# How to Leverage Data from Conjoint Analysis Exports

Let’s take another critical dive into conjoint analysis, a super informative market research feature that we released last year.

As you know, a Conjoint Analysis is a kind of tool and research technique that enables you to measure the value that consumers place on the individual features of a product or service.

This kind of analysis is particularly beneficial for product and pricing research, as it unveils a plethora of consumer preferences. You can then use this information to optimize your products and services.

In this article, we specifically focus on Conjoint Analysis exports on the Pollfish platform.

The Polfish market research platform generates optimized Excel, CSV and SPSS files that contain raw data, which you can use to form your own analyses and import the data from the tool to other software.

If you want to run your own Conjoint Analysis to compute and understand part-worth importance, you’ll need to use its export, as it contains raw data from your Conjoint Analysis.

## 1. How does Pollfish generate the Conjoint Analysis design?

The conceptual model of a conjoint analysis is pretty straightforward; it suggests that the utility of a multi-attributed product can be broken down into specific contributions of each attribute and their interactions.

The approach is easy to implement if the number of attributes is small. However, problems arise because of the large number of possible hypothetical alternatives for a given product.

For practical reasons, only a subset of possible alternatives is chosen for the study. To that end, experimental design methods exist for selecting good subsets of product configurations for performing the analysis.

Respondents will see a subset of possible options to choose from, which depends on the number of attributes and levels defined.

In order to do this, the Pollfish Conjoint algorithm first creates an orthogonal design of the alternatives (product bundles) that are formed by combining the levels of the attributes.

In the case that all possible combinations are more than 20, the total number of alternatives gets reduced using d and g optimality criteria.

Then choice sets are created, containing an upper limit of 5 alternatives each. The algorithms that we are using ensure the d-optimality of the choice-set design.

Finally, choice sets are assigned to blocks. One block cannot contain more than 30 choice sets.

The goal for our Pollfish Conjoint algorithm is to ensure that each level in each attribute appears the same number of times and that each alternative appears the same number of times in the design, so that the results have 0 bias and variance.

Each respondent gets assigned to only one block randomly. Blocks are distributed evenly to the audience, so that every choice set in the design is seen by the same number of respondents.

## 2. How is the Conjoint Analysis design distributed? Via the Pollfish algorithm based on Block Designs

A problem that usually arises when the choice-set design is ready to be administered to a target population is that the number of choice sets in the design may be too large for an individual to assess.

In these cases, a common technique is to partition the choice sets into blocks of equal size, in principle much smaller than the size of the original design, and then administer the blocks to the population.

That way, each individual would have to assess only the choice sets of one block, while collectively the population would assess all the choice sets of interest. However, in order for this partitioning to make sense, some constraints must be satisfied.

Each choice set should be evaluated by the same number of users. That means that each choice set should appear the same number of times into the blocks. Note that it is not necessary for a choice set to appear in all blocks. To that end, we may need to replicate the choice sets in order to always be able to divide them into blocks.

Formally speaking, the following equation should be satisfied: vr=bk

where v  is the number of choice sets, and r is the replication factor of each choice set, is the number of blocks and is the size of each block, that is, how many choice sets each individual should see. Thus, given a choice-set design of size, we have to find the three smallest numbers () satisfying the above equality. Given these numbers, we can safely partition choice sets into blocks.

## 3. Understanding the Excel structure

The Excel (CSV and SPSS) file is a complementary feature of the visualized results at the Pollfish dashboard. It contains all responses given by each respondent of the survey. Graphs on the results page are based on these individuals' responses.

To take advantage of the Excel for a Conjoint Analysis or MaxDiff Analysis survey, visit the results page of a completed one, select “Export results” and choose “Excel” as your export option. You will be sent an email that notifies you when your export is complete. Open the document in Excel or Google Sheets to get started.

The Excel sheet will include the following two (among the other tabs):

• Experimental design tab: This tab contains the Conjoint or MaxDiff’s design, as generated by the Pollfish Conjoint algorithm.
• Individuals tab: This tab contains the selections given to the questions of the survey by each respondent.

For the case of the CSV and the SPSS, the experimental design comes in a separate file.

### A. The Εxperimental Design Tab

Let’s assume that we have a Conjoint Analysis survey with the following attributes and levels, describing alternatives of detergents:

In the Excel tab, each component is listed in a column and has a unique ID. Blocks, their containing choice sets and alternatives(or “Concepts”) have their own IDs, as the following screen depicts.

For the specific example, the Pollfish algorithm will generate a design of 4 blocks of 5 choice sets each.

Each choice set contains 5 alternatives (or “Concepts”). Each row represents a generated alternative (or “Concept”), whose containing attribute levels’ columns are located after the “Concept” column (in our example of detergents Conjoint test, the attributes were 4: Type, Perfume, Quantity and Price).

The settings for the number of choice sets and the number of alternatives, are also displayed in the questionnaire while preparing the Conjoint survey:

### B. Conjoint selections at the Individuals tab

Each respondent gets only one of the generated blocks. The blocks are evenly distributed across the sample, hence, a block may be received from many respondents.

In our example, from the 120 total sample, each group of 30 respondents will get one of the 4 blocks:

• 30 respondents receive the block with ID 257
• 30 respondents receive the block with ID 258
• 30 respondents receive the block with ID 259
• And 30 respondents receive the block with ID 260

At the Individuals tab, each row represents the responses given to the questions by a single respondent. Data related to the Conjoint Analysis are placed among the other questions contained in the survey and start with the column named “Block” which holds the block ID the respondent received.

Then the choice-set ID columns follow along with the selected alternative (or Concept) ID columns. The choice-set columns are ordered according to the order the respondent received them.

So, ChoiceSet0 ID holds the first choice-set ID the respondent got. Selected Concept 0 ID holds the respondent’s selected alternative ID(or Concept)  in the first choice set and so forth until selections for all choice sets are provided in the tab.

### C. Correlate the data at both tabs for further analysis

You do so by combining the following:

• the data from the Experimental design tab, which holds the content for each block and their included choice-sets
• the data from the Individuals tab, which hold the selections per respondent

You can then proceed by then analyzing the data in a different system.