Performance Reporting and Analytics of Operations of Contact Center

A project report submit ted in part ial fulfilment for the requirement of two year Post Graduate Diploma in

M anagement (f inance) 2012-14

By Subhav Budhia

(262/ 2012- PGDM Finance)

Lal Bahadur Shastri Inst itute of M anagement, New Delhi

June 2013

1

2

LETTER OF TRANSM ISSION

To: Prof. Alok Pandey Date: 8th July 13

From: Subhav Budhia PGDM (Finance), 2012-14 Batch

Subject: A Report on Performance Report ing and Analyt ics of Operat ions of Contact Cent re

Sir,

I, Subhav Budhia, hereby, submit to you the Summer Internship Project Report on Performance Report ing and Analyt ics of Operat ions of Contact Cent re.

I, under the guidance of M r M ayuresh Karnik, Associate Director, Edelweiss Tokio Life Insurance, was able to successfully complete the project .

This report comprises of the learning and the job performed at the above stated prest igious organizat ion.

I also declare that the content of this report is my original work and has not been submit ted to any other university or inst itute either in full t ime or part t ime for the award of any degree, diploma or fellowship.

Yours sincerely

Subhav Budhia 262/ 2012 PGDM - Finance Lal Bahadur Shast ri Inst itute of M anagement

3

Acknowledgement

A successful project means a lot of efforts, not only on the part of the student but each and every person who is associated with the student in making the project achieve its object ive.

Right f rom deciding the KPIs/ metrics to making the project online, the role of few people has been instrumental. This project would not have been a success without immense contribut ion and support of Mr Mayuresh Karnik, Associate Director, Edelweiss Tokio Life Insurance, Mumbai. He has been a true inspirat ion during the ent ire process. He has been t ruly instrumental in point ing out all errors and mistakes and suggest ing better ways of doing work. He lef t no stone unturned to ensure that the project is well researched and encompasses his knowledge and expert ise.

I am also thankful to M iss Shraddha Kapoor, Mr Sandeep Singh and M r Surendre Singh. They have been really kind in helping me t hroughout the project whenever I faced any hurdles and have ensured that the final project comes out well.

I am really thankful to M r P Sreekumar, VP-Sales Operat ions, Edelweiss Tokio Life Insurance, M umbai, for giving me an opportunity to be a part of this organizat ion and bestowing faith on my capabilit ies.

I extend my thanks to the ent ire team at Edelweiss Tokio Life Insurance for their valuable t ime and support throughout the project. Their cooperat ion has helped me immensely and made the experience of the internship programme an enriching one.

Finally, I want to express my warmest thanks to Ashish Garg, Coordinator of PGDM (F), Lal Bahadur Shastri Inst itute of M anagement, Dwarka, New Delhi and Dr Alok Pandey, Professor of Finance, Lal Bahadur Shast ri Inst itute of M anagement, New Delhi to cont inuously guiding me throughout the project.

These two months have been a very fruit ful experience and I hope with my whatsoever limited knowledge I am able to contribute to the organisat ion and the inst itute in some way.

4

Executive Summary

These are challenging t imes for the insurance indust ry. Strong competit ion, savvy consumers and new channels for customer interact ions are driving insurers to seek more cost-effect ive ways to conduct business. At the same t ime, regulators are forcing greater transparency on insurers and the cost of compliance can be a difficult pill to swallow amid the urgent need to reduce operat ing costs. And when you consider the internal pressures of low investment returns and increased risk on assets, it s clear that insurers who wish to thrive in this environment must gain a specific set of capabilit ies that will allow them to: Build a customer-cent ric business model Find profitable ways to sustain growth Develop new, competit ively priced products Increase claims efficiency and effect iveness Improve capital management and investment decisions Improve risk management and regulatory report ing

The common thread in this marketplace narrat ive is how to ut ilize large quant it ies of information or what is now called big data. For those t hat are able to harness it , big data represents a huge opportunity. Insurance providers already have access to massive volumes of information about their customers and the organizat ion. However, much of this information and the insight into business outcomes it contains are unused or not leveraged to its full advantage.

Insurers can access big data on the est imated 1 bill ion cars and small t rucks on the road globally, the stat ist ical tendencies of consumers to shop for and purchase auto and home insurance online, global weather data that affects claims, and expansive fraud detect ion and prevent ion metrics to name just a few obvious sources. To be competit ive, insurers must be able to ext ract the business insights embedded within all this information.

As many organizat ions now realize, the key to unlocking the value within all that data is business analyt ics. According to research from The Economist and IBM , organizat ions that adopt analyt ics achieve significant benefits compared those do not, including: 1.6 t imes greater revenue growth 2 t imes greater EBITDA growth 2.5 t imes stock price appreciat ion

In this report , you will learn the pract ical applicat ions of business analyt ics/ business intelligence for Edelweiss Tokio life insurances contact center. The main object ive was to have performance report ing of every aspect of contact center and also to ident if y areas for any process improvement init iat ives.

The report gives an overview of the dashboards which were created based on each managers requirement. It establishes the reason for inclusion of different metrics in the dashboard. Along with this, the report describes how the dashboard can be used to ident ify the trigger point for process improvement init iat ives.

5

Table of Contents

TABLE OF CONTENTS ........................................................................................................................................ 5

OVERVIEW ....................................................................................................................................................... 6

INTRODUCTION ................................................................................................................................................ 6

ABOUT EDELWEISS GROUP ............................................................................................................................... 7

ABOUT EDELWEISS TOKIO LIFE INSURANCE .................................................................................................... 10

FINANCIALS ................................................................................................................................................... 10 PRODUCTS .................................................................................................................................................... 11 M ANAGEM ENT TEAM ...................................................................................................................................... 11

LITERATURE REVIEW....................................................................................................................................... 12

A DEFINITION OF ANALYTICS ?....................................................................................................................... 12 ROLE OF BI ANALYTICS IN INSURANCE ............................................................................................................... 13 ANALYTICS SUPPORT FOR CUSTOM ER ACQUISITION ............................................................................................. 18 ANALYTICS SUPPORT FOR CUSTOM ER RETENTION ............................................................................................... 19 USING ANALYTICS TO PRIORITIZE AND FOCUS EFFORTS ........................................................................................ 20

RATIONAL BEHIND THE PROJECT .................................................................................................................... 22

TIMELINE........................................................................................................................................................ 22

FLOW OF THE PROJECT ................................................................................................................................... 23

THE DASHBOARDS .......................................................................................................................................... 26

M ASTER DASHBOARD ...................................................................................................................................... 26 DETAILED DASHBOARD ..................................................................................................................................... 30 EM PLOYEE-WISE DASHBOARD ............................................................................................................................ 35

CUSTOMER CENTRICITY M EASURE .................................................................................................................. 38

NET PROM OTER SCORE .................................................................................................................................... 38 CUSTOM ER EFFORT SCORE ................................................................................................................................ 40

SUGGESTION .................................................................................................................................................. 42

LIMITATIONS .................................................................................................................................................. 43

LEARNING ...................................................................................................................................................... 43

REFERENCES ................................................................................................................................................... 45

GLOSSARY ...................................................................................................................................................... 46

APPENDIX ....................................................................................................................................................... 47

6

Over view

Being an indust ry, which works on the principle of Law of Large numbers, Insurance indust ry today has to grapple with Big Data to arrive at meaningful informat ion. With the rapid changes facing the indust ry, a good business intelligence tool can provide a deeper, incisive insight into the mult iple facets of the insurance business. A good BI tool can help the management in improving performance of key business levers like channel productivity, customer ret ent ion and claims management . It can help insurers by providing crucial informat ion to help them ident ify the t rends and device appropriate business st rategies.

