Topic modeling for customer returns retail data

Improving customer experience is a critical component to maintaining a successful business and can be accomplished by actively monitoring customer feedback. Online retailers typically capture feedback through ratings, comments, and surveys. While surveys broadly capture various aspects of customers’ experience, focusing on returned products can deliver greater insight on how a product did not meet the customer’s expectations. When a product return is initiated, the customer fills out a form describing the reason(s) for return. Return reason categories are often provided by the retailer in a broad manner, while the customers’ description for the return reason provides more information on why this product did not meet their expectations. Understanding product returns provides the retailer with information useful for improving customer experience and cutting down on return costs. This research analyzes return data using Latent Dirichlet Allocation (LDA) topic modeling. Analyzing product returns using LDA provides a more detailed tool to track reasons for product returns which helps observe new emerging patterns that encompass the majority of the returns. This study concluded that studying product returns using LDA is an insightful tool to understand how a product did not meet customers’ expectations. Discovering and understanding hidden patterns in customers’ product returns provides the retailer with information needed to improve the product’s online description, which helps enhance the customers’ online shopping experience and drive improved business..