Targeted Deals to Reduce Shopping Cart Abandonment

Online retailers offer various promotions to combat shopping cart abandonment. In this study, we propose that targeted deals based on consumer characteristics extracted from a firm’s online community can enhance the effectiveness of these deals. Using a unique dataset from an online apparel and accessary e-retailer, we combine a text mining and machine learning algorithm to extract consumer characteristics from their digital footprints in this e-retailer’s online community. Then, we use the corresponding individual transactional level data to show how the firm can leverage the consumer characteristics identified to reduce shopping cart abandonment among different consumer segments. We additionally show that the segmentation scheme outperforms that based on consumer demographics. These results illustrate the potential for a firm to use data science to enhance its ability to segment the consumers, to offer targeted deals and enhance its marketing effectiveness.