Introduction

The operat ions and products of insurance companies are quite complex in nature. The products of insurance companies can be broadly grouped into Life insurance and Property & Casualty insurance. Life insurance, Health and Pension forms the further break up of life insurance products while M arine, Fire, Automobile, etc forms the product line for Propert y & casualty.

In Post 2008 scenario, wherein we observed the fast paced changes in regulatory framework, volat ilit y in market place and changed preferences of customers lead to simultaneous mult iple challenges to be encountered by the insurance companies. The key to success in these turbulent t imes is to align and device products and processes to effect ively meet up with the unique requirements of the customer. It is important for insurance companies to rely on quality BI tools, which will effect ively leverage on the huge volume of data in the repository of the insurance companies.

7

About Edelweiss Group

Incorporation Date: 21st November 1995 Headquarter: Mumbai Website: www.edelweissfin.com Industry: Financial Service Group Businesses: Credit including Retail Finance, Capital M arkets & Asset M anagement, Commodit ies and Life Insurance. Group comprises of 52 ent it ies including 43 Subsidiaries and 8 Associate companies Revenue (FY13): Rs 16,707 M Net Profit (FY13): Rs 1,277 M Employees: 3,362 M arket Presence: 224 offices across 109 cit ies in India and overseas M arket Listings: NSE: EDELWEISS; BSE: 532922

Business Overview

CREDIT Companys primary offering in the financing business includes collateralized loan products such as loans to corporate, sponsor funding, loans against shares, IPO financing, loans against ESOPs and margin funding etc.

Retail Finance Edelweiss launched its retail finance business in H2FY11 in order to diversify its asset class and client segment in the credit book. As a part of this, the housing finance subsidiary init ially launched its home loans business in M umbai and has expanded to 13 metro or major cit ies in India. The business offers home loans, loans against property and lease rental discount ing. SME Finance has been recent ly launched in Q1FY13 as a part of Retail Finance business.

Debt Capital M arkets The Debt Capital M arkets Desk focuses on originat ion, sales, t rading and research. It has gained a strong foothold and visibilit y in the market. Its clients in the recent past included large corporates like RIL, Aditya Birla Group, SAIL, REC, PFC, PGC, IFCI, IRFC, SIDBI, HDFC, HUDCO, Tata Group companies, Sundaram Finance, Yes Bank, SBI Group, BoI, Canara Bank, Syndicate Bank and Dhanlaxmi Bank etc. While Edelweiss has achieved leadership posit ion in wholesale debt segment, it has also made inroads into the retail debt placement in FY13 wherein it was the Lead Arranger for public issues of NCDs for Sriram City Union, IIFL and M uthoot.

8

CAPITAL M ARKETS & ASSET M ANAGEM ENT Investment Banking Equity Capital M arkets & Advisory Services The vert icals within Investment Banking include Equity Capital Markets encompassing IPOs/ FPOs, QIPs, Rights and Open Offers, and Advisory services which offer M ergers & Acquisit ions Advisory, Private Equity Syndicat ion, Structured Finance Advisory and Infrastructure Advisory.

Edelweiss enjoys st rong franchise with emerging and mid-market companies which is reflected in its ranking and awards it has received over the years. Its client segments now range from private to public sector and from M id-caps to Large-caps across different industries.

Broking Services Inst itut ional Equit ies Edelweisss Inst itut ional equit ies business in India has a market share of 4 to 4.5%, among the highest in Indian brokerage firms. It has around 400 act ive inst itut ional investors, including domest ic inst itut ional investors and FIIs across different geographies. Research coverage present ly extends to 181 companies across 20 sectors account ing for around 75% of total market capitalizat ion.

HNI Broking Edelweiss offers dedicated equit ies and commodit ies broking services to high net-worth individuals. Product offerings include specialized trading execut ion for act ive trading clients and structured products backed by high quality execut ion and report ing.

Retail Broking & Distribut ion The organic Retail broking business is through the online portal www.edelweiss.in and provides advisory and research based broking services. It current ly has over 136,000 clients under the online broking. Edelweiss has also completed the acquisit ion of Anagram Capital Limited in July 10, now renamed as Edelweiss Financial Advisors Ltd. The offline broking model has around 261,000 clients. It also has presence through over 4,300 Sub-brokers and Authorized Persons in 625 cit ies.

Global Wealth M anagement The broad range of of ferings includes a truly mult i-asset class allocat ion advisory to Structured Products, Port folio M anagement, M utual Funds, Insurance, Derivat ives Strategies, Direct Equity, Private Equity, Commodit ies and Real Estate Funds etc. Recent launch includes Financial Planning advisory services.

Asset M anagement The Asset Management business includes Domestic Asset Management (AMC) and Alternat ive Asset M anagement business. Edelweiss Asset M anagement Company has launched a mix of 10 equity and debt funds. It has an act ive base of over 8,000 clients and has the distribut ion network of over 3,000 distributors. Alternative Asset M anagement focuses largely on offshore inst itut ional investors offering advisory/ management expert ise for Special Opportunit ies Fund, EW SBIH Crossover Fund in joint sponsorship with SBI Holdings of Japan, a Special Assets Fund, an Asset Reconstruct ion Fund and a Real Estate Fund.

9

COM M ODITIES Started about 4 years ago it focuses on sourcing and distribut ion of precious metals and dealing in agri-commodit ies. Commodit ies business distributes precious metals at 12 centres to over 400 act ive customers. Online portal (www.edelbullion.com) allows clients to book supplies in smaller lots giving them convenience of lower denominat ions.

LIFE INSURANCE Edelweiss Tokio life Insurance is a joint venture with Tokio Marine, one of the fastest growing life Insurance companies in Japan. Edelweiss Tokio life insurance has set up operat ions in India with a start-up capital of Rs 550 crores. The business commenced operat ions in July 11. Its product offering includes educat ion funding, wealth accumulat ion & enhancement, living with impaired health, income replacement and ret irement funding. It also offers group products for credit protect ion and life protect ion.

10

About Edelweiss Tokio Li fe Insurance

Edelweiss Tokio Life Insurance Limited is a 74:26 joint venture between the Edelweiss Group and Tokio M arine, one of the worlds leading insurance groups, headquartered in Japan. The company began w ith init ial capital of Rs 5.5 billion. Both the partners have decided to infuse Rs 11 billion in the business before the break-even (expected by 2017).

Incorporation Date: October 2011 Headquarter: Mumbai Website: www.edelweisstokio.in Industry: Life Insurance New Business Premium: Rs 50 crores/ ~25000 policies Business In-force: Rs 60 crores/ ~32000 policies Employees: ~1200 M arket Presence: 45 branches across cit ies in India (expected to open 26 new branches in current fiscal year) Distribution Channels: 10 different distribut ion channels Competition: Indiaf irst Life, Star Union Dai-ichi Life, DLF Pramerica LIfe

ABOUT TOKIO M ARINE HOLDINGS

Tokio M arine Holdings Inc, the holding company for Tokio M arine group is one of the oldest and biggest Insurance companies in Japan; with interests in Life, Non-Life, and Re-Insurance, it has a presence in 427 cit ies across 39 countries around the world.

The company has over 130 years of operat ional history in the Non-Life sector while it has been in Life Insurance since 1996. It has a presence in Japan, China, Singapore, M alaysia and Thailand. The Group has booked revenues of Rs 1,77,650 crore and Net Income of Rs 3,884 crore. In Life Insurance it s Gross Writ ten Premium (GWP) is over Rs 31,834 crore.

