Consumer Complain Classification
Good customer service is a cornerstone of any well-run business because, of course, without customers what have you got? Undoubtedly the most important part of providing good customer service is properly managing consumer complaints.
Customer feedback can come from anywhere, not just through emails, phone calls, and chatbots. Customers leave unsolicited feedback about your products and services on social media, online forums, pretty much all over the internet. And spontaneous feedback, more often than not, is negative because that’s when customers feel most compelled to voice their opinions – when they’re unhappy.
These online complaints are only growing by the day, and they can be difficult to get a handle on. Furthermore, surveys show that 18% of customers who comment or complain on social media expect a response immediately, while a full 83% expect it to come the same day. And nearly 50% of customers reported that they’d stopped doing business with a company due to poor customer service.
But how can you respond quickly and effectively to all these customers, coming from all corners of the internet (as well as through regular customer support channels)?
Customer complaint classification with machine learning is the answer. AI-powered text analysis tools can monitor customer comments – wherever they may appear – find out which are complaints, automatically route them to the right team, and analyze them for immediately actionable insights.
In this application, we use the consumer complaints dataset which contains consumer complaint narriative in 11 categories: 'Bank account or service' 'Consumer Loan' 'Credit card' 'Credit reporting' 'Debt collection' 'Money transfers' 'Mortgage' 'Other financial service' 'Payday loan' 'Prepaid card' 'Student loan'