Gunjan Gupta, Senior Vice President and Head of Analytics at Bajaj Allianz Life Insurance, is a noted industry veteran who heads the company’s analytics practice. In a candid interview with Analytics India Magazine, Gupta talks to us about why a change of paradigm for the insurance industry is crucial. Due to rising costs, challenging regulatory environment, increasing customer expectations, and digital disruption, the insurance industries are realigning business models to harness analytics and artificial intelligence to compete with their digital counterparts.
According to Gupta, analytics as a function is dynamic, constantly evolving — and that’s why the challenge is to continuously innovate to stay ahead of the market. “As a company, we are staying ahead of the curve by adopting and executing quickly on newer trends like Machine Learning, AI, Customer focused analytics, Bots, APIs, Data lakes, Cloud etc.,” said Gupta.
Gupta’s team is responsible for mining the data and generating actionable insights from them to enhance customer experience. The data is mined with the objective of providing goal-based solutions to customers at an optimal price via an optimal channel. “Once the customers are on board, we study their behavior to deploy custom life-stage engagement strategies to ensure we are with them to achieve their goals,” she said. The team leverages analytical tools like SAS, Alteryx, Qlik, Python, R, Google Analytics, amongst others and has a mix of business analysts, data scientists, data modelers, visualizers, and data engineers working together. The organization has deep partnerships with the academia in which the private life insurer provides industry and business context to students who are pursuing analytics programs. This ensures the financial service provider a steady stream of business-ready analytics talent that can hit the ground running.
Using Predictive Analytics
Bajaj Allianz Life is leveraging predictive analytics across the customer lifecycle. “Upsell propensity models and recommendation engines help in selling products as per the life goals of the customers. Our renewal nudge engines help in optimizing collections efforts, while agent productivity scorecards and attrition risk models help in improving the efficacy of distribution,” said Gupta. The insurance company also has a fraud identification trigger at the time of claims investigation which helps in minimizing losses. Claim prediction models further help in pricing their risk. The insurance company also deploys customer household level analytics along with geospatial tools to improve customer engagement and service. “We also have a robust forecasting competency that helps with the planning and targets,” she added.
Understanding The Role of Personalization At Bajaj Allianz Life
Personalization plays a key role in the insurance industry today and the insurers are increasingly relying on data-driven insights to reprice policy premiums, becoming more client-centric. Talking about segmenting techniques, Gupta shared the team segments the customer base on the basis of varied parameters as well as through different techniques (supervised or unsupervised ML), which gives the team a good understanding of a customer’s buying patterns and attributes.
The team further conducts interviews and focus group discussions to define personas around these segments and also discover micro-segments. Based on these segments, the team has developed personalized engagement and retention strategies. “For example, our most valued customers are serviced through a set of experienced and skilled operations personnel. The younger segments that prefer online interactions are contacted via SMS or emails and encouraged to sign up for online services,” she said.
Embracing AI Applications
Bajaj Allianz Life has started the AI journey with chatbots. Their chatbot Boing has been developed to deepen customer engagement level and help customers get instant information. In addition there are employee and distributor chatbots. “Chatbots improve customer experience at critical touchpoints with the organization. These chatbots are learning and evolving in real time to eventually become one-stop, personalized shops for their intended purpose,” revealed Gupta.
Try deep learning using MATLAB