Financials

Particulars FY 12* FY 13

New Business Premium (Rs cr) 11 52 Investment income (Rs cr) 42 46 Total Income (Rs cr) 53 98 New Business Policies 6600 ~23000

Branches 29 45

PFAs 825 3401

Employees 659 1,275

Products 7 20

* Operat ions started from Oct 2011

11

Protect ion Income ReplacementRaksha Kavach

ETL Educat ionCashFlow Protect ion

Wealth Enhancement

Ace

Dhan Nivesh Bima Yojana

Single Pay Endowment Assurance

Products

Management Team

Cashf low Protect ion

Safe n Sure Plan (WA)

Save n Grow Plan (WA)

M ult iGain Plan (WA)

Accelerated Cover Cover Plus

Comprehensive Cover Privelge

Income Replacement

Retirement

Wealth Accumulation

Wealth Enhancement

Deepak M it tal CEO

Jun Hemmi Executive Director

M uralidharan R COO

Yash Prasad Chief Agency

Officer

Sarju Simaria CFO

Abhay Tewari Appointed

Actuary

Papiya Banerjee Chief Human

Resources

Yoshiaki Okabe Head Direct

Sales

Education

12

Li terature Review

A defini t ion of Analyt ics?

The problem with def ining any buzz word, including analyt ics is that over use and bandwagon jumping reduces the specificity of the word. This problem is not new and any at tempt to create a detailed definit ion seems to be doomed, no matter how careful one might be, because there will always be someone with a dif ferent perspect ive or a personal or commercial motivat ion to emphasise a part icular aspect or nuance.

Definit ion of analyt ics, although it might better be called a descript ion, emphasises analyt ics as something people do:

Analyt ics is the process of developing act ionable insights through problem definit ion and the applicat ion of stat ist ical models and analysis against exist ing and/ or simulated future data

" It is a combinat ion of business knowledge, computer science and stat ist ics," says Pankaj Rai, Director of Dell's Global Analyt ics division

We can also define it as aholist ic approach that t ransforms information into insights and insights into business outcomes.

13

Role of BI Analyt ics in insurance BI can play a crucial role in almost every aspect of the Insurance business. It can help ident ify the ways and means of improving sales force performance and operat ional eff iciency. BI also provides with cr it ical information on claims management leading to better underwrit ing pract ices and product pricing. Few of the analyt ics arising out of Insurance business are presented below.

Sales and Channel Management

Although Tied Agency has been the main stay for dist ribut ion of life insurance products, it has been observed that the emergence of newer channels like Bancassurance, Broker and Direct sales team is leading to deeper penetrat ion of the market in the last few years. New age channels like Telemarket ing, Online / Internet market ing are being used. It has been observed that customers intend to do more research using the internet, even if they ult imately rely on face to face interact ion with the intermediaries for purchase. BI tools can help insurers in deeper understanding of these distr ibut ion channels and help in devising suitable channel management st rategies.

Channel Strategy Optimization BI tools can help in analyzing the performance of different channels across mult iple geographies, product lines and also provide dr ill down facilit y to the level of a producer. An appropriate channel strategy in terms of business expectat ion, product mapping and payout structure can be structured based on the BI analyt ics.

Sales Reporting A comparison of business achieved as against the target, geography wise, channel wise, and product wise new business can help in ident ifying the trends across channels and can help in init iat ing mid-course correct ive measures to achieve the planned profitabilit y.

Channel M anagement An analysis of NB across product / geography/ channel/ term/ mode using BI tools can help in understanding the sales behaviour of agents, sales person and channel partners leading to designing of rewards program, relat ionship maintenance matrix and key partner retent ion strategy.

Channel Analysis Using BI tools, insurers can compare the performance of various distribut ion channels and can drill down to the level of individual agents and products. The performance should be tracked closely and compared over a period of t ime so as to assess the effect iveness of developmental act ions init iated.

Channel Profitability Assessment of channel profitability can be done through a comprehensive analysis of new business, commission, persistency and claims by each of the channel and this can lead to redesigning the compensat ion st ructure, revisit ing of products, revising of underwrit ing processes.

14

Operat ions Management

Premium received by the insurance companies is the source of revenue and it is important that all the deposits received are converted into premium income w ith least leakage. It is important to convert the proposal into a policy as early as possible as income accrues to the life insurance companies only on conversion of the proposal into policy.

New Business processing A Cause analysis can help in understanding the delay in processing across channels, geographies and product lines and help in quick follow up and auct ioning leading to least proposal withdrawals.

New Business leakages Leakages in NB are a major concern. It is about a customer who has signed up and paid the proposal deposit but subsequent ly not complet ing the conversion processes. It is not only a loss of NB but also represent non recoverable cost and a dissat isfied customer. Insurance companies would like to minimize the NB leakages as much as possible. An analysis of the leakages in New Business like Cheque Bounces, Proposal Withdrawals, NTU across geography, channel help in early ident ifying of wrong pract ices, misplaced commitments.

Customer Retention/ Persistency Persistency levels are lower in India compared to other count ries. IRDA, Indian Regulatory authority is also seized off the magnitude of the lapsat ion and has brought in regulat ion governing minimum persistency level to be maintained by both individual agents and corporate agents as IRDA is of the considered view that the insurance intermediaries have an effect ive role to play in minimizing lapsat ion A close monitoring of the trends in lapsat ion in terms of products, Premium mode channels and geographies can help in devising suitable policy revival strategies. M oreover very close monitoring High Value premium lapsat ions can help in connect with customers direct ly and also pave way up selling and cross selling. A lapsat ion cause analysis can help in improving customer retent ion by helping insurer in addressing the underlying causes of lapsat ion.

Claims Management

While it is important to set t le a legit imate claim at the earliest possible t ime, it is equally important to have good select ion and filtering mechanism to avoid fraudulent claims. It has been observed during recent studies that a high level of customer dissat isfact ion arises on account of delay in claim sett lement and under payment. Claims analysis is one of the most common BI applicat ions in the insurance industry. It involves analysis of the claims data coupled with other data sources like underwrit ing and policies. It is primarily used to gauge claims processing eff iciency, which has a direct bearing on customer sat isfact ion

Claims Payment M anagement A good tracking and report ing system on registered claims and their progress in terms of set t lement will help in avoiding unnecessary delays in claim payment leading to higher customer sat isfact ion.

15

Claims analysis A good BI tool will help in understanding the trends in claims in terms of product, channels and geographies and provide insight into to product profitability and pricing. Analyzing trends in claims and loss patterns across channels, products, geographies can help in spott ing fraud and also to opt imize reserve requirement. In respect of health insurance, any abuse by any medical pract it ioners can be easily ident ified through analysis of patterns of payouts.

Market ing Management

Customer acquisit ion, engagement and retent ion strategies are very crit ical to insurance business. Customer behaviour is changing rapidly. Technology, and in part icular the growth of online and social media, is driving a fundamental shift in customer expectat ions in terms of how products are marketed, sold and serviced, and how companies are perceived. Pure internet businesses like online sale of term policies in India have set new standards for customer-centricity. In todays competit ive and knowledge driven environment, it is a challenge to really understand the key drivers of customer behaviour across geographies and distribut ion channels

Customer Segmentation Segmentat ion is used to segregate customers acquired through different channels but who exhibit common at tributes. For example an analysis of product wise customers demographics can throw light on the customer segment of the product line and help in devising strategies in furtherance of the business. Similarly customer segmentat ion can be used for devising target segment specific, the most appropriate customer engagement act ivit ies like communicat ion, performance updat ion to ensure retent ion and upsale to the customers.

Product M anagement It is important to retain exist ing customers, while f inding ways and means to cross sell new products/ solut ions to them. Building detailed customer knowledge through quality analyt ics can help in ident ifying the right customer group for up selling.

Campaign analysis Insurance companies periodically launch campaign and contest to accelerate sales or for improving persistency. A good BI tool can help in analyzing the effects of part icular campaign and help in understanding cannibalizat ion of products, incremental business against incremental cost and the learning can be used for launch of similar schemes in future.

