Improving Collection Percentage of Active Agent Base

Transcription

1 Key words: Life Insurance Policy, Six Sigma, CTQ, Disillusioned Customers, Active Agents, Green Belt, Yellow Belt, Black Belt, Quality Tools, Matrix diagram, Fishbone Analysis, Pareto Analysis. Abstract This paper is about progression in the relationship of a company with its customers. The daunting challenge was to demystify as to why the customers let go of their precious investments and simply walk away and to win back disillusioned customers of a Life insurance company. A Project Charter was prepared which included Business Case, Problem Statement, CTQ with POD & POA & the team members were identified for this project. Project Scope was determined by using CTQ Tree Diagram as Greater than 180 Days Lapsed Customer Base of Active Agents. Further, a deep dive into the data helped the team to identify that collection from Active Agent base was 9% (Average) for the period, October-2010 to January This was contributing towards the dip in overall collection percentage of Business Recovery Unit (Renewals & Retention). Six Sigma Methodology was adopted and that helped us to deal with this issue in a focused and fact based manner. Introduction Max Life Insurance, a joint venture between Max India Limited and Mitsui Sumitomo Insurance Co. Ltd. is one of the prominent Market Leader in Life Insurance in India and provides a comprehensive life insurance and retirement solutions for long-term savings and protection. We are a financially stable company with sound investment expertise, Max Life Insurance has a strong customer-centric approach focused on advice based sales and quality service. Max Life Insurance Protects Life through its Life Insurance Company and Mitsui Sumitomo Insurance, Japan; Cares for Life through its Healthcare company. Project Journey Max Life has two sources of Revenue, viz. 1) New Business and 2) Renewal Income. From 2009 Renewal Income is forming a major contribution toward the Max Life Revenue. At this Year, contribution is more than 75% of the total Max Life Revenue income. Through an annual exercise, list of Improvement & opportunities were identified which were further aligned with Organizational Goals and then were categorized into Black Belt (BB), Green Belt (GB) & Yellow Belt (YB) projects based on Complexity Impact Matrix. When the project started, we observed a high impact on our key Strategic Pillar Persistency as well as revenue enhancement. Hence a separate focus in the process was built by mandating a Black Belt Project on Lapsed Customer Base of Active Agent Base Portfolio which was named as Unnati. 134

2 The word Unnati means Progress and this project is really about progression in the relationship of a company with its customers. The daunting challenge was to demystify as to why the customers let go of their precious investments and simply walk away and to win back disillusioned customers of a Life insurance company. A Project Charter was prepared which included Business Case, Problem Statement, CTQ with POD & POA & the team members were identified for this project. Renewal Premium Declining Conservation Ratio Falling Persistency Customer Dissatisfaction Increased Recovery Cost Agent s Dissatisfaction Lapsed Portfolio Project Scope was determined by using CTQ Tree Diagram as Greater than 180 Days Lapsed Customer Base of Active Agents. Further, a deep dive into the data helped the team to identify that collections from Active Agent base was.9% (Average) for the period, October-2010 to January This was contributing towards the dip in overall collection percentage of Business Recovery Unit (Renewals & Retention). Project Unnati CTQ Drill Down Renewal Income Active Portfolio Lapse Portfolio Days >180 Days Active Agent Inactive Agent 60% 40% Six Sigma Methodology was adopted and that helped us to deal with this issue in a focused and fact based manner. We contacted over 500 customers/ agents to identify the reasons for the lapse. As a result of this, three main 135

3 reasons came out, viz. 1) No contact by Agent, 2) Customers dissatisfied with the products & services and 3) Low fund value etc. Why Policy Lapse s???? I am not satisfied with the product sold to me I can t pay the complete amount in one go. I want to pay in installments My agent has not got in touch with me after I bought the policy Voice of Customer ~ 500 customers contacted I am extremely dissatisfied with Max I did not know that I can reinstate my policy. I thought now my money has been forfeited Your process is too complicated. I don t want to pay for medicals In spite of paying a huge amount, my fund value is low I am not happy with the Agent service Post that we identified Quick Wins through Brainstorming and Process Walkthrough and through its implementation we could improve collection rate by 20% which was validated statistically by using 2P test. Next, in the Measure Phase, we used two pronged approach, viz. 1) Analysis and Base Lining of Project CTQ and 2) Identification of Root Causes. Approach 1 6 Sigm 2 Metho a Proc Analysis & Base Lining of the Project CTQA d ess Im provem ent Project Mapping SIPOC Top Down Chart Functional Deployment Chart Identification of the Possible Root Causes 5 Whys Ishikawa Diagram Pareto Diagram Failure Mode and Effect Analysis In Analysis and Base Lining of Project CTQ, the project team analysed 3 months data and Process Deep Dive was done using Process Mapping, SIPOC, Top Down Chart & Functional Deployment Chart. These tools helped the us in detailed identification of Key Process Steps, Suppliers, Customers, Input Output and involvement of Cross- Functional Units in the process. For Identification of Possible Root causes, the team & Stakeholders came together, brainstormed and identified 57 possible root causes by using few technical tools like 5-Why, Ishikawa Diagram and Failure Mode & Effect Analysis (FMEA). 136

