How Policybazaar adopted chatbot and callbot to ramp adult patron experience

With a marketplace share of 50 percent among online insurers and 90 percent among aggregators, Policybazaar claims to be a largest online space to review and buy policies. It provides life, health, motor, corporate and ubiquitous insurance. According to a company, ever given a pregnancy in 2008, it has been witnessing 100 percent year-on-year growth.

This fast-paced expansion was putting extensive vigour on Policybazaar’s height where millions of word buyers transacted with some-more than 40 insurers. As business total and acclimatisation rates soared, it became humanly unfit to support to patron queries effectively.

“We hoop half a million calls any day. The scale during that Policybazaar operated, there were hurdles associated to receiving patron calls, servicing them and updating business with a scold information. It was removing formidable for a call centres to answer any and any call,” says Rahul Agarwal, CTO, Policybazaar.

With a design of ‘customer initial and postulated profitability for a whole word ecosystem,’ a association motionless it was time to muster cutting-edge record to waves over operational bottlenecks.

Bots to a rescue

Policybazaar realised a approach brazen was lay in leveraging discuss bot and call bot technologies powered by AI, ML, speech-to-text technology, and Natural Language Processing (NLP) algorithms.

For addressing a half a million calls perceived daily by a call centres, Policybazaar grown a home-grown call bot called Polly. Whenever a patron calls, a call bot’s speech-to-text (STT) algorithms figure out what he/she is observant and what is a intent. The system, that is powered by AWS, afterwards translates text-to-speech (TTS) and replies to a query. The complement is totally programmed with no tellurian intervention.

“The record algorithms figure out what a patron wants. Polly afterwards helps to voice-out a answer to a patron so redressing his grievances,” avers Agarwal.

All a messages are available and transcribed to content by Policybazaar’s exclusive speech-to-text technology. The NLP and ML are means to figure out a sentiments of a business and capacitate improved assembly segmentation.

The discuss bot — PB bond – also maximises these underlying technologies to answer patron queries effectively. Not usually does it assistance business with information per their claims, it also tells them a updated standing of a new process that they have practical for.

In box a few papers need to be filed again for approvals and processes, a discuss bot conveys a scold message, observant “document x needs to be filed again” instead of what a progressing summary of “problem in filing documents.”

Instant redressal

Together, a discuss bot and a call bot have helped Policybazaar in achieving a ‘customer first’ policy.

During a rise deteriorate any year, there is a outrageous spike in two-wheeler insurance. The association used to fastener with this challenge. With a discuss bot and call bot, policybazaar has been means to solve 70-70% patron queries.

Earlier when a shade flashed ‘problem in filing documents,’ a patron had to write an email seeking for a reason. With a formation of these bots, they now now get to know where a emanate is and what needs to be done.

With a bot technology, Policybazaar has turn some-more efficient. It is now means to implement tellurian resources in a improved way.