Profi tabi l i ty Management

Profitability of insurance business depends on the actualizat ion of the assumptions relat ing to expenses, income and mortality. A very close constant monitoring of each of these factors is an absolute essent iality

Premium Analysis Premium income both new business and renewal is the primary source of revenue for an insurance company. Premium analysis allows the tracking of premium performance by a product or product

16

line, by geography, by distribut ion channel. This will assist in assessing the product ivity, prof itability of each product, geography and distribut ion channel

Financial Analysis A periodical analysis of key rat ios like Retent ion Rat io, Conservat ion rat io and expenses rat io can throw light on the profitability of the business and helps in avoiding cost and underwrit ing over runs and these key rat ios can be presented in a dashboard form.

Product Profitability Analysis An analysis of the profitability of each product can be tracked across geography, channel and customer segment and this analysis will include claims, lapsat ions etc. The result of such comprehensive analysis can help in product redesigning and in ident ifying profitable customer segment.

Underwriting Loss Analysis In respect of few products, like mediclaim in health insurance business, the premium revenue might be less than the claim payouts and is termed as underwrit ing loss. This may be due to inaccurat e init ial risk est imate. Insurers need to constant ly monitor the loss data to determine the cost of gett ing new customers and renewing old ones for those products or product lines. This can help in improving profitability through revisit ing the underwrit ing programs and help insurers salvage their book of business.

CXO Repor t ing

The M IS depart ment is typically responsible for providing reports to the t op management and help in filing the statutory reports to the concerned authorit ies. A business intelligence environment that leverages data collected across the value chain is possibly the only effect ive solut ion for M IS

Dashboard / CXO Reporting Key Performance measures like Channel product ivi ty, Claim Processing TAT, Conversion rate, Product category, Mode of premium analysis can be presented in dash board reports to the top management to facilitate decision-making process. Also alerts can be triggered if any performance measure reaches a pre-defined thresh old level.

Statutory Reporting Insurers have to submit several periodical reports to the regulator, insurance council and other government agencies. These reports can be easily generated from the business intelligence environment. Timely report ing with accuracy of data is the key in regulatory report ing and it is also important to maintain consistency across mult iple reports filed with the regulator. BI tool will provide w ith t imely report along with the appropriate data backup for any future reference and validat ion.

17

Risk Management

Risk is more an integral part of business for life insurance indust ry than it is in perhaps any other industry. Strategic success is possible only when the companies understand which risks are being assumed and can appropriately price them in the product for those part icular risks, wherever possible. In a business like life insurance, where the value creat ion to the mult iple stake holders depends on the actualizat ion of several assumptions over a longer period of t ime, a sound risk management pract ice is a compulsive prerequisite.

Reinsurance A reinsurance company, normally shares a part of the insurers' risk in return for a share of the premium. In the eventuality of a claim, the reinsurance company w ill pay the corresponding claim amount and this protects the insurance companies from few adverse large claim payouts. Actuaries need to decide the right amount of reinsurance in order to maximize the returns for the risk acceptable to the insurance company. BI tools can help to arrive at the acceptable reinsurance level based on the historical claims data and help save on premiums ceded.

Underwriting Underwriter in an insurance company decides whether the risk undertaken by insuring a client is acceptable or not and also determines the appropriate premium to be charged for accept ing the risk. If it is acceptable, she determines the r ight amount of premium to be charged. BI can help in ascertaining early claims trend, pattern of claims to understand the robustness of the underwrit ing process. This analysis c-an also help in redraft ing the underwrit ing policy for the select products.

Conclusion

Companies are at cross roads as they feel the need to migrate to an advanced technology platform from the current mult iple proprietary systems. With the change in customers preferences in insurance products, the way customer access information in buying insurance coupled with the regulatory compliances makes it an ideal case for a quality BI tool in the enhancement of the business value of the insurance companies.

18

Analyt ics Suppor t for Customer Acquisi t ion

Analyt ics can reduce the cost of customer acquisit ion by opt imizing the results of market ing campaigns. The challenge for most insurance companies, given their fixed market ing budgets, is to decide where to allocate resources to obtain the best market ing return on investment. Predict ive modelling helps address this problem. Predict ive modelling for customer acquisit ion looks at a combinat ion of psychographic, text, web-log, or survey data regarding prospects. When the data is fed to the analyt ics engine, predict ive modelling can uncover hot spots for prospect scoring.

The prospect scoring model shown in below takes into account both the propensity to convert each prospect and their future potent ial. These two factors help an insurer create specific market segments and build appropriate strategies and act ivit ies for each segment. Each lead can be given due importance according to the segment in which they reside.

Prospect scoring models can be very successful in improving the eff iciency of customer acquisit ion act ivit ies, but scoring models cannot be stat icthey must be updated frequent ly to reflect the changing market condit ions and to verify whether an insurer is get t ing the correct response. During each update the insurer should add, remove, or modify the models parameters for the most effect ive results.

19

Analyt ics Suppor t for Customer Retent ion

The Impact of Policy Lapse on Revenue and Profit Policy lapse is a concern for most insurers since it often occurs within the first policy year and prevents insurers from recovering the init ial expenses of policy acquisit ion. The sooner a policyholder leaves an insurer, the less likely the insurer has recouped the acquisit ion costs and the policy is contribut ing to the companys bot tom line. That is why insurers focus on reducing lapse rates, part icularly for the most favorable customer profiles. .

M ethods for Reducing Policy Lapses M ult i touch Point Program A mult i-touch point program with appropriate message content and frequency brings down the chances of lapse during the first and corresponding policy

Cross-selling Another way to reduce lapse is to deepen the relat ionship with exist ing customers by selling them new products. Cross-selling expands the relat ionship and helps reduce attrit ion. Analyt ics play an important role in cross-selling campaigns by: Determining the next-best products for exist ing customers based on the typical buying patterns

of customers with similar demographic characterist ics Uncovering customer segments those are most likely to respond within the exist ing customer base

In the long run, an effect ive combinat ion of cross-selling and up-selling can help offset the negat ive effects of lapse and increase the value of the relat ionship.

20

Cross-selling for exist ing customers Within a part icular product port folio, there are a number of policies that go into lapse status. It does not make sense for an insurer to try to act ivate each lapse case. The driving factors which prevent an insurer from doing so are: Cost: Sending reminder let ters or calling every customer will result in significant costs. Effort Optimization: Within a product port folio, an insurer has different t ypes of customer

profiles. For the insurance company, some customer prof iles are desirable, some standard, and some loss-making. To increase profits, insurers will focus on specific policies to be act ivated and not take an umbrella approach

Using Analyt ics to Pr ior i t i ze and Focus Effor ts

Analyt ics can be used as an effect ive tool to priorit ize and focus efforts in two ways.

Customer lifetime value A framework can be created to determine customer lifet ime value based on demographics as well as transact ional details. For a new customer, customer lifet ime value is normally determined using only demographic details. As the customer relat ionship grows, the insurer gets more information about the customers transact ional behavior and can also leverage this new data source for determining customer lifet ime value. The general rule is to put more weight on transact ional details than demographic details when the relat ionship crosses the one year mark.

This analyt ics model can help insurance firms classify their exist ing clients into Plat inum, Gold, and Silver categories.

21

Risk of lapse Similarly, analyt ics can help build models to predict the risk of lapse. Risk of lapse is dependent on the servicing channels as well as t ransact ional behaviour of the policyholder.

Once risk of lapse has been determined, customers can be classified into Low, M edium, and High risk categories.

Comprehensive Customer Retention Strategy Once an insurance company has developed these two metr ics, it can develop a comprehensive customer retent ion strategy to determine where to apply the focus for lapse reduct ion.

