Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application

Building on the distinction between search and experience goods, as well as vertical and horizontal differentiation, we propose a set of theory-grounded, data-driven measures that allow us to measure not only product type (search vs. experience, horizontal vs. vertical differentiation) but also sources of uncertainty and to what extent consumer reviews help resolve uncertainty. The proposed measures have two advantages over prior methods: (1) unlike prior categorization schemes that classified goods as either search or experience goods, our measure is continuous, allowing us to rank-order the degree of search versus experience and horizontal versus vertical differentiation among products or categories. (2) Our approach is easier to implement than prior methods, because it relies solely on consumer ratings information (as opposed to expert judgment) and can be employed at multiple levels (attributes, products, or product categories). We illustrate empirical applications of our proposed measures using product rating data from Amazon.com. Our data-driven measures reveal the relative importance of fit in driving product utility and the importance of search for determining fit for each product category at Amazon. Our results also show that, while ratings based on verified purchasers are informative of objective product values, the current Amazon review system appears to have limited ability to resolve fit uncertainty. Our method and findings could facilitate further research on product review systems and enable quantitative measurement of product positioning to support marketing strategy for retailers and manufacturers, covering an expanded group of products.