We don't offer that calculation automatically, but you can compute it pretty easily.

First, you compute the prior likelihood that a person belongs to the segments. That's simply equal to the size of the segments. For example, if:

Segment 1 has 50% of respondents

Segment 2 has 30% of respondents

Segment 3 has 20% of respondents

...then those are the prior likelihoods of a person belonging to the segments, before we see any data (any choice data from that respondent).

Next, we compute the likelihood that each segment chooses concept A instead of Concept B in a choice task. You can do that via aggregate logit, or any other technique (e.g. latent class or HB) that allows you to build a model with a subsequent market (choice) simulator to predict the likelihood that respondents in each segment choose A or B. Let's imagine that we build a model (a market simulator) that predicts the likelihood of respondents in each segment picking concepts A vs. B and the results are:

Segment 1: A=50%, B=50%

Segment 2: A=20%, B=80%

Segment 3: A=70%, B=30%

Now, imagine we observe a new respondent who picks A when given that same choice between A and B. What's the likelihood that she belongs to segment A, B, or C? It's proportional to the prior likelihood times the likelihood of picking A given the segment membership, or:

Segment 1: (0.5)(0.5) = 0.25

Segment 2: (0.3)(0.2) = 0.06

Segment 3: (0.2)(0.7) = 0.14

...but those three posterior probabilities don't sum to 1.0, so we need to normalize them to sum to 1.0 by dividing each by the sum (0.25+0.06+0.14) = 0.45, leading to...

Probability of a person who picks A in the set {A, B} belonging to each segment:

Segment 1: 0.25/0.45 = 0.55555

Segment 2: 0.06/0.45 = 0.13333

Segment 3: 0.14/0.45 = 0.31111

Sum = 1.000

This is called a Naive Bayes classifier and it is the same approach that we use with our MaxDiff Typing Tool, which generates a small number of "golden" MaxDiff questions to ask respondents to assign them with high accuracy to an existing segmentation scheme.

I don't completely understand what you're saying here:

"Next, we compute the likelihood that each segment chooses concept A instead of Concept B in a choice task. You can do that via aggregate logit, or any other technique (e.g. latent class or HB) that allows you to build a model with a subsequent market (choice) simulator to predict the likelihood that respondents in each segment choose A or B. Let's imagine that we build a model (a market simulator) that predicts the likelihood of respondents in each segment picking concepts A vs. B and the results are:"

I figured out how to make a choice simulator and how to get the percentages, but only for all my respondents together, but I want to have the different percentages for my 2 segments. Is there something I should check or do in the simulator to get these for 2 groups separately? I got the results with my Logit model.

I hope you can explain this, thank you very much!