22

Rational behind the project Edelweiss Tokio life insurance company is just a 2 year old company. Hence the processes our not much automated. In contact centre also there was no standardized report ing structure. The reports which were generated were on demand by the top management . They didnt have a definite t ime period between any two reports. Also every t ime they used to take days to generate this report .

Director had also noted that there was a need of more in depth analysis of the performance report ing. In depth analysis would lead to ident if icat ion of areas of improvement.

Taking the above two point of view into considerat ion, director asked me to make a dashboard which would be totally automated and would self generate report every day. He emphasized on the fact that the dashboard should be proact ive rather than react ive. It should give enough information that would help us to ident ify key areas of improvement in contact centre operat ions.

Timeline

23

Flow of the Project

Understanding the operat ions of the Contact Centre

Whenever we need to access the performance of any department , or make some operat ional improvement in a department, the first step has to be understanding of the work that they perform. What and how they do it , who are the people responsible, etc. It is very essent ial to understand the work they perform at a micro level so that we can give them some useful insights.

Thus, as soon as the project was assigned to me, I sat for few days with the contact center team to understand their work and the environment . I was carefully observing the way the service experts (SEs) were handing each call and observing the challenges they were facing.

I had a session with departmental head and CPO-officer to understand their work. I needed to understand what their decisions making areas are? Which are the areas which they would like to track to measure their SEs and departments performance? What are the dif ferent t ypes of calls which are happening in the contact centre?

As told by them, they are a team of 9 people (5 SEs-Outbound/ Inbound calling, 1-email, 1-helpdesk, 1-CPO manager, 1-departmental head). In the contact centre they have Status update calls , Confirmation Calls , Inbound Calls , Emails and customer centricit y calls which is a part of outbound calls only.

Status Update calls most ly happens on Policy login date + 10 days. In this call usually SEs introduce the company to the customer and states the status of his applicat ion. They give the customers the expected date of when the policy will be issued. Also, if some addit ional documents are required like medicals or address proof,it is not ified to the customers in this call.

Confirmation call is made at policy issue date + 10 days. In this call the SEs welcome the customers. They explain the product features to them. They usually tell the customers the basic benefits of what they are gett ing, the premium they need to pay and at what t ime, the sum assured etc. They also verify the personal details of the customers.

Inbound calls and emails are received as the customers may have some complaints or request. The prospects may want to understand the product features, or brokers/ employees may have some query with respect to the sale they are transact ing.

The contact centre has a toll free number and few paid numbers. There is a separate line for the helpdesk which is solely meant to address the queries of the internal employees. All the calls,

24

whether inbound or outbound are recorded for t raining purposes. The t iming for the SEs are 8 am to 8 pm from Monday to Saturday. After 8 pm all the inbound calls are transferred to voicemail.

Understanding the data

After understanding the operat ions of the department , you certainly get a clear idea regarding what to include in the dashboard that would be of help to the departmental head and the associate director. However, all the data required for the measures that you may want to calculate may not be available. Or, it may happen that the format in which the data is entered in the database may not be appropriate for retrieving it .

Thus, the next few days were invested in understanding the st ructure of the database and the information it contains.

Ident i fying KPIs & metr ics

This is the most important and crit ical aspect in the creat ion of the dashboard. In the companies due to the introduct ion of IT, data is available in abundance. So ident ifying key metrics to be used in dashboards is of at most importance. Both, including unnecessary metrics or neglect ing few important ones, can kill the usefulness of dashboard.

Based on rigorous secondary research and on the data available with the company, follow ing metrics were ident ified to be used in dashboard (only for outbound calls). They are as follows:

Service M easures (ident ifies work load, speed and adherence to company policy) Policy Logged-In/ Issued wrt. t ime Policy assigned, first call made, Calls closed, Calls pending wrt . Time Avg t ime taken to assign, Avg t ime taken to close a call No of calls made within TAT Convert ibility rat io Efficiency M easures No of calls made by Service expert(SE)/ Calls per hour by SE Avg handling t ime Call made/ assigned calls rat io by SE Calls > x mins

Quality M easures Telephone Et iquette Knowledge and Competency Avg NPS & CES score by branch, region, channel and SE

25

Final izing the Dashboard

Combining my mentors experience with my work, we came up with the final list of KPIs. It was decided that three different dashboards will be made. Each dashboard was designed keeping in mind the decision making requirements of the specific manager.

M aster Dashboard Chief Operat ing Officer Detailed Dashboards Associate Director and VP Operat ions Employee-wise Dashboard Contact Centre Head & SEs Each dashboard will cover all the aspects of the contact centre namely outbound calls, inbound calls, emails and customer centricit y measures.

26

The Dashboards

As finalized the dashboards will be divided into three levels based on the requirement of the concerned user. They are master dashboard, respect ive detailed dashboards and employee-wise dashboard. The master dashboard has a consolidated view of the metrics related to outbound calls, inbound calls, email and customer experience. Detailed dashboard tracks the performance in more depth. Outbound calls are further broken down to confirmation call and status update calls. In the employee-wise dashboard, performance and workload is being tracked of each employee.

The dashboards made are dynamic in nature. They will automatically generate daily report and report specific to any pr ior date. The user only needs to ent er the desired date and all the dashboards will be ready.

Keeping in mind the fact that the company may need to make few small changes in the future (say adding an employee), the dashboard is kept quite flexible and can accommodate such minor addit ions. Great importance has been given to its maintenance. It has been tried to make it as simple as possible so that the maintenance is quick and easy.

In the sect ions below, each of the dashboards will be explained in detail; reasons for inclusion of a KPI, its significance and what inferences can be drawn from the data.

Master Dashboard

Below is the snapshot of the master dashboard.

1 2

3 4

5

27

1. Dashboard Header - It states the name of the department for which the dashboard is.

2. Date Displays the date of the report generated. It is dynamic in nature and will automatically update itself daily. In case a report is generated for any specific date than the same date will be displayed.

3. Detailed dashboard links Instead of switching tabs, one can direct ly go to the desired detailed reports through this link. It makes accessing the desired report quick and easy.

4. Company Logo Each and every document/ report generated in any company should have the company logo on it .

5. Report Section - This sect ion is divided into four sub-sect ions consolidated outbound calls, inbound calls, emails and customer centricity measures.

Consolidated outbound calls

It gives the summary of the Confirmation calls and the Status update calls.

The master dashboard will generate monthly reports for the COO of the company. His main object ive is to have knowledge about the performance of each department. In case the performance is not up to the expectat ions then he can ask for the reasons. Hence, for him the primary object ive is to have a control on things.

In this we have three sub sect ions namely call summary, quarterly performance and monthly performance.

In the outbound calls, each SE makes several at tempts to close the call. It may not be necessary that he close the call in f irst at tempt. The reasons for this can either be that the policyholder is busy and has asked to call back later, his contact number is not reachable, or that the contact number is wrong. Hence, we have broken the calls in different categories, namely: No of at tempts Policy called Effect ive calls Not Contacted Call Backs Apart from that we have a count of the total policies issued in a part icular t ime period and out of that how many are pending.

All these seven measure give you a clear picture of the call status of the policies. We can clearly see that out of the total policies issued in a part icular t ime period how many of them have been called and how many are st il l pending. Out of the total policy called how many have been closed (i.e. effect ive calls). This is the figure in which any person will be primarily interested.

All these numbers are absolute in nature. It is always advisable to measure the relat ive performance. Performances should always be tracked in relat ion to some base. As effect ive call is the result that the management would be interested in, we used the rat io of ef fect ive calls to total policy called. This rat io is known as contact ibility rat io. Current ly we have a contact ibility rat io of around 31% that

28

is quite low. This means that even if the team is putt ing in efforts the results aren t up to the expectat ions. Hence, for the COO it would be a cause of concern. He can ask the managers to dig deeper to f ind the cause of such low contact ibility.

