Conjoint analysis is a popular market research technique that marketers use to determine what features a new product should have and how it should be priced. It requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. Using a conjoint study you can ascertain a consumer willingness to purchase products at certain price points, and which attributes are most desirable. Knowledge Excel offers an advanced and comprehensive range of trade-off and choice-based conjoint solutions. We simplify the application and apply the right solution to solve each specific business issue.

How does this benefit you?

One of the most important strengths of Conjoint Analysis is the ability to develop market simulation models that can predict consumer behavior to product changes. With Conjoint Analysis, changes in markets or products can be incorporated into the simulation, to predict how consumers would react to changes.


  • Designing new products
  • Product line extensions
  • Estimating brand equity
  • Measuring price sensitivity (Elasticity)
  • Branding and packaging

Designing a Conjoint Study

The foundational assumption in Conjoint Analysis is very simple - products are bundles of Attributes. These attributes (Brand, color, price, location, warranty, etc.) have at least two levels , and usually more levels options when presented in a survey

Experimental design principles are used to construct a limited number of different choice sets for testing in the survey. The number of conjoint choice sets and the configurations used are carefully controlled to ensure valid statistical estimation of the relative importance of each level of each attribute.

We prepare both Fixed and Randomized Conjoint designs which is further used in computer assisted conjoint exercises.

In our conjoint designs, we follow below principles:

Minimal Overlap: Each attribute level is shown as few times as possible in a single task. If an attribute's number of levels is equal to the number of product concepts in a task, each level is shown exactly once.

Level Balance: Each level of an attribute is shown approximately an equal number of times.

Orthogonality: Attribute levels are chosen independently of other attribute levels, so that each attribute level's effect (utility) may be measured independently of all other effects.Prohibitions: We eliminate the combinations which are not desired by researcher.

We broadly have four types of Randomized Conjoint Designs:

Complete Enumeration: This strategy considers all possible concepts and concepts within each task are also kept as different as possible.

Shortcut Method: The faster "shortcut" strategy makes a much simpler computation. It attempts to build each concept by choosing attribute levels used least frequently in previous concepts for that respondent. Unlike complete enumeration that keeps track of co-occurrences of all pairs of attribute levels, the shortcut strategy considers attributes one-at-a-time.

Balanced Overlap Method: This method is a middling position between the random and the complete enumeration strategies. It permits roughly half as much overlap as the random method. It keeps track of the co-occurrences of all pairs of attribute levels, but with a relaxed standard relative to the complete enumeration strategy in order to permit level overlap within the same task.

Random Method: The random method employs random sampling with replacement for choosing concepts. Sampling with replacement permits level overlap within tasks. The random method permits an attribute to have an identical level across all concepts, but it does not permit two identical concepts (on all attributes) to appear within the same task.

We also prepare Advanced CBC Designs, this includes:

Conditional Pricing: In pricing research, it is sometimes very useful if prices for product concepts are made to depend on other attribute levels (such as brands). The conditional pricing option lets you create a look-up table to determine the prices to show for specific combinations of attributes.

Alternate Specific Design: This is a specialized type of CBC design wherein some or all product alternatives have their own unique sets of attributes. Conditional Pricing is one example of such a design, where each brand might have its own set of prices.

Partial Profile Design: This is type of design to estimate preferences for a large set of attributes. With partial-profile designs, each choice task includes a subset of the attributes. Across all tasks and respondents, a much larger list of attributes is evaluated.


At Knowledge Excel, we have the experience to work on various types of Choice based Conjoints like:

  • Discrete Choice Based Conjoint
  • Discrete Choice Based Conjoint - Best / Worst
  • Volumetric Choice Based Conjoint - Constant Sum
  • Volumetric Choice Based Conjoint - Numeric
  • Please go into our Demo section to view our sample surveys.


Analysis of conjoint data yields a series of scores for each respondent at each attribute level. These scores, known as ‘part-worths’, are a measurement of the ‘utility’ that the consumer associates with a product and its attributes. Each score reflects the value that a respondent associates with each attribute level. Thus we are able to build a product and then calculate the value the consumer finds in that product. By comparing this outcome with other products on the market, we are able to gain a more precise understanding of how consumers actually decide which product to choose.

This will help quantify what is driving the preference from the features and levels, but more importantly, it evaluates and compares each feature and level against one another.

Please mail us at to request us for a demo conjoint utilities output.


The simulator is a stand-alone package that allows clients to conduct alternative “what-if” scenarios. Developed in Excel, a simulator is a powerful analysis tool and the most important deliverable resulting from a conjoint analysis project.

The market simulator is an effective tool for analyzing consumer preference for different product configurations among a competitive set. By fine-tuning individual product attributes and product price points, clients are able understand whether product preferences will increase or decrease.

Simulators transform the utility data from your conjoint study into a tangible tool that you and your end-clients can use. By specifying each level on each attribute of each real or hypothetical product, user defined scenario is created. The computed product utilities are used to estimate strengths of preference in terms of acceptance.

This is done by accumulating the individual estimates over respondents to predict aggregated interests in different product concepts. Because it is in Excel, you can easily share it with colleagues and end-clients to maximize use.

Below are some screenshots of our Excel Based Simulator:

Case Studies






Contact Us

At Knowledge Excel, we have been conducting Conjoint Analysis studies for over a decade and know how to use Conjoint Analysis to help you obtain the market advantage. Let's connect to discuss your requirements. Get in touch with our experts at

Knowledge Excel US Address:

  21018 Roaming Shores Terr Ashburn VA 20147, United States
  +1 (315) 642-2662

Knowledge Excel Services India Address:

  Solar Business Tower C-192 Vivek Vihar Delhi 110095 INDIA
  +91 11 43536466