How PolicyBazaar Improved Lead Qualification at Scale with Ringg

Overview

At Policybazaar, every day, thousands of users come to the platform looking for a term plan, health cover, motor policy, or another insurance product. The challenge is not generating demand. The challenge is reaching that demand while it is still warm.

That was the problem Policybazaar wanted to solve. It needed a better way to respond to the leads it was already getting, quickly, consistently, and at scale.

Why insurance lead intent cools quickly

When someone fills out a form for a term plan or health policy, the intent is often real. They may be thinking about family, financial protection, risk, cost, or timing, but that intent does not stay warm forever.

In the earlier process, a lead came in, was assigned to a relationship manager, and got called when someone was available. Sometimes that happened quickly. Sometimes it did not. At Policybazaar’s scale, even a strong human team could not guarantee that every new lead would be reached in the first minute.

By the time a customer receives a call, they may have already moved on, spoken to another provider, ignored the number, or lost the urgency that made them fill out the form in the first place.

Policybazaar needed a way to reach every lead immediately, qualify the serious ones properly, and make sure human advisors were spending their time where it mattered most.

BEFORE RINGG

Why the model worked at Policybazaar’s scale

A strong first call does not always need to be long. It needs to be timely, relevant, and structured, that is what made this system effective.

In roughly four minutes, Ringg could confirm interest, understand the customer profile, capture budget comfort, identify coverage expectations, and assess urgency. For many leads, that first AI-led conversation was enough to resolve the initial layer of qualification without needing immediate human involvement.

Instead of relying on a human RM to perform the same repetitive first filter across thousands of leads, Policybazaar was able to respond in real time and gather structured information at the start of the journey.

That changed the role of the RM completely, they arrived with context: product interest, budget, coverage requirement, and preferred callback timing. That meant relationship managers could begin from a much more informed place and spend more of their time on people who were actually ready to move forward.

Built for multilingual, on-premise insurance operations

The deployment was designed to work within Policybazaar’s existing operating environment.

Ringg was implemented using Policybazaar’s current telephony infrastructure through a BYOT model. It supported multilingual conversations across Hindi, English, and Hinglish, and was built with enterprise-grade security requirements in mind.