4 Multiple sessions of Brainstorming were done with ground staff and Fishbone Analysis and Cause & Effect Matrix were used for identifying and prioritizing 8 potential root causes. Through FMEA we were able to identify the Key Process Input Variables having more Risk Priority Number than the threshold. Out of these 8 identified potential root causes, we were able to arrive at 7 final root causes by applying Pareto Analysis, and FMEA. analysis was done on each data door X s to check the Statistical Validation. For Process door X s, FMEA was used to validate the causes. Results were shared with the stakeholder through meetings and reviews. Hypothesis testing was done to validate the impact of X s. The first significant factor was Annualized First Year Premium (AFYP). The - was used to statistically validate the same. Through this test we were able to identify that this factor had an impact; the policies where the premium to be paid was as low as 10K were being lapsed most and thus were contributing the most for the low collection. A further drill down exercise on this base helped us to draw the inferences like which zone, which agent, what frequency etc. had an overall impact on low collection percentage. The second significant factor found was ZONE which is nothing but East, South, North and West from where the business was sourced. Statistically validation of the same helped us identify that the East zone was the highest contributor followed by North and South Zone. The third factor was Ageing which is Policy Premium Slabs, from 7 to 12, 13 to 24 and 25 to 36. They are segmented for proper tracking monitoring the performance. Policy slabs of 13 to 24 were found to be the highest contributors. A further drill down study helped us to gage further on zone, frequency etc. The forth factor was Single and Multiple Policyholder. Single Policyholder is one owing a single policy of Max Life while Multiple Policyholder is the one owning more than one policy. Our hypothesis got validated and it was found that the Multiple Policyholder contributes the highest and with drill down on this segment by taking zone, agent type ageing etc further snapshots were formed The fifth X was Business Partner Performance, where Statistical validation was not done. The sixth X was Agent Type which comprises two categories of agent, viz. 1) Active Agents 2) MDRT Agent/EC/5 Years Agent. It was found that the Vintage agents were the highest contributors to low collection %. We further used Pareto analysis to identify which occupation base is contributing to low collection base. This study was shared with Agency Team for further planning and action. The seventh and eighth Xs got validated through FMEA Failure Mode and Effect Analysis. A Pareto was also done to identify which all Disposition 137

5 Codes, i.e. calling comments are impacting the Overall and also the Paid and Unpaid base. Thus, out of a total of 8 Xs, 5 got validated by using, 1 was not validated and rest two get validated by using FMEA tool. S.No. Xs Identified Hypothesis Validated Outcomes Test Used P Value Highest Contributions X1 AFYP wise Ho:-AFYP does not have impact on collection of Active Agency Base HA:-AFYP has impact on collection of Active Agency base Less than 10K AFYP Premium Policies X2 Zone Ho:-Zone does not have impact on collection of Active Agency Base HA:-Zone has impact on collection of Active Agency base East, North and South Zone X3 Ageing Ho:-Ageing does not have impact on collection of Active Agency Base HA:-Ageing has impact on collection of Active Agency base 13th to 24th Month 24th to 36th Month X4 Single and Multiple Policies Ho:-Single and Multiple Policy does not have impact on collection HA:-Single and Multiple Policy has impact on collection Multiple Policies X5 Business Partner Performance Ho:-Business Partner does not have impact on collection of Active Agency Base HA:-Business Partner has impact on collection of Active Agency base NA X6 Agent Type Ho:-Agent Type does not have impact on collection of Active Agency Base HA:-Agent Type has impact on collection of Active Agency base MDRT/EC/5 Yrs X7 Disposition Code Which Codes are contribution highest as per business partner feedback FMEA N.A. CB/RNR/PTP X8 Agent Engagement Interaction of Agent with Customers FMEA N.A. Status At Max Life Insurance we use a 3 Step approach for Generation, Prioritization and Validation of Solutions. By doing Brainstorming & Benchmarking, we generated possible solutions using this approach: Step 1 Generation of Possible Solutions Step 2 Prioritization of Possible Solutions Step 3 Validation of Possible Solutions Final Solutions were selected using the 4 Selection Criteria namely. CTQ Impact, Time Impact, Cost Impact and Ease. For every project each selection criterion is classified either Mandatory or Optional. For example, for this project the CTQ Impact and Cost Impact were Mandatory and other two were optional. If the solutions failed to meet any of them they were automatically eliminated. Each criterion was aligned with Organization Goals. 23 possible solutions were measured through selection criteria mapped with Impact on Organizational Goals to arrive at 16 final solutions. 138

