Conjoint analysis is a survey-based statistical market research technique that helps determine what attributes people value in products and services. It uses surveys to measure how much the features, functions, and benefits of a product or service affect their overall perception. Marketers can use it to find out which attributes of a product or service are most influential in driving purchase decisions or impacting respondent choices.
For example, a company ABC that manufactures laptops may want to find out which features (e.g. processor speed, RAM, size) are the most crucial for their purchasers when it comes to their decision making.
It can get invaluable insight into customers’ preferences with surveys that feature various combinations of product attributes. Respondents are then asked to rate these, allowing the organization to pinpoint which attributes exert the greatest impact on people’s decisions.
Hence, a few key takeaways about conjoint analysis methodology can be –
- Conjoint analysis is an effective model for extracting consumer preferences in the purchasing process, which then is quantitatively measured through statistical analysis – yielding data that no other method can.
- Conjoint analysis is a data-driven marketing research tool that reveals the key factors that consumers consider when evaluating a product or service.
- When introducing a new product or service, executing a conjoint analysis is highly recommended to gather insights that can help you optimize it for success.
What is Conjoint Analysis?
Conjoint analysis is a combination of marketing research techniques that help businesses gain valuable insights about what attributes and features of your product or service are effective in influencing the purchase decisions of your target audiences.
The former global director of product management for Oracle’s Advanced Customer Services, Brett Jarvis, wrote in an article (Conjoint Analysis 101) for Pragmatic Institute –
Conjoint analysis is a set of market research techniques that measures the value the market places on each feature of your product and predicts the value of any combination of features. Conjoint analysis is, at its essence, all about features and trade-offs.
Conjoint analysis examples can be found in many industries ranging from consumer goods, electrical appliances, and insurance plans to luxury items and air travel. Market researchers extensively use it in marketing, advertising, and product management to identify what consumers are likely to purchase and their preferences towards certain products, as well as to do product and pricing research. All in all, you can use conjoint analysis methods when –
- You are choosing features for a product
- You are assessing the product’s price sensitivity
- You are forecasting market shares or predicting the adoption of new products or services for your survey respondents
Conjoint Analysis Examples
1) Conjoint analysis example in Branding & Package Design
Through conjoint analysis, a researcher can effectively test out the design and packaging of a product. This method is reminiscent of how consumers have previously interacted with various brands and products in their daily lives.
Package design undergoes a lot of changes due to the aesthetics and how it affects interactions between colors, text, and other visuals. CBC or choice base conjoint can be a powerful technology in demonstrating how a product or branding interacts with customers and markets. It helps you optimize and customize your offers to maximize market response.
2) Conjoint analysis example in Healthcare
Research on how people feel about different health options has increased over the past few years. This is due to the development of new tools that can be used in conjunction with conjoint analysis, specifically discrete-choice experiments and other multi-attribute stated preference methods.
In this study, the task force met regularly to identify the important steps in a conjoint analysis, to discuss good research practices for conjoint analysis, and to develop and refine the key criteria for identifying good research practices. It is used to know Patients’ Preferences and offer Personalized Care.
Why is it important for researchers?
The conjoint analysis experiment is a popular survey method to find out customer preferences. It involves creating surveys, distributing them to participants, and analyzing their responses to determine their purchasing decisions and reactions to different products or features.
Using a conjoint analysis survey can help you gain insight into consumer behavior by analyzing data. This allows you to assess the worth of different factors such as price, features, and location. With this information, you can make informed decisions that may benefit your business.
It uses real customer data to create consumer profiles and conducts a regression analysis to generate an accurate report, rather than relying on hypotheses. Accessing accurate and reliable data can give your business a competitive advantage by allowing you to develop products or services that meet your customers’ needs and expectations.
Types of Conjoint Analysis
- Choice-Based Conjoint Analysis – CBC is one of the most common types of conjoint analysis, which helps identify how respondents value combinations of features.
- Adaptive Conjoint Analysis – In the ACA survey, each respondent gets to answer a bunch of questions and we use those responses to customize their experience. Because it’s often used in studies to reduce effort and produce accurate insights,
- Full-Profile Conjoint Analysis – This type of conjoint survey presents the respondent with a series of full product descriptions and asks them to indicate which one they’d be most inclined to buy.
- MaxDiff Conjoint Analysis – When you conduct conjoint analysis, you are given multiple options to rank them on a scale of best to worst or most likely to buy and least likely to buy.
History of conjoint analysis
Conjoint analysis is a process that has been used commercially since the 1970s. It begins with an assessment of objective data, such as demographics and purchase history.
In 1964, two mathematicians, Duncan Luce, and John Tukey published a rather difficult article. Their article was on Simultaneous Conjoint Measurement: A new type of fundamental measurement that was difficult to
The theory of conjoint measurement was first developed by Luce and Tukey in the late 1950s but the first consumer-based approach was created by Green and Rao in 1971.
