Max New York Life Insurance Used BI To Increase Cross-Sell Revenue By About 13 Percent

Summary:

Hitesh Arora, CIO, used a Business Intelligence (BI) solution that enabled accurate predictions and forecasts that has helped the company increase revenue from cross selling to more than 20 percent of revenues — from 7 percent. Read the case study of how this BFSI player maxed revenue with a BI solution.

Highlights:

Thanks to the BI tool building a model now takes two days — from three weeks. The frequency of marketing campaigns has increased. Max New York Life Insurance uses BI analytical tools to understand his demographic profile, his insurance priorities, his policy requirements and purchasing patterns.

Reader ROI:

How to best leverage BI tools to boost revenueHow to use crosss elling as a means to

Organization: Studies indicate that it’s seven times more expensive to cultivate a new customer than it is to sell to an existing one. But cross selling isn’t easy. Between multiple business units, conflicting interests and numerous databases, most companies in India haven’t dared to take on cross-selling in an organized fashion. Max New York Life Insurance (MNYL) isn’t one of them.

The BI project gave us a very strong end-to-end integrated piece with analytical support and that clinched the deal.

Business Case: A joint venture between Max India and New York Life, a Fortune 100 company, Max New York Life started operations in April 2001. Today, the private insurer has cast net across 389 cities and 139 rural districts and has over 40 lakh customers.Executives at Max New York Life knew that the size of their customer base could be their greatest strength — if they cross-sell to them — or be their greatest weakness because their very numbers made it hard to retain them. Cross-selling could fix both problems. “We’ve found that the retention probability for a customer goes up 300 percent to 400 percent once they make a second purchase with us,” says Nagaiyan Karthikeyan, head of business intelligence and analytics at Max New York Life. Anisha Motwani, chief marketing officer, Max New York Life Insurance agrees. “Cross-selling is better than coldwalking into a new customer and making a pitch ground-up. It’s one of the most effective ways of increasing your share of wallet and expanding your relationshipwith customers.”

Challenges: Yet only 1 percent of its customersowned two or more MNYL policies. The problem was, like many other growing organizations, Max New York Life Insurance was hamstrung by legacy systems which were not geared for a cross selling push. The result? A measly seven percent of the life insurer’s revenue came from sales to existing customers. Executives at the insurer were determined to grow that number and formulated a strategy: they would createnew customer segments (according to their profiles) and introduce products that matched their needs. “If we could analyze customer data and categorize them into tight segments of customers with similar requirements we could create products and policies that suit the needs of that customer segmentand run highly-focused campaigns. But in order to design effective marketing campaigns we need to look at the customer closely. We require analytical tools to understand his demographic profile, his insurance priorities, his policy requirements and purchasing patterns,”says Motwani. “But in the absence of an accurate data warehouse we were ill-equipped to do any of this. We couldn’t even segment our customers accurately.” “Access to business critical information was a problem,” agrees Hitesh Arora, EVP and head-IT, Max New York Life Insurance. “We were not in a position to harvest information to identify revenue opportunities from existing customers.”

Project: For that rich harvest Arora knew they needed a centralized repository of customer data. And he decided to invest more time in the project. His focus makes sense. According to studies, a 2 percent increase in customer retention has the same effect on profits as 10 percent worth of cost optimization. MNYL’s management was quick to appreciate the need to accumulate customer data across all its touch points and multiple distribution channels into a unified data warehouse. So they decided to embark on a BI project. After eight months of scouting for a solution, Arora zeroed in on one. “We settled on this one because it has a more structured approach towards analytics. And we found it more suitable for the kind of analytics that we want to run. The BI solution has given us a very strong end-to-end integrated piece with analytical support and that clinched the deal,” says Arora. The BI project took off in the second quarter of 2008 and it brought results quickly. “Not only does it clean data but it also pumps the right information to the right people at the right time. Accurate predictions and forecasts have helped us do away with guesstimate,” says Arora. Today, the BI project provides increased support to Max New York Life Insurance’s sales force. Earlier sales were dependent on an agent’s understanding of a customer’s demographics. But now the BI tool gives them that data. This means that agents are better prepared when they approach a customer because they understand his needs more closely. That’s left the marketing department very glad. “We now have access to the right data and the right models. Consequently, high-margin revenue from cross-selling has increased three-fold,” says Motwani.

The BI project gave us a very strong end-to-end integrated piece with analytical support and that clinched the deal.

Benefits:In the first quarter post the BI implementation, revenue from cross selling rose to more than 20 percent of revenues — from 7 percent. The project also improved revenue by nearly 40 percent with shorter sales cycles (since it’s faster to sell to a known customer). And thanks to quicker access to data, building a model now takes two days — from three weeks. That has a tangible effect on the marketing teams. “The frequency of marketing campaigns has increased. Earlier we conducted two, broadly-targeted campaigns each quarter. Now, with rapid modeling, tightly-defined customer segments and accurate data, we’re executing 60 campaigns a month. This has made us more agile,” says Motwani.