Next measure is the valid contact percentage . This measure shows how many of the policyholders have a valid contact number. Since an invalid contact no can have a direct impact on the contact ibility rat io, the management would like to keep this percentage as high as possible. The YTD figure for this measure is around 55%, which is very low. Hence, certain st ructural changes can be made in process of flow of information. Certain procedures should be laid down so that unnecessary efforts are not put down by the SEs to call invalid numbers.

We also have certain product ivit y measures/ rat ios like total talk time and product ivit y per employee. Talk t ime in hours will give an idea of the total product ive hours of the team. The team will also be engaged in different non-calling work like putt ing the information in excel, but we do not have the system and procedure to t rack that down. Hence, we have just included talk t ime in hours to est imate their product ive hours.

At t imes due to the increase in workload, team size might increase but it should not lead to decrease in the product ivity per employee. Hence, COO would be more interested in this figure to go higher.

Now, the next sub-sect ion, which is quarterly performance and monthly performance, gives you the number of at tempts which have been made in the said t ime period and how much of the target was achieved in terms of number of working hours. Both the tables will have the current quarter/ month first , ending with the last quarter/ month. This table will automat ically update itself in case new month or quarter comes. It gives you a picture of how we have performed over a longer t ime period.

Inbound calls

Similar to outbound calls, inbound also has three sub-sect ions: Call summary, quarterly performance and monthly performance.

Under call summary, we have the total inbound calls that are coming in. We have broken it down into three categories depending upon the caller. They are customers, prospects and others. Others may include any broker or any other channel partner. Such categorizat ion helps us to recognize from where are the maximum calls coming from and how can we address them. The COO would be concerned if inbound calls from customers and others are increasing. He would be less concerned for prospects number going high, as it would reflect that the market has become more recept ive to your products and they are inquisit ive to know more about your products.

We have also categorized the inbound calls into pending and closed. This is the area, which is of most interest to COO who would always want the pending queries to be as less as possible. Delays in solving the queries can lead to dissat isfact ion among customers that can be of great concern in such a compet it ive industry.

Total talk t ime in hours have also been added in this sect ion. Along with this we have added a measure Closures within TAT % . Customer would not only want his queries to be addressed, but

29

addressed at the earliest . Providing the customer with the solut ion ahead of his expectat ions can lead to a state of Delight . Few praises by him of our services to our prospects can make them into customers.

Quarterly performance and monthly performance chart is almost similar to outbound calls. The only difference here is that we do not have a target because one cannot have any control over the inbound calls.

Emails

The structure for emails is exact ly similar to the inbound calls. All the measures mean the same and their significance is the same as for inbound calls.

Customer Centricity

In customer centricit y we have two measures: Net Promoter Score (NPS) Customer Excellence Score (CES)

In both the measures, the customer is asked to rate the company on a set of parameters. This rat ing is not asked from all customers but from a randomly selected sample. Details on each of the score have been explained in a separate sect ion.

This sect ion will give the score of both the categories of a specif ic t ime period. Sample size as well as increase in the score is in comparison to the base month.

30

Detai led Dashboard

The detailed dashboards would primarily be used by the associat e director of the company who is responsible for the contact centre. There are a total of six detailed dashboards: confirmation calling, status update calling, NPS calling, CES calling, inbound calls and emails.

Like the master dashboard, this also has the dashboard header, date, company logo. Each dashboard also has a link to the master dashboard and a link to an employee-wise dashboard (in case required). This makes easier for the user to toggle between different dashboards.

Apart from the information present in the master dashboard these dashboards will cover more in-depth analysis.

Confirmation calling and Status Update Calling

As we see from the snapshot of this dashboard, we have added few things in the call summary sect ion. First ly we have added the M oM , QoQ, and YoY comparison of the figures. When comparing with the previous t ime period it has been kept in mind that length of the t ime period is same. Hence if the report is generated on 20t h of a month and we comparing M oM f igure, then only the f irst 20 days of the last month would be compared w ith current period performance.

We also can see a measure Closure within TAT % ; this gives us the percentage of calls which have been closed within the TAT. Current ly TAT has been kept at 10 days from the date of issue of the policy in case of confirmation calling, and for status update calling it is 10 days from the date of policy logged-in. As we all know over a period of t ime with the increase in efficiency of the team, the

31

company might need to revise the TAT and bring it down. Hence, keeping in mind all such key variable, values have been recorded in a separate tab called M aster . So, in case company changes its policy, they just need to update this figure in master tab. All the dashboards will automatically incorporate this change.

Next is the sect ion where we have listed top 3 channels and branches which are have maximum effect ive calls and maximum not contact ed policies. If a part icular channel or branch is not performing well over a period of t ime then we can dig deeper and find the cause. For example, if we constant ly see that web-sales channel is always among the highest in the not contacted list , then it can be because of the reason that no company employee is in direct contact with the customer during sales. Hence accuracy of the data can be an issue. We have to find out ways to control it . Will using IT can help? Can we verify the validit y of the contact number during the online filling of the form itself? Similarly, for effect ive calls, if a part icular branch is performing well, we can study the pract ices of that branch and incorporate in other branches.

Pending call break up gives you the total pending calls t ill date. It has been categorized in to within TAT and outside TAT. Further the outside TAT has been categorized into 100%-200% TAT, 200%-300% and >300% TAT. This gives a clear picture of the pending calls to the associate director. In case he sees the figure in the last two categories quite high, then for him it is an area of concern. He would ask them to focus on such policies and clear this back-log keeping balance with the current new policies.

Next in sect ion is the attempts break-up. It gives you attempts w ise break-up wrt . effect ive and not effect ive calls. It gives you the f igure as percentage of total at t empts. As we see in the table, almost 67% of the total at tempts were not ef fect ive and had only one attempt. In fact, as the calls were not closed they should have made more attempts to close the calls, which did not happen.

At the extreme right end corner, we have added the link to go back t o the master tab and a link to go to the employee-wise tab.

NPS calling and CES Calling

32

NPS and CES calling both are related to measure the customer sat isfact ion index and hence their loyalty towards the firm. In this sect ion we have four dif ferent sub-sect ions.

Call Summary Quarterly and monthly performance graph Top posit ive and negat ive responses Top channel and branches receiving high scores In call summary we have the number of responses that have been taken. As these scores are collected randomly from customers and not all the customers usually give the score hence we need to know the sample size. This sample size is the no of responses received. Further, we have broken it down into calls, emails and SM S.

Next is the avg NPS/ CES score of a part icular period. It is the based on the NPS rat ing given by each customer. We have also made the M oM, QoQ, and YoY comparison of the f igures in the similar way as in outbound calls.

Quarterly and monthly performance shows the no of response and the NPS/ CES score over different t ime periods. The recent quarter or month is shown f irst .

In the next sub-sect ion we have listed the top 3 posit ive and negat ive responses. These responses give an idea of what are the areas which the customers like and what areas we need to work on. Against each response we have NPS/ CES score, sample size/ responses, promoters count and detractors count. If a part icular response is gett ing negat ive rat ings repeatedly then the managers can go deeper into finding out the reason for the same and correct them. After one response is addressed then we can shif t to the next one.

Similarly we have tables for top 5 branches and channels having t he highest scores. All the four tables can be viewed in monthly, quarterly and yearly t ime period.

In this sect ion at the extreme right end corner, we have also added the link to go back to the master tab and a link to go to the employee-wise tab.

