Using Text Analysis to Improve Chargeback Management and Customer Experience
Overview:
Before the introduction of ChatGPT, I implemented advanced NLP techniques to analyze customer feedback. I automated the scraping of Google Reviews, Trustpilot, and other platforms to extract insights from customer experiences. As soon as ChatGPT became available, I integrated the API to streamline the classification process, allowing us to process data faster and more efficiently.
In response to the growing problem of unauthorized – fraud chargebacks (friendly fraud), I initiated a customer feedback program for chargeback cases. We introduced email responses to customers, which significantly boosted our recovery rate by providing direct evidence of customer verification, challenging claims of unauthorized purchases.
Beyond reducing friendly fraud, these analyses gave us a clearer view of underlying issues contributing to chargebacks—ranging from customer service shortcomings to website errors—enabling us to drive meaningful improvements across the business.
Challenge:
In the chargeback space, unauthorized – fraud chargebacks, or friendly fraud, are a major challenge. These cases often occur when customers falsely dispute valid transactions, claiming the purchase was unauthorized. One of the most effective ways to counteract friendly fraud is to provide proof of customer interaction or verification to contradict the claim.
Additionally, other types of chargebacks, such as those related to billing issues, cancellations, or customer service, needed to be addressed. Identifying these patterns allowed us to provide insights to relevant departments and improve business operations.
Impact:
The text analysis initiative drove several important outcomes:
Increased Recovery Rates: By including customer feedback as evidence in chargeback responses, we successfully challenged more friendly fraud cases.
Operational Improvements: Reports identifying common issues (e.g., refund delays, cancellation policy inconsistencies) led to improvements in customer service and website functionality.
Better Collaboration Across Teams: Insights from chargeback feedback enabled departments to address root causes and enhance customer satisfaction.
Solution:
Text Analysis with NLP and ChatGPT Integration
Initially used NLP techniques and automated the web scraping of reviews from Google, Trustpilot, and other sources to identify customer feedback trends.
Once ChatGPT became available, I integrated the API to automate the classification process, allowing us to analyze customer feedback more efficiently and with greater accuracy.
Chargeback Feedback Program and Email Response
Launched an initiative to gather customer feedback during chargeback disputes. This feedback was provided as evidence to refute unauthorized chargeback claims.
Implemented a chargeback response email, including proof of customer interaction and verification during the transaction, which improved our recovery rate.
Identifying Root Causes Across Chargebacks
Analyzed chargeback feedback to uncover common issues, such as cancellation policies, website errors, billing issues, and service inconsistencies.
Created custom reports to show the impact of specific problems on chargeback volumes, helping other departments address the root causes.