s much easier to lower a price than to increase it. There are several methods to collect data on a customer’s WTP:

Price testing: Test an increasing price with new groups until you consistently receive pushback from multiple customers. This is a good indicator that you’re on the higher end of your pricing. However, it’s important to note that people talk, so it’s best to test within a subset of your customer base that doesn’t interact with other groups.

Reviewing sales history: Analyze your data to understand why people buy certain products. Do you have better customer retention with some products than others? What does the sales cycle look like? Which features are used more frequently? How are they rated? Are there higher return rates for certain products? Do most of your customers tend to funnel toward a specific set of offerings?

Shuffle your product offerings: If you have products with multiple features and offerings, shuffle them to understand how different combinations funnel customers. For example, let’s say your product offerings look like this, with each letter representing a different feature:

Product 1: A, B, C (50% of sales)

Product 2: D, E, F (25% of sales)

Product 3: G, H, I (25% of sales)

Let’s also say that Product 1 is priced lowest, Product 2 is mid-priced, and Product 3 is the most expensive.

To determine which features are the most attractive to customers, you could shuffle the features and see where people flow. For example, let’s say you test this by moving feature C to Product 2:

Product 1: A, B (25% of sales)

Product 2: C, D, E, F (50% of sales)

Product 3: G, H, I (25% of sales)

Because the percentage of sales increased for Product 2 (mid-priced) with the addition of feature C, you can deduce that people are willing to pay more for feature C. You can now use this information to consider restructuring your products to encourage more customers to choose Product 2.

This is a simple example and I’m sure you can see that it becomes more complex with additional variables (features, price bands, usage cycles, etc.). Unfortunately, WTP is far more about testing than calculation. As more variables come into play, it becomes increasingly complex.

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