First, respondents are notoriously bad at self-reporting their future purchase likelihood of products within market research surveys. Conjoint analysis is not immune to the over-exaggeration. So, you cannot use conjoint results to “determine” the purchase likelihood of products. You can estimate it, given what respondents tell you in the survey, however. But, exercise caution in interpreting the results, as it is only as good as respondents self-reporting and will probably demonstrate too high of purchase intent vs. reality.
ACBC’s “None” share of preference can come from two sources, depending on how you do it. 1) If you do not include the optional “Calibration” section at the end, then the None threshold is taken from the “Consideration” phase of the survey, where respondents are asked for each of many product concepts if they would consider it or not (binary logit specification). The “or not” option is the None threshold. 2) If you include Calibration concepts at the end of the survey, then the None threshold can be replaced by the threshold the researcher specifies as associated with the 5-point purchase intent scale used in that optional series of questions. If you say that a “4” on the 5-point scale marks the boundary between non-purchase and purchase, then the part-worth utility associated with a 4 on the 5-point scale is used as the new None threshold, etc.
The Purchase Likelihood market simulation method was designed for use with ACA and CVA ratings-based conjoint results (if you use single-concept presentation in CVA with the logit transformation on the dependent variable). It uses the formula:
100*e^Ui / (1 + e^Ui)
Where Ui is the total utility for the product concept in question. This math is not suited for CBC or ACBC to predict any sort of stated purchase intent. If using the Purchase Likelihood simulation transformation as above with CBC or ACBC, the interpretation should be: “what’s the likelihood of picking this product concept as specified within the market simulator vs. a product concept with a utility of zero (since e^0 = 1). Also, note that because we typically zero-center the utilities within CBC and ACBC, the zero-utility concept represents a product concept of “average” utility based on the attributes and levels you specified in your model.
Next, if you use either Share of Preference (logit rule) or RFC in our simulators vs. the None concept (one product vs. the None concept), then you need to interpret the results based on how you estimated the None parameter. If it comes naturally out of the Consideration phase of ACBC, then the interpretation of the simulation output is “the likelihood that this product would be viewed as ‘a possibility’ by respondents”. If you use the None calibration from the optional Calibration section in ACBC, then the interpretation is “the likelihood that this product would exceed the purchase threshold as specified by the researcher”.
The math for the Share of Preference vs. the None would be:
100 * e^Ui / (e^Ui + e^None)
Where Ui is the utility for the product placed in the market simulator and None is the utility for the None threshold.
For example, if the researcher specifies that the purchase threshold is a “4” on the 5-point scale, then a product that achieves a utility equal to the utility associated with a 4 on the purchase intent scale would get a 50% RFC or Share of Preference prediction vs. the None concept. Once the product achieves higher utility than that associated with the “4” on the purchase intent score would the probability of selection instead of the None exceed 50%.