Blazeclan Helped A Leading Insurance Service Provider Gain 30% Reduction in Development Cycle

A leading life insurance provider, headquartered in Mumbai, was aiming to shift away from its traditional way of driving sales that was formed on a Broadcast-based incentive scheme. The customer wanted to empower its Front Line Sales (FLS) executives with a mobility-based solution to realize its incentive aspirations and track incentive accruals. Blazeclan helped the customer by developing a serverless stack of service on the AWS cloud for data-driven predictive analysis of FLS. This is done by feeding real-time data generated from complex business transformations to a stack of algorithms.

Requirements Put Forth by the Customer

The company’s operations are driven through various sales channels. It aims to change its traditional way of steering the sales force based on Broadcast based incentive scheme. The company wants to empower its Front Line Sales (a.k.a. FLS) executives with a mobility based solution to view their personalized tasks which can help them to meet their incentive aspirations and track their Incentive accruals.

The FLS Incentive & Task Mobile App will predict the prioritized tasks which are personalized to improve incentives, past behavior, and business objectives. The backend of this App drives through multi-layered complex data transformation of front line sales information captured through various existing/external systems and running them through a stack of Algorithms. The program is designed to address productivity and attrition challenges at an FLS level. The objective is to maximize incentives to kickstart the virtuous cycle of productivity.

Blazeclan’s Solution to Help Customer Enhance its Front Line Sales

Blazeclan helped the customer by creating a unique system that will reshape their sales process completely. The approach followed includes key steps as described below.

• Microsegmentation of the FLS

Based on the demographics data and performance, the front line sales has been branched into 80 distinct fragments. This pushed the relevant tasks to FLS and identified the incentive ranges for every task.

• Task Allocation

Machine learning algorithms are used to provide personalized actions to front line sales. The price points provided will maximize the incentives earned by the average FLS. The personalized tasks are displayed by considering their shortcomings and performance.

• Lead Allocation

The algorithm used for lead allocation will generate ranks for the order of front line sales against new leads, resulting in the best probability of conversion. An agent recommendation engine is deployed for predicting the first stage of eligible FLS pitched for the lead. A product recommendation engine is deployed for predicting the first stage of eligible products pitched for the lead.

• AWS Services

Serverless architecture is proposed for faster development, deployment, testing, and easy maintenance. The proposed solution involves the data pipeline along with the use of AWS Lambda, ECS fargate, DynamoDB for processing & storing the data. Auto-scaling based on traffic & data load making the backend systems operate at AWS scale, security, and performance. 

The application is a PWA ionic and hosted on Amazon S3. By exposing the public-facing resources such as S3 and API Gateway using CloudFront ensured the secure delivery of applications and data to customers. The AWS DynamoDB is used for storing the NoSQL, non-structured and transactional data. For active monitoring, all logs are sent to CloudWatch to gain a unified view of the applications and resources of the company that will run on the AWS cloud.

This data will be queried by the AWS Athena, which works directly with data stored in Amazon S3. Athena has been used for creating reports that have helped in deriving insights for further analysis by the company. CloudFormation, Jenkins, and Git are used for managing continuous & versioned deployments.

Benefits Realized by the Customer 

• Better visibility into the linkage between performance and incentive.

• Ability to visualize earnings.

• Contextualized motivation (stretch but achievable nudges) which can drive immediate performance and boost earnings.

• A lower proportion of low performers.

• Improved Gini coefficient for incentive distribution.

• Leading and lagging sales metrics tracked and incentivized.

• Lower front line sales attrition.

Value Additions

Tech Stack