increase in sales
increase in agent utilizationand productivity time
Policy Bazaar, an online insurance policy aggregator that helps people find the best insurance policy to fit their needs and prides itself in bringing transparency to the insurance industry in India, needed a tool to help streamline their sales and operations departments. As the company grew it realized 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 Policy Bazaar forward with data. Following implementation, Policy Bazaar 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
Policy Bazaar’s challenges can be categorized into two different areas. First, they have a call center that is home to 1,500 agents who, on a daily basis, call leads to try and get 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 and streamline their operations.
Second, the analysis that Policy Bazaar was able to do was entirely dependent on the analytics team. Employees would have to put in a request for a report and then the analytics team would go out and gather the data and create reports in Excel. This meant that once you got a report it could easily be out of date and was very limited in terms of what questions could be answered. If employees ever needed more information than what was already in the report they would have to go back to the analytics team and request another report. The whole process was resource heavy, manual, and hindering the rapid growth and decision making leadership wanted.
The Search is on
p>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, Policy Bazaar needed a scalable tool. In order to address their other challenges, they needed a BI software that could combine all of their data sources, run reports on a rapid basis, was extremely self-service and 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 Policy Bazaar’s website, they become a lead. Leads then need to be assigned out to call agents to try and get them to convert and purchase a plan. 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 what source the lead came from. Ayush knew that this was leading to a conversion rate that was lower than it could be.
To fix this problem and streamline the leads process, Ayush took all of the lead data and mapped it against agent data, including how many leads an agent gets 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, by doing this, he has been able to match the right lead to the agent best suitable to converting that lead. Since implementing Sisense, Policy Bazaar 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 Policy Bazaar 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 just be given the appropriate leads that were 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 what kinds of leads agents were manually picking up, the amount of effort it took to convert a lead, how leads that are callbacks were treated versus how first-time calls were treated, and what the optimal time for leads to convert was.
Following the analysis, he put together a task list of calls for agents that is optimized to close as well as save time. Agents no longer have to spend time looking through records and trying to decide who they should call because 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.