33

Inbound Calling and Emails

All the queries, complaints and request from the customer come to company either in the form of emails or calls. In this sect ion we have 5 sub-sect ions

Call Summary Quarterly and monthly performance graph Top queries Pending queries break up Top channel and branches receiving high scores In call summary we have number of queries/ emails which are broken down into closed ones and pending issues. The main reason behind this is to keep a track on the count of queries which are coming in, and see how many of them are open and closed. Further we have broken the incoming queries/ emails by senders. We need to track from where the queries are majorly coming in. If it is the prospects who are generally asking for product information or premium rates then it is not much of an issue, as it shows an increase in interest among customers regarding our product. But if queries/ email from customers are increasing gradually then we need to curb them.

34

We can do this by finding out the issues/ queries which are most ly cropping up and give this information to the customers beforehand. We should try and find the alternat ive cost effect ive ways to provide this information to them. So to help the mangers to ident ify the major issues we have provided them with the table having top 5 queries. This can be seen monthly, quarterly and in yearly trend.

Also the next quest ion which arises is whether there is any part icular product or branch from where the queries are coming in. For eg, if we have lot of queries coming in from product - cash flow protect ion then we should find the reason for it . Is the product too complicated for customers to understand or is the complete information is not available with the brokers/ PFAs regarding the product. In such cases we can ask either the product development team to simplify the product or to describe it in a better way. We can ask the HR team to t rain the PFAs and brokers and equip them with bet ter understanding of each product. Similarly, is there any part icular branch whose customers are generat ing more queries. So such data gives you a trigger point for further in depth analysis which may be useful for the company in improving their efficiency and its customer as it will lead to increase in customer sat isfact ion.

Pending call break-up gives you the total pending calls t ill date. It has been categorized in to within TAT and outside TAT. Further the outside TAT has been categorized into 100%-200% TAT, 200%-300% and >300% TAT. To the associate director this gives a clear picture of the pending calls. In case he sees the figure in the last two categories quite high, then for him it is an area of concern. He would ask them to focus on such policies and clear this back log keeping balance with the current new policies.

Last ly, quarterly and monthly graph is there which gives you a t rend of the queries received by the company.

At the extreme right end corner, we have added the link to go back to the master tab.

35

Employee-w ise Dashboard

The employee-wise dashboards would primarily be used by the CPO-officer and service experts of the contact centre. There are a total of four detailed dashboards: confirmation calling, status update calling, NPS calling, CES calling.

Like the previous dashboards, this also has the dashboard header, date and company logo. Each dashboard also has a link to the master dashboard.

Confirmation calling and Status Update Calling

As we can see from the snapshot, we have three sub-sect ion in this dashboard: Employee-wise summary, employee detailed report , and pending calls by priority.

36

In the first sect ion, the CPO-officer and the SEs can track their performances. They can see the number of calls made and the target achieved. This helps the CPO officer to assess and compare each SEs performance over a period of t ime.

Next sect ion is the employee detailed report . This is specifically added to assist SEs to keep a track on their work load. Each SEs can choose his name, and its detailed FTD/ WTD/ and M TD report will be visible. They can track the call backs, t rack what are the individual talk t ime and other metrics. In this detailed report sect ion, each employees calls are broken down into:

Calls made Policy called Effect ive calls Call Backs Calls within TAT Avg talk t ime Avg at tempts per policy Avg days between attempts It also helps CPO manager to keep an in-depth check on each SEs performance. In this dashboard, the last 6 months call volume for each SE has also being provided. This gives us a trend of his work effort .

In the last sect ion, we have provided each employees pending calls volume broken down by priority. This helps the CPO off icer to track their work load. Hence, whenever he would assign new calls to SEs, he would keep in mind their workload. This also helps SEs to priorit ize their calls. Current ly, they dont keep a track on call backs and hence usually in case of call backs, they dont call the customer again they concentrate on new assigned calls. Based on this chart , they would realise that how many calls are pending and which policies to call first .

Low priority is given to calls which are within TAT, medium to those which are outside TAT but within 20 days from date of issue/ log-in and calls more that 20 days are given high priorit y. The criteria for set t ing these priorit ies have been kept f lexible. One can anyt ime change the no of days by going to master tab. Also for both the calls (confirmation and status update) priorit ies have been defined separately.

NPS calling and CES Calling

37

In this sect ion we have recorded employee-wise calls that they have made and out them how many NPS/ CES responses are they able to get. Alongside, we have also measured the NPS/ CES score by each employee. First ly, this helps to know whether the employee is able to convince customers to give NPS and CES score or not. If the responses are too low when compared to calls made by him/ her, we provide them with training. Also if a part icular SE is always gett ing very high NPS/ CES score then it can be possible that she is only asking those customers who were happy with our services. In this case are sample with not be a true representat ive and hence the average NPS score may be skewed.

We have also provided the NPS graph by different t ime period showing the promoters, fence sit ters and det ractors breakup.

38

Customer Centr ici ty Measure

To measure the customer sat isfact ion levels, the company uses NPS and CES scores.

Net Promoter Score1

The Net Promoter Score (NPS) is a simple but powerful tool to measure client sat isfact ion with one single quest ion, an indicat ion of the growth potent ial of your company or product.

The Net Promoter Score is a cust omer loyalt y met r ic developed in 2003 by management consul tant Fred Reichheld of Bain & Company in collaborat ion w ith t he company Sat met r ix. The object ive was to det ermine a clear and easily int erpretable customer sat isfact ion score which can be compared over t ime or betw een dif ferent indust r ies.

Net Promot er Syst em is based on the fundamental perspect ive that every company's customers can be divided into t hree cat egories. " Promot ers" are loyal enthusiasts who keep buying from a company and urge their f r iends to do the same. "Passives" are sat isf ied but unenthusiast ic customers who can be easily wooed by t he compet it ion. And "det ractors" are unhappy customers t rapped in a bad relat ionship. Cust omers can be cat egorized based on their answer to the ult imat e quest ion.

The NPS assesses to what ext ent a respondent would recommend a certain company, product or service to his fr iends, relat ives or colleagues. The idea is simple: if you like using a certain product or doing business w ith a part icular company, you l ike t o share this experience w ith others.

Specif ically, the respondent is asked the follow ing quest ion:

How likely are you to recommend company/ brand/ product X to a friend/ colleague/ relat ive?

Calculat ion

Depending on the score t hat is given t o the Net Promot er quest ion, three categories of people can be dist inguished:

Promoters = respondents giving a 9 or 10 score Passives = respondents giving a 7 or 8 score Detractors = respondents giving a 0 to 6 score

1 Bain and Company - NPS

39

The Net Promot er Score is calculat ed as t he dif ference between the percentage of Promot ers and Det ractors. The NPS is not expressed a s a percentage but as an absolut e number lying betw een -100 and +100.

If you have for example 25% Promot ers, 55% Passives and 20% Det ractors, the NPS w ill be +5. A posit ive NPS (>0) is generally considered as good.

Don t make the common mistake of placing a percent sign (%) behind your NPS score, i t is not a percent .

Usage

NPS is used nowadays by many large companies as a customer feedback tool. It gives your organizat ion an unambiguous number that is easy to understand for all employees and useful as input for managers to st eer the company. According to many people the NPS also gives a good indicat ion of grow th pot ent ial and customer loyalt y for a company or product .

You can t rack the evolut ion of the NPS over t ime, or compare it w ith a predet ermined target . You can also benchmark di f ferent areas or products, or check where your company posit ions it self versus the indust ry average i f this is avai lable.

To give an indicat ion: according to Reichheld the average American company scores less than +10 on the NPS, while the highest performing organizat ions are situated betw een +50 and +80. These values may however vary considerably f rom sector to sector and from culture to culture.

To understand the mot ives of Promot ers and Det ractors it is recommended to accompany the NPS quest ion by one or more open quest ions that probe the underlying reasons behind t he given score. This allows you to make the appropriate adjust ments to increase the future NPS, either by boost ing the percent age of Promot ers, ei ther by reducing the proport ion of Passives and Det ractors (or bet t er yet , a combinat ion of both).

