Value-Based Pricing Research: What Is Conjoint Analysis?
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Conjoint analysis is one of the most versatile methods in market research today. One of the most powerful forms of value-based pricing research, conjoint analysis is most often used for new product and feature pricing, along with determining the best feature and benefit mix. It can also be employed for existing products and services that need to be adapted to changing customer needs.
Along with commercial market research, the method is also often used in academic, psychology, or life sciences research. Over the past 10 years, conjoint analyses have been accepted as scientific evidence in U.S. court cases (like in the legal fight between Apple and Samsung over software patents).
What Is Conjoint Analysis?
When you run a conjoint analysis for pricing research, you break a product into its parts, like a Lego house made from many bricks. But in a conjoint case, the parts of that house would not be the bricks, but the walls, roofs, doors, and so on. In a conjoint survey, respondents choose among houses, and from these choices, you can derive the perceived value of each part. You can then build hypothetical new concepts and simulate preference among customers.
The purpose of conjoint analysis is not to understand choice among products as they are today. There are likely better data sources for that. Rather, it’s to learn what customers would choose in hypothetical new situations. The whole point is to be experimental and forward-looking. In a conjoint context, we are also not really asking about price acceptance or willingness to pay directly. Price is only one of many attributes. Respondents will never know a survey is about pricing. They will also not know that we are interested in one brand.
Value-Based Pricing Research: An Example
Here’s an example. Suppose you want to run a conjoint study on laptops. Respondents would see three laptop concepts and be asked to choose the one they’re most likely to buy. The survey would include 10 to 15 questions. From the choices across many respondents, you can derive which parts have the most value to them.
Webcast: Using Conjoint Methods to Create and Capture Customer Value
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Let’s say you are interested in how a laptop that costs $999 competes with eight other products. A market simulator would calculate a preference share for this laptop at 15.8%, meaning that percentage of respondents in the sample would choose this product. If you changed the price of the laptop to $899, it’d gain 1.2 percentage points in share. At $799, add another 2.5 percentage points. With these simulations, you can create a price sensitivity curve, as well as a demand curve and elasticity values for the product and each of the price increments. You also learn which other products lose out.
Determining Willingness to Pay
Conjoint simulations are also great if you want to measure customer willingness to pay for a single feature. For example, if you add extra memory to the survey laptop, the share goes up to 18.8% because the product is now worth more. If you compare the laptop with one that has more memory and costs $90 more, you can surmise that the added value of extra memory is $90. This insight is useful for many business decisions, such as which features to prioritize when developing the next generation of products and how to price them.
The true value of conjoint analysis is that with only a few questions, you get an unrivaled amount of information about preferences and perceived value.
Businesses benefit from this especially when they want to create and/or capture value. Developing added-value products is necessary if you want to win new customers or retain existing ones. Conjoint analysis helps in this context by showing exactly what constitutes value from a customer’s point of view. When they want to catch added value, conjoint analyses show how price changes will impact demand for a product in the market by revealing what prices customers will accept.
Creating and capturing value is relevant for all businesses, but most of all, for those with ambitious growth goals in a dynamic environment. Why? Businesses need to adapt quickly to changing customer needs, and they may follow different strategies to this goal. They may acquire new products and services, or innovate with their own R&D capabilities. In either case, they need to know how to maximize customers’ perceived value of those assets.
On the other hand, many companies are struggling with customers who constantly bargain for lower prices, and it is helpful to know which costs could be reduced and which features could be omitted without compromising the perceived value of the brand. Pressure is exerted not only internally but also by competitors that cut prices and innovate. Understanding the options you have to respond and knowing what the impact your responses to these competitive pressures will have on market performance is critical. Conjoint analysis is popular because it addresses these issues and reduces the risk of making the wrong business decision.
It can guide the product and price strategy and provide fundamental insights into the needs and preferences of customers. The results can then be used to prioritize features or product benefits or to get a sense of customers’ price sensitivity.
Conjoint analysis can give you the data and the customer insights to allow you to plan for profitable growth. It allows data to be turned into informed opinion about the way forward in pricing and innovation management. That can concern the management of prices and innovation, how to transition efficiently into the next product development stage, and how to involve and guide internal stakeholders and external partners.
For many decision makers, all of these insights will be a huge help in staying competitive in the market.
Read our other article in the GLG Applied Value-Based Pricing Research Series:
Getting Value-Based Pricing Right Is Difficult, But Worth It
Bernd Grosserohde leads GLG’s offer for new product development and pricing research. His focus is on building company and brand value through innovation and optimization. With over 20 years of marketing research experience, Bernd has worked with many global companies on building stronger, more profitable products. Before joining GLG, Bernd was Global Head of Pricing and Portfolio Management at Kantar.