Our client is one of the Best General Insurance Companies in Asia and they received the Insurance Regulatory and Development Authority (IRDA) certificate of Registration in 2001. They are renowned for their outstanding customer service levels and innovations in the field of insurance. They have been generating continuous profitability over the years. Today the company has a country-wide network in over 200 towns spread from Surat to Siliguri and Jammu to Thiruvananthapuram. Their Head Office is located in Pune.
Workforce size: 800
Levels: Team Leaders (Level 1) and FOS (Level 2)
The company’s full-time insurance sales people (called the FOS - Feet On Street) have the following activities as their deliverables: Sale of Motor Insurance, Renewals and Premium collection. The FOS team approaches prospective insurance purchasers in their territory to offer them motor insurance. When the buyer purchases motor insurance, it is called an ‘Policy sale’ (expressed as a Policy Sold Count). When a buyer renews their insurance policy, it is called a ‘renewal’ (this is expressed as ‘Yes’ or ‘No’ entry). The total premium collected by the FOS is expressed as ‘Net Premium Collected’.
While there are monthly reports of Policy Sold, Renewals, and Net Premium collected sent out to the FOS team and their Team Leaders, they need to raise a request to get an update on these numbers, and resultantly the incentive they are eligible for. It takes an average of a day for this report to reach them or their manager.
The company wanted this performance data to be more transparent and readily available for their FOS team. They also wanted to use this data to nudge the team to perform better and recognize the high achievers.
Compass Program design, User Management, and Data Sources & Variables features were used to construct the below-given incentive logic.
The FOS are eligible for the incentive if only the following conditions are met:
1. Achievement Conditions:
At 6 - 9 Private Car Policies: Incentive is calculated as Rs. 250 * No. of policies sold
At 10 -12 Private Car Policies: Incentive is calculated as Rs. 300 * No. of policies sold
At 13 - 19 Private Car Policies: Incentive is calculated as Rs. 400 * No. of policies sold
At >19 Private Car Policies: Incentive is calculated as Rs. 500 * No. of policies sold
2. Nudge Conditions:
Target milestone wise nudges for:
Cumulative Policy Sale Milestones: 6, 10, 13, 19
Frequency: Weekly
Medium: email, SMS and push notifications on Compass app
Performance warning nudge
The FOS who have not achieved a minimum 75% of their milestone target and 25% net premium targets over a period of 2 months, are notified with a performance warning nudge to improve their performance - this way they can work on improving their performance proactively. (They tend to face separation with continued performance issues.)
Compass Groups feature was used to send out team messages that offered the below:
The Compass dashboard feature offered branch and regional managers to view the below:
The impact of the above Compass implementation was evaluated using:
Heavy top-down drive, TTT (train the trainer) programs, and training webinars helped drive awareness.
The Team Leaders and webinar (indicating top-down messaging) contribute to 85.7% of the download compared to Whatsapp (Grassroot messaging).
The successful awareness initiative leads to a successful number of downloads (92.9%).
The users were asked to review the app based on their perception of the below benefits on the rating scale: 1 - Highly Dissatisfied, 2 - Moderately Dissatisfied, 3 - Neutral, 4 - Moderately Satisfied 5 - Highly Satisfied.
The maximum adoption rate was achieved within the first 15 days of implementation.
This is a function of the top-of-the-mind recall, curiosity and high frequency of touchpoints (via Webinar, TTTs and Launch mailers) with the end users.
Average logins per week stabilised at 2.2 at the end of 45 days
This indicates that beyond the first 2 weeks, the users tend to access the Compass app 2 to 3 times a week.
Average time spent on Programs Section was 3.17 minutes per user
The primary purpose of Compass implementation was to create Program Awareness and access to the Performance Progress of FOS. A higher time spent on the Programs section (3.17 mins), proves that the application served this purpose.
To understand how the app impacted performers and improved performance, performer and non-performer cohorts were studied.
Performer cohorts were found to have logged in more (254 logged in out of 274); rated the Compass app better (4.8 rating); and understood and engaged with the app more (average quiz score was 29.7 out of 40). The total number of performers increased over the period (from 259 to 379) and even their average performances increased (from 206K to 255K average net premium per FOS).
While Compass-implemented as well as the not-implemented cohorts of the team grew in performance (Compass from 95K to 129 avg. net premium per FOS; others from 69K to 96K avg. net premium per FOS), the Compass cohort saw a sustained growth through June (Compass cohort growth improved from 22% during May to 27% during June, other cohort growth decreased from 36% during May to 14% during June).
To understand perspectives of Team Managers a survey was conducted with close to 50 managers. Here is the snapshot of the survey results.
To summarize, managers consider Compass an essential tool to improve performance. They rated Compass high on parameters* such as - Viewing Earning Potential (4.3/5); Live Incentive Tracking (4.48/5); Viewing Product-Wise and Segment-Wise Incentive Details (4.39/5); Live Performance Reports (4.48/5); and Overall FOS performance (4.48/5).
*These parameters were identified only after detailed conversations with the Managers.
The following is a snapshot of how the FOS Compass implementation fares against the Compass industry benchmark:
* The logins/users/week and Performance Contribution are lower than our benchmark because this client has only signed up for our incentive calculation and display solution instead of our standard end-to-end incentive calculation + redemption solution.
To conclude our findings from the the FOS case study: