Product Insights: Analyzing Product Intents in Web Search

Web search engines are frequently used to access information about products. This has increased in recent times with the rising popularity of e-commerce. However, there is limited understanding of what users search for and their intents when it comes to product search on the web. In this work, we study search logs from Bing web search engine to characterize user intents and study user behavior for product search. We propose a taxonomy of product intents by analyzing product search queries. This itself is a challenging task given that only 15% - 17% of queries in the web refer to products. We train machine learning classifiers with query log features to classify queries based on intent with an overall F-1-score of 78%. We further analyze various characteristics of product search queries in terms of search metrics like dwell time, success, popularity and session-specific information.