I am tasked with deciding whether a new product should be developed. I have data of customer transactions of previous products and wish to build a model which determines whether customers will have a higher propensity to buy this new product.
My approaches in mind:
Look at the average daily sales for each product, then find the closest product (using Euclidean distance) to the new product based on a set of covariates. If the closest product isn't the highest-grossing, then decide not to launch.
Build a classification model based on the transactional data and the covariates of each product. For each transaction a customer makes, augment the dataset by assigning 0 to the products that wasn't bought by the customer. The model thus works out the predicted probability that each customer buys each product at a given point in time. Then use as input a series of times, and the covariates of the new product to work out the average propensity score for the new product.
How plausible is such an approach? Is there a more statistical/data science approach to answering this question?