increase in sales
increase in agent utilizationand productivity time
Policybazaar.com is an online insurance policy aggregator that helps people find the best insurance policy to fit their needs and prides itself on bringing transparency to the insurance industry in India. The company realized they needed a tool to help streamline the sales and operations departments — it was no longer okay to use stale Excel reports to make decisions. That’s where Ayush Mittal, Head of Data Science, came in. It was his role to take Sisense and apply it to the company’s data to improve efficiencies and drive policybazaar.com forward with data. Following implementation, policybazaar.com saw a 15% increase in efficiency and a 10% increase in sales.
With Sisense we have the flexibility for everyone to pull out data and do whatever they need to do, whenever they need to do it.
The Challenge: Stale Reports and Wasted Time
Policybazaar.com’s challenges can be categorized into two different areas. First, they have a call center that is home to 1,500 agents. On a daily basis, their agents call leads to encourage them to convert and purchase an insurance policy. Data was being gathered by their CRM as well as the call agents’ software but couldn’t be mapped together in a clear way to gain any insights for streamlined operations.
Second, analysis was constrained and entirely dependent on the analytics team. Employees requested reports that were then fielded by the analytics team, but by the time the data had been gathered and pulled into Excel reports, it was out of date and very limited in terms of the questions it could answer. If employees ever needed more information than what was already in the report, they went back to the analytics team to request another report. The whole process was manual, resource heavy, and hindering the rapid growth and decision making that leadership wanted.
The Search is on
Although Ayush was not with the business when they first searched for a solution, he knew that in order to expand and grow their business, policybazaar.com needed a scalable tool. To address their other challenges, they needed a BI software that could combine all of their data sources, rapidly run reports, was easy for anyone to use, and would free up their analytics teams to focus on other projects that were core to the business.
Streamlining for Increased Sales
In order to tackle the call center challenge, a system was put into place via Sisense to classify leads. Every time a person signs up on policybazaar.com’s website, they become a lead. Leads are then assigned out to call agents to drive conversions. Prior to Sisense, leads were assigned out in very broad strokes and didn’t take into account any detailed data such as the location of the lead, the age of the lead, or the lead source. Ayush knew that this was leading to a conversion rate that was lower than it should be.
To fix this problem and streamline the leads process, Ayush took all lead data and mapped it against agent data, including how many leads an agent receives versus how many they convert and how long an agent spends on the phone per day. Besides lowering the dependency on IT to get this sort of information and create structured reports, this allowed Ayush to match the right lead to the agent with the best chances of converting that lead. Since implementing Sisense, policybazaar.com has seen a 10% increase in sales across each vertical of insurance they sell.
With Sisense, any meeting that we start ends up with a conclusion because we can analyze multiple parameters at one single go.
For years, the executive team at policybazaar.com had been hoping and looking for a way to start doing predictive dialing for agents in the call center. They believed that in order to improve efficiencies even more, agents shouldn’t be spending time looking for who they needed to call. They should be given the appropriate leads — already optimized for them.
With Sisense, Ayush has been able to do exactly that with their car insurance group. First, he needed to look at the types of leads agents were manually picking up, the amount of effort it took to convert a lead, how callbacks were treated versus leads receiving a first-time call, and what was the optimal time period for converting a lead.
Following the analysis, he put together a task list of calls for agents that is optimized to ensure the most conversions in the shortest period of time. Agents no longer waste time looking through records to decide who they should call — the system does it automatically for them. Since implementation, they have seen a 15% jump in agent utilization and productivity — all through the power of Sisense.