Combining Behavior and E-mail Content to Improve Customer Support


Authors: Stephane Fotso, Philip Spanoudes, Benjamin C. Ponedel, Brian Reynoso, and Janet Ko

Introduction

One of our primary objectives at Square is to provide exceptional customer experience, especially when the customers are entrusting the company with their financing. This is even more critical for us at Square Capital, the lending arm of Square, where our sellers are also borrowers. Our operations team is devoted to this goal and provides consistent, fast and tailored responses to seller inquiries regarding loan products.

Every year, the team receives tens of thousands of emails from sellers regarding our products. In order to provide consistent responses to inquiries, operations agents (OA) manually classify emails into more than 20 different categories. For example, these include topics such as loan cost explanations, loan eligibility requirements and loan prepayment. OA then base their responses on the template email associated with the category. Over the years inquiry volume has increased in tandem with loan origination volume, prompting the need for a data science solution. The Square Capital Data Science team sought to alleviate some of the email volume by developing a deep learning model that surfaces topical information to the seller about their inquiry immediately after submission.

To accomplish this, we partnered with Square content and legal teams to create easy-to-read articles corresponding to each category that contain similar information to the email the operations team would have sent as a reply. Upon submission of their inquiry, the sellers are presented with the relevant articles and may remove themselves from the servicing queue if the content solves their problem. Because written inquiries can typically be linked to multiple categories, we constructed our model to provide the top three most relevant articles relating to the inquiry.



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