What is Conjoint Analysis - Conjoint.ly

Conjoint analysis is a popular method of product and pricing research that helps uncover preferences for product features, sensitivity to price, forecast market shares, and predict adoption of new products. Its many applications include:

  • Feature selection for new or revamped products.
  • Marginal willingness to pay for specific features relative to other features.
  • Price elasticity of demand to understand how sensitive customers are to price changes.
  • Pricing your products, taking into account competition, cannibalisation, and customer preferences.
  • Testing branding, packaging and advertising claims.

Conjoint.ly offers a fully automated, state-of-the-art solution that is trusted by leading companies, market research agencies, and academics.

Sample experimental design

Design conjoint quickly

You do not need a PhD in statistics to use our simple, yet flexible tool to design your experiment.

Sample survey

Engaging experience for respondents

Our tool makes it easy for respondents to answer the survey on mobile, tablet or computer.

Sample report

Fully automated analytics

Learn what features customers want, how much they can pay, explore segmentation.

Preference share simulator

Preference share simulator

Run what-if scenarios for new product launches, feature changes, and price elasticity.

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. It is commonplace in marketing, advertising and product management, helping test acceptance of new product features, pricing, messaging. These days, it is also frequently used in healthcare, environmental, government and non-profit settings.

Conjoint analysis is a proven statistical technique used in market research to measure how much customers value an attribute (or product feature) of a good or service. There are many types of conjoint analysis. Conjoint.ly uses the most proven, tested, and theoretically sound type: choice-based conjoint analysis (CBC). All types of conjoint analysis have the same basics: products are broken down into features, and customers are faced with trade-offs when deciding which combination of features/attributes/levels to buy. In choice-based conjoint, respondents are presented with several questions, each a set of two to five products, and are asked to say which one they would buy or choose. The results are then used to calculate a numerical value (known as a “utility score”, “utility” or “part-worth”) that measures how much each attribute and level influenced the customer’s decision to make that choice. These scores are not absolute, but relative to other attributes and levels within the experimental settings.

Do you know what your customers value the most (and least) about your product? With conjoint analysis, you can. Uncovering customers’ preferences provides valuable information to guide decisions about new products, marketing strategy, advertising and promotion to increase sales.

This technique is frequently used for all types of products such as consumer goods, electrical goods, life insurance plans, retirement housing, luxury goods and air travel. Don’t have a large marketing budget to use conjoint analysis? That’s OK: Conjoint.ly does simple conjoint analysis for you. Even small businesses such as local grocery stores or restaurants can benefit from conjoint analysis. A profit motive is not necessary either, for example charities can use this technique to find out donor preferences.

Where is conjoint analysis used?

Conjoint analysis can be used in a number of different industries for a variety of applications where you need to know what kind of product your customers are likely to buy.

Pricing. Need help deciding on the right price for your product, balancing the uptake by customers and profitability? Conjoint.ly helps you find optimal price by simulating customers’ behaviour in the market based on their revealed preferences?

Product feature selection. Deciding which features to implement in your product? Conjoint.ly reveals your customers’ true preferences, including how much they are prepared to pay for different features.

Plan creation for telcos. $40 or $70 a month? 3GB or 5GB data inclusion? Conjoint.ly will help you decide the best option to grow your business without expensive in-market testing of different product combinations.

For FMCG companies. Want to quickly test an idea for a new flavour or size? Conjoint.ly will help you automatically set up an experiment, gather and analyse responses – no need for big market research budgets.

How does conjoint analysis work?

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
  • Ranked list of product constructs,
  • Segmentation of the market based on respondent characteristics,
  • All the raw data in Excel, including preferences of individual respondents for the different levels, and
  • Other types of output.

What is unique about doing conjoint analysis on Conjoint.ly?

Being the home of conjoint analysis, Conjoint.ly offers complete set of outputs and features through an accessible interface.

Quick to set up. It only takes a few minutes to set up your experiment with a simple wizard, which will help you choose appropriate settings and suggest the minimum sample size. You do not need to customise and test any survey with Conjoint.ly – our system does that for you. You can either get Conjoint.ly to send out invites to participants on your behalf or share a link with them. Conjoint.ly embodies an agile approach that puts you in control of the research process without hiring costly statisticians.

Easy on respondents. Experiment participants only need to respond to several questions of which product concepts they prefer most. It typically takes a few minutes and is easy to answer on their mobile phone, tablet, or computer.

Smart analytics done for you. You do not need to know the theory of discrete choice experimentation (DCE) or conjoint analysis. Conjoint.ly guides you through the process, uses state-of-the-art analytics behind the scenes to crunch the numbers, and checks validity of reporting. Outputs are 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.).

Insightful reporting. Conjoint.ly gives you insights about customer preferences of different product features and pricing in a simple interactive report, which you can export and share with colleagues.

Our support team is ready to help with you with your studies if you need any assistance.

A simple conjoint analysis example in Excel

In this post, we provide a conjoint analysis example in Excel (also available as a Google Sheet conjoint example – which you can copy to edit). This example will show:

  1. Inputs into a conjoint study
  2. Questions (choice sets) that respondents see
  3. Calculations of partworth utilities (relative preferences and importance scores of attributes)

This example is limited to:

If you’d like to experience a real online conjoint analysis tool, sign up and take a look at example reports.