BPO stands for “business process outsourcing.” In short, it’s a business practice we see implemented when an organization decides to outsource activities like payroll, human resources, billing and customer service. The best example of this is customer service because we all have experienced speaking with someone from a different country when we’ve called a bank or had an issue with a credit card and needed it resolved.
We will not spend any more time discussing BPO, but our technology conversation in this article will be focused on improving customer service. Now, recall an incident when you called your credit card company. You were likely asked to press 1 for English, press 2 for Spanish and then several options were presented before you finally get an option to press a number to talk to a real human. Next came the verification process where you had to provide your first and last name, then your date of birth, then your secret answer, or pin, or maybe the last four digits of your social security number. Finally, a CSR (customer service agent) validates your identity and you have an opportunity to ask questions. At this point, the customer service agent may have full access to your call history and any other interactions that you had with them in the past.
So what’s the role of machine learning in all this?
Now, imagine a smart system where you are automatically redirected to a smart agent (or a digital agent) who knows that you are calling in to talk to a customer agent because you were on the website or app looking for answers to a particular question. You even interacted with the chatbot, but your question was not answered. Your calling number and voice can be used to verify your identity to search your record instead of spending the time to look up your information. There are machines behind the scene ingesting, processing and analyzing this interaction in real time and predicting that you are about to call the customer service.