A Seenu Srinivasan of Stanford Graduate School of Business has created a model which can help predict the course of product upgrades:
“The model is quite simple,” says Srinivasan. It is based on how much the benefits of the new product (as compared to the old one) outweigh all the factors that typically hinder a customer’s decision to upgrade. For example, a customer is more likely to buy a new PC if it is significantly better than the one she already owns and if the upgrade seems painless and inexpensive. In this model, the hindrances include not only the upgrade’s various costs (financial, procedural, and psychological), but also expectations about how quickly future technological improvements will be made; consumer characteristics (such as innovativeness); and the consumer’s perceptions of the product in general (such as whether or not it saves time).
Academicians are excited because the model is an innovative mix of two existing methodologies in marketing science: conjoint analysis and hazard rate modeling. Conjoint analysis, which involves asking a sample of customers from the target market how important they deem different features, has long been used to determine which sets of product features to offer. But because conjoint analysis takes a static snapshot of the marketplace at a given moment, it alone doesn’t answer the sorts of questions intrinsic to product upgrades. Hence the addition of hazard rate modeling, which has traditionally been used to estimate the time difference between a product’s first purchase and subsequent, replacement purchases.
As might be expected, the greater the gap between the incremental benefit of the upgrade and its hindrances, the greater the probability that the consumer will upgrade within a given month.
