Chris is a business analyst who likes to practice data modeling in her free time.
She particularly enjoys building analytical models to achieve marketing objectives. For example: clustering models for auto segmentation, propensity models for customer lifetime value predictions, and attribution models for channel evaluations.
This is a blog for Chris to practice her analytical skills and connect with like-minded people.
All in Consumer Sentiments
Unlike conjoint analysis that requires consumers to rank the level of each product attribute, discrete choice analysis need not to do that. Conjoint analysis uses multiple linear regression whereas discrete choice analysis adopts logistic regression, using maximum likelihood estimation and the logit model to estimate the ranking of product attributes for the population represented by the sample.
Conjoint analysis enables analyst to determine the product characteristics that drive a consumer’s preference for products. Through conjoint analysis, analyst will be able to learn the relative importance of the product attributes and within each attribute the ranking of the levels.
As we can see from the data, we already know the real choices of these 333 commuters. Now it’s time for us to build a predictive model and see how it works on the training data. Today we’ll use a linear combination of the four explanatory variables to predict commuter choice. We will also add a code in the end to evaluate the predictive accuracy of our model.
