Why Conjoint Analysis Is Best for Price Research

Summary: Conjoint analysis is the best research approach available to price research. There are different conjoint analysis approaches. Which method should we use? The one that better reflects how buyers make decisions in the marketplace.

4 minutes to read. By author Michaela Mora on June 14, 2023
Topics: Price Research, Analysis Techniques, Conjoint Analysis, New Product Development

Conjoint Analysis And Realism In Price Research

Conjoint analysis is currently the best research technique to study the impact of pricing on buyers’ preferences and choices.

Conjoint analysis includes a set of trade-off analysis methods, such as Full Profile Conjoint Analysis, Adaptive Conjoint Analysis, Choice-Based Conjoint Analysis (CBC) or Discreet Choice Analysis, and Menu-Based Conjoint Analysis (MBCA).

Respondents must make trade-offs in conjoint analysis exercises. We have to make choices as we all do in real life. We are always looking for convenient, affordable, and as little disruption as possible to our habits when we choose products and services. In addition to product availability and competing alternatives, brand perceptions, cultural norms, core values, past experiences, and other factors play a role in the trade-offs we make and the weight we put on pricing in our decisions.

In conjoint analysis, we attempt to mimic purchase scenarios for products and reflect factors affecting the buying decision-making process used by your target audience.

Conjoint analysis is the best approach to represent the competitive landscape in which products and services live. Studying reactions to pricing in the context of the competition provides more accurate insights than capturing the appeal or likelihood of using or buying a product in a monadic concept test.

Steps in Conjoint Analysis

Not long ago, I received a survey from a trade association testing the appeal of market research online training courses using a poorly designed concept test. I doubt they got any actionable insights from the study.

To make matters worse, they also wanted to know how much I would pay for these courses using the Van Westendorp Price Sensitivity Meter (PSM), a method with many drawbacks.

To get more valuable insights, I would have recommended conducting a Choice-Based Conjoint Analysis for their online market research training course product. Below are the steps to follow.

Identify Relevant Attributes and Levels

Defining relevant attributes that can be drivers of preference is the most important step when designing conjoint analysis. Attributes can be product or service features or benefits. Levels are variations within the same attribute category. For instance, we consider Price a product or service attribute. Its levels are the different price points we want to test.

If no prior research exists to identify relevant product features and benefits, we strongly recommend conducting qualitative researchIncluding irrelevant attributes will produce biased or inconclusive results.

Presenting attribute levels is not limited to text descriptions. We can use images when appropriate.

Attributes should: 

  • Cover the full range of possibilities for existing products
  • Be independent of each other, with no overlapping meaning
  • Be mutually exclusive
  • Have a balanced number of levels across attributes (when possible)

The table below shows attributes (product features) that could have been included to test a portfolio of online market research courses. 


Choice-Based Conjoint Attributes


Create an Experimental Design

Experimental designs combining different product attributes are the engines behind conjoint analysis exercises. They generate sets of unbiased choice tasks presented in a survey format to respondents.

We created them to provide frequency balance (each attribute appears the same number of times), orthogonality (each item is paired with other items the same number of times), and position balance (each item appears the same number of times in each position). Balanced experimental designs are needed for estimation accuracy.

 Below is an example of a potential choice task for online market research training courses. Respondents will make choices in several task scenarios similar to this with different combinations of levels. As respondents make choices across scenarios, we capture data that will allow us to understand their preferences.


Choice-Based Conjoint Choice Task


This approach provides more realistic scenarios for respondents and prevents them from focusing solely on price, decreasing the natural tendency to lowball.

Lowballing is inevitable when price becomes the center of attention. We observe this behavior in pricing research approaches using direct questions about willingness to pay, purchase intent, or the Van Westendorp PSM.

Develop a Market Simulator

Once the data is collected, we estimate probabilistic models to predict which product configurations are more likely to be chosen among other product configurations using simulators.

Market simulators are the most useful output from conjoint analysis exercises. They allow managers to conduct “what-if” analyses to predict shares of preferences for different product configurations. If cost information is available, the simulator can help identify optimal price points and ranges that maximize profitability.

In the market simulator example below, Option 1 could get the highest share of preference, because of a lower price and the availability of a Q&A session if this was important to the target audience.

Option 3, which doesn’t have a Q&A option, could get the lowest share of preference compared to Option 1. Even Option 2, which offers Q&A with a live instructor, could receive a higher share of preference than Option 3, despite having the highest price.


Choice-Based Conjoint Simulator


Pricing Is a Complex Topic

More straightforward price research approaches are appealing because of their simplicity, affordability, and ease of implementation. This appeals to managers without experience with statistics or uncomfortable with advanced techniques such as Conjoint Analysis.

Unfortunately, simpler approaches often don’t reflect how people make buying decisions and can result in misleading conclusions.

The field of conjoint analysis is constantly evolving. Conjoint analysis studies are likely to be more expensive than traditional concept tests. They require more time and expertise to do them well. Still, they are worth it. They allow us to get more realistic decision-making situations in price research, and thus more accurate insights.