6 M CTQ Impact Project Performance Indicator O Time Impact Time to Implement M Cost Impact Cost of Implementation O Ease Simple to Implement Final solutions were selected using a Prioritization Matrix incorporating the selection criteria.this ensured that only viable solutions were selected. The team scored each solution on a scale of 1 to 10, 1 meaning low and 10 meaning high against all criteria used. Each solution was mapped on the Solution Selection Priority Number Scale and solutions which scored greater than 550 were selected as final solutions. As per the Solutions Selection Priority Number Rating Scale, 16 final solutions were selected for implementation. Solution Selection Matrix CTQ Impact + + Time Impact Cost Impact + Ease Solution Selection Priority Number l Fina tions Solu Solution Selection Priority Number Scale Low 1 Medium 550 High Thus, out of 23 solutions, 16 were implemented successfully. I will take you through few of them. 1) An Agent engagement drive, namely Get your customers back to life was launched. Through this drive Agents were motivated and encouraged to revive their existing lapsed customer base. The platform of Agent Meet 139

7 2) 3) 4) 5) 6) 7) 8) Town Hall helped in the implementation of this solution. We went to the extent of explaining the agent of How to revive back the customer. A dedicated Id was especially created for the agents to send their feedback and FAQ s The objective of this project was Do Simple Things to achieve big. With this in mind, we initiated YB projects at potential locations in selective zones with the help of our Field Quality Team. Contest and campaigns always help in motivating and engaging the people around. We created a Campaign Calendar on different segments like product base, frequency base etc. Among these one was Paisa Talk through which we made our agent realize how commission was related to revival of a Policy and if so much of existing customer base would get retained by him, how much money they would be able to earn. Another was Sangrakshan, which had an objective to cover those customers who fall under the slabs of 13 to 24 and 24 to 36. Customers were covered via SMS and ers and we were able to create a Fear of Loss in them by explaining how their choice of plan would be impacting the future of their family.we were able to receive good response from this targeted ordinance from all impacted zones. Robust monitoring, capturing feedback, resolving FAQ helped our business partners & selective agents to perform at their best. Involvement of Site Mangers at HO level helped in registering good growth in Active Agent Base. During our analysis phase, we identified that the multiple policy holder, disposition code like CB, PTP, and RNR etc. were the highest impacting factors. As a team we built a science around this and logic was revised for BRU Allocation Strategy. This solution was so effective that it helped in sustaining the growth as on date. It is always important for any unit to keep the knowledge of their employees/outsourced staff updated. Looking up the fact Objection Handling Scripts were created and existing scripts were relooked upon. Train the Talent theme was adopted and was implemented with all business partners/site managers. Daily dashboard of CRE was used for performance monitoring and was published to keep them on track. During the analysis phase, it was identified that the customers with less than 10K premium were the highest contributors. As it is a huge base, calling them was seen as a challenge. So we decided to get in touch with them via SMS. SMS were sent to customers of this base and the agents were equipped with their lapsed customer base with priority as per allocation logic, like zone, single and multiple policyholders etc. through personalized mails. Post Implementation of solutions, 2P tests were conducted to statistically validate the results achieved. Since the P Value was less then alpha value (0.05), the impact of final solutions was validated. 140

8 Validation Tools 2P Test Pilot Testing Control & Sustenance Method Control Chart Process Control Sheet The results were spectacular and went beyond our expectations, whether it was revenue generation, customer conservation ratio or customer satisfaction scores. We broke all previous records by revenue generation of Crores annually. Also, we are proud to share that we became the best in the industry in the Conservation ratio at 84%. This is a key measure tracked in the competitive Indian Insurance Industry. We saw a surge in customer satisfaction scores. The top 2 box score increased from 46 to 51 with significant drop in bottom 2 box.other parameters like Ease of Paying, Call Quality Scores and CTA scores also showed encouraging and good results. 141