Conjoint.ly helps managers understand what drives customer behaviour through surveys that simulate the act of purchasing (or any other type of choice-making). It offers a complete online solution from experiment set-up to data analysis and presentation of reports on marginal willingness to pay, market share simulation, segmentation, and more.
Conjoint.ly uses discrete choice experimentation, which is sometimes referred to as choice-based conjoint. DCE is a more robust technique consistent with random utility theory and has been proven to simulate customers’ actual behaviour in the marketplace (Louviere, Flynn & Carson, 2010 cover this topic in detail). However, the output on relative importance of attributes and value by level is aligned ot the output from conjoint analysis (partworth analysis).
Conjoint.ly uses the attributes and levels specified in the interface to create an unlabelled choice design based on a D-optimal linear design (Kuhfeld, 2010). If the number of choice sets is excessive, the experiment is split into multiple blocks. Each choice set consists of several product construct alternatives and, by default, one “do not buy” alternative.Minimum sample size.
Conjoint.ly automatically recommends a minimum sample size. In most cases, it is between 50 and 300 valid responses (which means you will need to invite several hundred respondents given that probably not everyone will take part). In our calculations, we use a proprietary formula that takes into account the number of attributes, levels, and other experimental settings. Conjoint.ly estimates a hierarchical bayesian (HB) multinomial logit model of choice using responses deemed valid.
The value (partworth) of each level reflects how strongly that level sways the decision to buy the construct. Attributes with large variations in the sway factor are deemed more important. Specifically, we calculate attribute importance and level value scores (partworth utilities) by taking coefficients from the estimated model and linearly tansforming them so that:in each attribute, the sum of absolute values of positive partworths equals the sum of absolute values of the negative ones, and in each attribute, the sum of the spreads (maximum minus minimum) of parthworths equals 100%.
For experiments where one of the attributes is price, Conjoint.ly estimates a separate logit model that allows the calculation of the marginal rate of substitution between the price attribute and the non-price attribute. Conjoint.ly also performs checks for appropriateness of calculation of the measure, taking into account both the experimental set-up and the received responses (for example, limiting MWTP calculation in cases where there is non-linerity in price).Market share simulation is performed using individual coeffients from the estimated HB multinomial logit model.Ranked list of product constructs.
Conjoint.ly forms the complete list of product constructs using all possible combinations of levels and ranks them based on a score computed from the relative level value scores (partworths).Segmentation. Conjoint.ly segments the market based on the individual coeffients from the estimated HB multinomial logit model using k-means clustering. We provide the values of the Calinski-Harabasz criterion (to help choose the appropriate number of clusters) and the normalised (0 to 1) Dunn partition coefficient for fuzzy k-means (to help choose the appropriate number of clusters as well as to decide if segmentation at all is crisp and hence appropriate). We provide the same reporting for each segment."
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