Conjoint analysis with XLSTAT-Conjoint

Conjoint analysis

Conjoint analysis is a marketing method that allows to know the expectations of consumers about a product and to model their choices.
The conjoint analysis method is now extremely common in marketing. Modeling of consumer choice is a key area of marketing.
Conjoint analysis is used to simulate competitive markets using a single analysis; it is its biggest advantage.

Conjoint analysis is a method that helps you to find out the expectations of consumers towards new products and to model their choices - both crucial steps of a marketing analysis. Two methods of conjoint analysis are available: full profile conjoint analysis and choice based conjoint analysis (CBC).

XLSTAT-Conjoint allows you to run through all the analytical steps of conjoint analysis which can be divided into five steps:

  1. Choice of the relevant factors and their modalities to describe the products.
  2. Generation of a design of experiments based on full factorial, fractional factorial, or D-optimal.
  3. Collection of the results in Microsoft Excel sheets.
  4. Data analysis with specific regression methods - MONANOVA (monotone regression), multinomial logit, conditional logit, etc.
  5. Simulation of new markets with various methods: first choice, logit, Bradley-Terry-Luce, randomized first choice.

These steps can be carried out both for a full profile conjoint analysis and for a choice based conjoint analysis (CBC).

In this tutorial, we will detail the steps necessary for the implementation and interpretation of a full profile conjoint analysis with XLSTAT-Conjoint.

Dataset to conduct a conjoint analysis

An Excel spreadsheet containing the results of this example can be downloaded by clicking here.

The results are divided into different sheets:

  1. Factors: this sheet contains the characteristics of the selected factors.
  2. CA Design: this sheet contains the profiles generated, and the rankings given by the 10 individuals.
  3. Conjoint Analysis: this sheet contains the results of conjoint analysis (CBC).
  4. Simulated market: this sheet contains the complete market to simulate.
  5. Market Simulation: this sheet contains the results of the market simulation.

First step: the choice of the factors

In this tutorial we will look at a classic case of conjoint analysis on the introduction of a new product in a concurential market. This product is a drink based on tea.

A brand of softdrink want to introduce a new product and in order to answer two questions, a conjoint analysis is applied. What are the characteristics that should have the drink in order to, first, please the greatest number of people, and, secondly, gain market shares in an already concurential market?

The first step in the conjoint analysis is done in collaboration with experts in the beverage market. It is the choice of the important characteristics to define a drink.
The selected factors are:

  1. Temperature (very hot, hot, iced)
  2. Sugar (no sugar, 1 sugar, 2 sugar)
  3. Lemon (yes, no)
  4. Intensity (strong, medium, light)

From these factors, you can get 54 different products. Judges will not be able to evaluate all these products. So we will use experimental designs to reduce the number of products presented to the respondents.
The obtained profiles will be ranked by 10 interviewed people.

Second step: the selection of the profiles

XLSTAT-Conjoint uses experimental designs to select a number of profiles and allow interviewed people to make their rankings.

Once XLSTAT is started, click on the CJT icon and choose the function Design for conjoint analysis.


Once the button is clicked, the dialog box appears.

You can then enter the name of the analysis, the number of factors (four in our case) and the number of profiles to be generated (12).


In the "Factors" tab, use the option of "select on a sheet" and select the data in the "Factors" sheet. Do not select labels associated to each column.


In the Output tab, do not activate the individual sheets in the case of this example because the generation of these sheets is not necessary. In a comprehensive analysis, they can be very useful in order to fill the results directly by individuals.


Once you click the OK button, a new dialog box appears. This allows you to select the fractional factorial design of experiments or to optimize the design (D-optimal). We use the "optimize" option.


Once you click the Optimize button, the calculations are made, then the results are displayed.

The first table summarizes the generated model.


The second table is the table of the conjoint analysis with the profiles on the left. The right part of the table has to be filled with the rankings of the respondents.


Step 3: Fill the conjoint analysis of tables

The conjoint analysis tables can either be filled directly after interviewing individuals about their choices externally or directly using the individual sheets and automatic referencing of results.


Step 4: Results of the analysis

As part of this analysis, 10 individuals have been questioned about their preferences in terms of tea.
The results are in the "Conjoint Analysis" sheet.

To start the analysis, click the icon CJT and choose the function conjoint analysis.


You can then select the data.
Select the 10 columns of the conjoint analysis table completed using the rankings of the individuals (right part of the table) as responses. Select the four columns associated to the profiles as profiles (without the names of the profiles). Choose the ranking option as response type.


In the options tab, select the MONANOVA method in order to apply a monotone transformation to the responses.


Once you click the OK button, the computations are performed and the results are displayed.

The most important results are the partial utilities as well as the individual importances. They can be found in the first tables.
We see that the utilities are individual as well as importance.


Their averages are calculated and give an idea of ​​the importance of each factor.


These first results show that temperature is the most important factor both at the individual level ant at the average level. The fact that the drink is ice has a largely positive if you look at the utilities (in terms of averages).

XLSTAT-Conjoint allows to make segmentations of the individuals by using statistical clustering methods. This option allows to see if homogeneous groups of individuals emerge.

Step 5: Simulation of the market

The main advantage of conjoint analysis is to simulate a market even if the products in the market have not been tested by the individuals.

In our case, the market for a tea-based beverages is analyzed and we would like to know the impact and market shares associated to a new product.
This product is a strong iced tea with lemon and no sugar. We know that in today's market there are 4 tea-based beverages that have different characteristics, the following table shows the simulated market:


To start the simulation, click the CJT icon and choose the function conjoint analysis simulation .


You can then select the data.
Utilities are those obtained in the "Conjoint analysis" sheet, the table of information about variables is that obtained in the "Conjoint analysis" sheet. The simulated market is in the simulated market sheet (do not select the names of products). You can also select the name of the product just behind the Product ID button.
Select the Full profile model and the logit method for simulation.


Once you click the OK button, the calculations are performed and the results are displayed.

The table shows that the market share for the new product are almost 30%. This result seems satisfactory in order to launch the product on the market.


The associated pie chart validates our interpretation.


Much more advanced analysis are possible with XLSTAT-Conjoint (use of segmentation variables, weights, use of statistical clustering methods...).

Click here for other tutorials.

About KCS

Kovach Computing Services (KCS) was founded in 1993 by Dr. Warren Kovach. The company specializes in the development and marketing of inexpensive and easy-to-use statistical software for scientists, as well as in data analysis consulting.

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