Paul Green’s 1974 article on the design of choice experiments is still relevant today. In the 1980s and 90’s, conjoint analysis grew in popularity (Wittink & Cattin 1989 then Wittink, Vriens, and Burhenne 1994 cited in Green and finally Kreiger & Wind 2001).
Conjoint surveys can be enjoyed on a range of platforms. The platforms range in price and feature set, allowing marketers to choose the survey that suits their needs.
Conjoint analysis is a marketing tool that is widely used in the market. It provides insights into what product features and benefits consumers are interested in, and how different trade-offs between them will impact purchase behavior.
How does conjoint analysis work?
Step 1: Break your products into different attributes and levels
For a conjoint study of your product or service to be run, you place it into its component parts, called attributes and levels. One way to conduct an experiment is to break the data into different attributes and evaluate them. For example, a car manufacturing company can break down the components of a car into attributes of interest for our experiment such as brand, price, engine type and etc.
Step 2: Survey respondents choose their preferred concept in each choice task
In this step, we should have the conjoint experiment design and invite respondents to complete the survey. The assignment for respondents is to choose their most preferred option among sets of person profiles based on their own responses. Your conjoint analysis experiment should be asking respondents to choose products from available options instead of asking them about preferences for attributes and levels.
Step 3: Use a survey that builds and uses models to predict the preferences of your target audience
Discovering insights from the data you collect is the final step in our analysis process.
To understand the preferences of your customers better, the conjoint software includes a statistical model that considers the available product options and importantly which alternatives the respondents chose. Your business needs to be able to provide customers with the products they want, which means developing a strategy that accounts for both customer needs and their reactions to ensure success.
What is Conjoint analysis used For?
Some of the applications of conjoint analysis are –
1) Conjoint Analysis in Pricing
The process of determinant analysis helps a company understand how its customers would value different options so they can develop a pricing strategy based on their findings.
An automation tool company might decide to implement a freemium model to scale its business. In this model, they offer their product without any charge to people who are not willing to pay. If a company uses conjoint analysis to determine that some of their users really value one feature, they might choose to charge for the feature until it becomes less important.
2) Conjoint Analysis in Sales & Marketing
When successful companies understand which features consumers value most, they can better target those features with their advertising copy and promotions. Companies should use conjoint analysis when they find that their customers are not as uniform in terms of what they want or value. They can also use it to figure out the most important features to customers and how much they value them. That way, their marketing material can be smarter and more targeted.
For example, a TV manufacturer may use choice-based conjoint analysis to find out if customers value screen size, sound, and picture quality more or if they are looking for better prices. Accordingly, the manufacturer can optimize its sales and marketing strategies.
3) Conjoint Analysis in Research & Development
The insights from the conjoint analysis can help companies predict whether new features will be a success and how much market demand there is for new content. These insights can also help determine which products or services to increase or decrease significantly.
One example is a smartphone manufacturer that conducted a conjoint analysis and discovered the most desired feature of its customers is the screen size. With these details, it’s safe to say that using the product development budget and resources by developing larger screens is the most logical decision.
Using Conjoint Analysis for Business!
By conducting conjoint analysis, you are able to understand which features your customers value the most. This will help you make thoughtful decisions about pricing, product development, and sales and marketing activities.
Who invented conjoint analysis?
Duncan Luce and John Tukey published the first source on conjoint in 1964, called ‘Simultaneous conjoint measurement: A new type of fundamental measurement’.The paper, which was published in 1971 by Paul E. Green and Vithala R. Rao, is an important approach to the idea of studied judgments. It is “consumer-oriented” because it purports to focus a great deal on how different people respond to the same information.
Is conjoint analysis qualitative or quantitative?
A form of quantitative research, conjoint research helps businesses see how their customers feel about different products and refine their product offerings. Respondents can make suggestions on which combination would be the most appealing to them
What is the conjoint analysis formula?
For calculating conjoint analysis, you need to use different formulae for different steps to finally find the relative importance of attributes –
- Calculate Attribute Utility Range – You can do this by using the formula – Utility Range = Highest Utility Value of an attribute – Lowest Utility Value of an attribute
- Calculate Total Attribute Utility Range – Find out the total attribute utility range via equation – Total Utility Range = ? Utility Range
- Calculate the Relative Importance of Attributes – Finally, you need to find relative attribute importance through the formula – Relative Importance of attribute = (Attribute Utility Range/Total Attribute Utility Range)*100
What are part-worths?
Partworths is a number that compares the influencing factors of various products or concepts. Part-Worths means that each attribute value contributes a portion to the total value of the product. It refers to level utilities for conjoint attributes.
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