What is conjoint analysis?


Conjoint analysis is one of the most widely used and powerful quantitative methods in market research. It helps uncover how people make choices and what they really value in products and services.

The most common type of conjoint analysis, called choice-based conjoint, involves presenting people with choices from several product concepts and then analysing the drivers for those choices. The output from conjoint analysis is measurement of utility or value. It is perfectly suited for answering questions such as:

  • “Would an increase in price lead to more sales or less?”
  • “What additional features should we build?”
  • “Will this new product cannibalise the market share of my existing products?”

The utility scores (also known as partworth utilities) are used to build simulators that can forecast market shares for a set of different products offered to the market. Through modelling (simulating) people’s decisions, you can find optimal features and pricing that balance value to the customer against cost to the company and forecast potential demand in a competitive market situation.

Conjoint analysis is a robust methodology that has been developed since the 1970s. It far surpasses its alternatives, such as SIMALTO and self-explicated conjoint, in its predictive power. Thanks to Conjoint.ly, you do not need to dive deep into technicalities of the methodology that desktop software tools require. And you can rest assured in the quality of the analysis and full functionality.

How conjoint works

The foundation of conjoint analysis is breaking a product or service down into its components (they are called attributes and levels) and then testing combinations of these components in order to find out what customers prefer. It is then possible to estimate the value (also called “partworth utility”) of each component of the product in terms of its effect on customer decisions.

For example, a smartphone may be described in terms of attributes such as brand, screen display, colour, and price. Each of these attributes is broken down into levels - for instance levels of the attribute for screen display might be 5”, 5.5”, 6”.

Example conjoint analysis choice task: respondents are asked to choose between three concepts (alternatives), each consisting of levels from four attributes

Rather than merely asking respondents what they like in a product, or what features they find most important, conjoint analysis employs the more realistic task of asking respondents to choose between potential product concepts, which are combinations of attributes and levels. These product concepts are then carefully assembled into choice sets. Each respondent is typically presented with 8 to 12 choice sets (or questions).

The process of assembly of levels into product concepts and then into choice sets is called experimental design and requires a substantial deal of statistical expertise. Conjoint.ly automates this process, using state-of-the-art methodology. You can specify the number of alternatives (concepts) per choice set, the number of choice sets per respondent, and other settings when you set up an experiment.

Then respondents go through the conjoint survey to complete the choice tasks (typically it takes a couple of hundred responses, but may vary depending on the complexity of the study). You get a report that contains:

  • Relative importance of attributes (attribute partworths),
  • Relative value by level (level partworths),
  • (In a brand-specific study) average rating of each brand,
  • Marginal willingness to pay,
  • Market share simulation (both online and in an Excel file)
  • Ranked list of product constructs,
  • Segmentation of the market based on preferences,
  • All the raw data in Excel, including preferences of individual respondents for the different levels, and
  • Other types of output.

Advantages of running conjoint on Conjoint.ly

  • Being the home of conjoint analysis, Conjoint.ly offers complete set of outputs and features through an accessible interface.
  • Conjoint.ly embodies an agile approach that puts you in control of the research process without hiring costly statisticians.
  • Ready for any application of conjoint analysis (pricing, feature selection, product testing, new market entry, cannibalisation analysis, etc.) in any industry (telecommunications, SaaS, FMCG, automotive, financial services, etc.).
  • Our support team is ready to help with you with your studies if you need any assistance.