BlogBlog Details page
Research Resources
research resources icon

Conjoint Analysis: Optimize Your Product & Pricing

Naira Musallam, PhD • 6 Jul 2020

If you work in or around market research, you have most likely used or (at the very least) heard of conjoint techniques. 

We will be the first to admit that literature on the topic is often intensive and can feel overwhelming for those unfamiliar. But, this doesn’t have to be the case.

While we can automate your curiosity and research projects with the SightX platform, it's still crucial to study these topics as researchers for a more holistic understanding. To save you from sinking into a technical academic paper, we’ve compiled some of the most common and pressing questions we get about conjoint analysis.


What is Conjoint Analysis? 

Conjoint analysis is a market research approach that measures the value consumers place on specific features of a product or service. It uncovers consumers' preferences, allowing you to use the data to predict adoption, test price sensitivity, choose optimal features, and project market share.

Conjoint analysis is often used in research to test products before launch, as it works well for evaluating product features. This type of research is also quite flexible and can be used across most industries for various products, from air travel packages to consumer goods.


When Should I Consider Conducting a Conjoint Analysis

When it comes to decision-making, consumers often consider a variety of product features. These features can be referred to as product "attributes", with each "attribute" having several levels or options.

To illustrate, let’s use an example: You are considering purchasing a new car (product). One of the attributes you examine could be the car's color, making the choices of color the attribute levels (black, white, or grey). Another potential attribute could be the car's price ($25K, $35K, $45K). And a third attribute could be the energy type the car uses ( gas, hybrid, or electric).

Sample of conjoint analysis data. Image of a car with "color", "price", and "Energy" overlaid with their corresponding levels

But what is the ideal combination of the three attributes that will result in the highest sales? That is exactly what a conjoint analysis will tell you.

When you have multiple attributes and levels to study, a conjoint analysis is the research solution that can help you optimize your product.


What is the Purpose of a Conjoint Analysis?

In market research, there are many applications for conjoint analysis. You can measure consumer preferences for specific features, learn how price changes affect demand, and even forecast the likely acceptance of a product if brought to market.

The most popular type of conjoint analysis is Choice-Based Conjoint Analysis (CBC), also known as Discrete Choice Modeling (DCM). The purpose is to assess how consumers evaluate a product and the tradeoffs they make across various attributes and the respective levels.

For example, would consumers be more likely to purchase a medium-priced electric car in grey as opposed to a higher-priced gas car in white? Or could it be some other set of alternatives?

A conjoint analysis will enable you to estimate the utility (or value) consumers place on each of your product's attribute levels- this is also known as part-worth.


What is Part-Worth and How Does it Relate to Conjoint Analysis? 

While there are several estimation models, the most widely used is the Part-Worth model. Unlike other models, it does not make any prior assumptions regarding the utility caused by a specific level of any attribution. Simply put, the outcomes will be a more accurate depiction of consumer preference.

In your product research, you will see that multiple attributes come together to define the total worth of a product. And some may be more important to consumers than others.

Part-worth is the estimate of the overall value (or utility) associated with each attribute and level used to define your product. So, the values of each separate attribute are the part-worths.

There are several research techniques to estimate part-worth. The most widely used are Latent Class Analysis and Regression modeling based on Hierarchical Bayesian (HB).

The values of the part-worth utilities provide information on how attractive attribute levels are. Remember the example of car types across various price points and colors?

If you want to know the relative importance of each attribute, you will need to calculate Attribute Importance by determining how much difference each attribute can make in the total utility of a specific product. That difference is the range in the attributes utility value.

You can calculate percentages from relative ranges, obtaining a set of attribute importance values that add up to 100. The higher the percentage is, the more important the feature.

In the end, you can discern which features should be combined for the most impact- ideally, you are working with a software platform that automates this process for you.


What is the Link Between Conjoint Utility Scores and Market Share?

In addition to those insights, you may be interested in taking your utility scores and using them to simulate market share, where a market simulation provides information on the relative share of respondents who prefer predefined products in a certain context.

Simulating market share enables researchers to test various scenarios and assess factors like price demand curves, the impact of product adjustments, and the competitive landscape.

The first step in conducting a market simulation begins with specifying relevant products. The total utility of these products is computed at the individual or target level- the total product utility being the sum of its part-worth utilities (Green and Krieger, 1988). From there, consumer insights leaders can compare the total utility value of products to that of a “none of the above” option.

The larger the difference between the total utility score of an alternative and the utility score of the “none of the above” option, the more likely it is for users to accept the alternative. Conversely, if a product's total utility score is below the “none of the above” option, it indicates that the users are not likely to accept the offering.

Researchers can apply the logit model to estimate market share. Market share is predicted by simply exponentiating the total utility and then dividing this value by the sum of all products’ exponentiated values and that “none of the above” option. Here is the formula:


If, like most people, you’re a bit scared off by formulas, have no fear!

Conjoint analysis is simply the mathematical representation of what we covered in the paragraph above: “market share is calculated by exponentiating the total utility of a product and then dividing this value by the sum of all products’ exponentiated values and the “none of the above” option."

The calculation can be done using a basic calculator equipped with an “EXP” (or ex) button.

Below is an example with total utility scores, exponential scores, and the market shares associated with it.


While the overview above should prove helpful, we understand that these concepts can be technically challenging.

 SightX allows you to automate conjoint analysis, helping you to more easily optimize your product development and forecast the likelihood of market acceptance. Our step-by-step set-up enables you to launch projects within minutes and instantly analyze results with real-time analytics.

Ready to conduct your own conjoint analysis? Get started with a free trial today!


Naira Musallam, PhD

Naira Musallam, PhD

Naira the co-founder of SightX and our in-house expert for all things research, statistics, and psychology. She received her doctorate from Columbia University, and served as faculty at both Columbia and NYU. She has over 15 years of experience in data analysis and research across multiple sectors in various industries.

Ready to meet the future of market research?

Reach out to get started

Ready to meet the next generation of market research technology?

The Future of Market Research