40

Customer Effor t Score

Customer Effor t Score was developed by US research and advisory firm Corporate Executive Board (CEB), which began its research back in 2008. Lara Ponomareff, research director at the CEBs Customer Contact Council (CCC), who undertook the five-year study alongside Anastasia M ilgramm and M at thew Dixon, explains that as products became more commodit ised, customer service emerged as the different iator.

The research showed that once a customer had received a sat isfactory experience there was very lit t le increase in loyalty by wowing them even further. The team found that the real uplift in loyalty came from having a very poor customer experience to having one that meets their expectat ions. The best way of bumping an experience up to sat isfactory, according to the research, is to remove as many of the barriers as possible from the customers path.

Typical obstacles in a customers path might be:

an overly complex IVR with many dead end choices mult iple transfers between departments having to call in mult iple t imes to resolve a problem not listening to preferences or select ions made having to switch channel from social, to email, to phone to resolve a problem

Thus CES is derived from asking a simple post-transact ion quest ion:

How much effort did you personally have to put forth to handle your request?

Scores range from 1 (very low effort) to 5 (very high ef fort)

To wrap it up, some t ips to minimize customer effor t and to maximize the customer experience. Remember, not only does minimizing the effort make for a great customer experience; it also has an impact on your bot tom line. It means less effort for you, as well !

Accuracy: provide accurat e informat ion about your products, services, policies, et c. M ake sure all messaging is accurate, and conf i rm that everything that should be in the box, is.

Thoroughness: if you provide al l of the necessary forms and informat ion needed for a customer to complete a t ransact ion or int eract ion, then no addi t ional ef fort is needed to chase down forms, etc.

Consider Expectat ions: when you're designing and def ining your policies, consider t he customer's viewpoint and what their expectat ions may be about that int eract ion; if you need to, set appropriat e expectat ions.

41

Self-Service: i f you are t rying to make an act ion/ interact ion self-service, ensure t hat it is t ruly self-service. Again, provide clear, thorough, and accurat e informat ion so that customers can complet e everything on their own.

Communication: I can' t st ress enough how key communicat ion is to reducing cust omer ef fort . Not just any communicat ion, but clear, accurat e, personalized, and t imely communicat ion.

Consistency: if you offer mult iple channels for cust omers to shop or interact w ith you, be sure the experience is consistent across channels. Share informat ion internally so that the customer doesn' t have to start f rom scratch when moving from w eb t o phone, for example.

Responsiveness: it goes w ithout saying that if you respond in a t imely manner, the customer doesn' t need to follow up w ith you or chase you down. Effort avert ed.

Partnerships: ensure that your partners process facil itate the customer experience, not hinder it .

42

Suggestion

Data validation I have found the database maintained by the contact centre team is not clean. There are, at t imes, a lot of t yping errors. Data, like categorizat ion of responses, can be more standardized. Hence, a suggest ion to the team would be to use data validat ion in the database file wherever applicable. As far as categorizat ion of responses/ feedback/ queries is concerned, more standardized categorizat ion needs to be done. This will increase accuracy of the analysis.

Talk time In the current dashboard we have used est imated t ime of a call to calculate the average talk t ime. However, I have found that each call is recorded and a file is created in the main computer. Hence each SE can easily record the talk t ime. Incorporating human resource and finance data As this dashboard is created within seven weeks, we have just incorporated the operat ional data. If in the future, we include the human resource data and finance data along with this information then we can gain even bet ter insights. Contact centre head would be able to predict the human resource requirement and eff iciency in a more precise manner. Also CXOs would be able to measure the return on investment which is being made in the contact centre. Worst and best call of the weekWhile spending t ime in the department I realised that there were various calls which were tough to handle by the SEs, as the customer may be rude and harsh. At t imes SEs are not able to handle them properly. Hence as a part of t raining, every week CPO officer can choose worst and best call of the week and can help them in handling it in a bet ter way. Weekly rating chart for employee This is a mot ivat ional technique that can be used to enhance the product ivit y of the employee. We can rate each employee on different parameters like number of calls at tended, conversion rate/ response t ime, type of calls at tended, quality of calls, at tendance and other parameters. Each parameter can have weights and last ly weighted average score can be calculated. Every week such score can be disclosed and quarterly award can be given to the best performer. This process will be transparent.

43

Limitations

M ore precise targets Current ly the team targets and individual targets for outbound calls are kept fixed. Thus in case a SE is on vacat ion or on leave or if in current month the employee count has decreased compare to previous month, the target set will be inappropriate. Thus, it has to be adjusted everyday based on the SEs availability. Responses/ Feedback/ Queries Categorization Current ly, in the database, the categorizat ion is not precise and standardized. This has resulted in hundreds of categorizat ion. Hence, after we consolidate such cat egories based on their relevance, the signif icance and accuracy of the analysis will improve

File size Everyday data being fed into this file can lead to increase in the file size making the file very slow. Hence, we should find alternat ive ways to keep the file size in check.

Learning

The most important learning for me in this internship period is I got an idea of how the top managers think. M r M ayuresh Karnik, the associate director and my mentor had a vision to improve the operat ional efficiency of the contact centre. Hence, he asked me to make a dashboard which should be proact ive enough to forecast issues which may crop up in future. The dashboard which was created gives very micro level picture. It shows how the small, very minuscule information available, can be used to make long term improvements in the operat ions of the contact centre. He always emphasised on small changes keeping in mind the bigger goal. In every organizat ion any change is not welcomed. Hence during my tenure I felt , several t imes, that my colleagues were not very recept ive to this idea. Current ly, there was no definite structure of report ing to top management . Though they used to generate monthly reports for top management, it was at a very macro level and didnt provide in-depth analysis. They perceived this idea to be a kind of a control on them. They thought that because these reports will be published daily, the management will have cont inuous watch over them. Therefore, for me it was very difficult to extract precise information from them. I had to make them believe that it was meant to help them and to increase their teams effect iveness. Being a manager it is very important to always show your employee a posit ive picture. One should always keep them in conf idence and should make them believe that they are a very important aspect of this process of change.

I had a fair bi t of idea of how the insurance indust ry operates. However during this int ernship period I realised the importance of dist r ibut ion partners and the contact cent re in the success of an insurance company. We know customers are very cr it ical for the success of every organizat ion par t icular ly for insurance indust ry. As these dist r ibut ion partners and contact cent re are in direct contact w i th the customers, their ineff iciencies

44

can have a very huge effect on t he customers sat isfact ion and in turn on companys performance Lots of data is available with the companies today but one should know how to use them. Through the creat ion of this dashboard I realized the power of data and how it can be used to bring posit ive changes in an organizat ion.

Last ly, as the ent i re dashboard is made on M s Excel , I have learned various new t echnical aspects of M s Excel. Right f rom making the dashboard automat ic to making it dynamic, I have learned various ways to present single informat ion.

45

References

Edelw eiss Financial Services Edelw eiss Tokio Life Insurance Invest or presentat ion Edelw eiss Tokio life Annual Report Benchmarkpor tal .com: KPIs for insurance indust ry Benchmarkpor tal .com: KPIs for cal l cent er indust ry NPS: Check market blog Net Promot er U.S. Consumer Benchmarks 2013: Satmet r ix NPS: Bain & Company CIO Art icle: creat ing a prof itable call center Genre Insurance Issues Newslet t er Apri l 2013 Ernst & Young 2013 US life-annuity insurance out look SAS Blogs Call cent er M et r ics: North American Quit line Consor t ium Business Analyt ics for insurance - IBM

Life insurance 2020 - PWC Transforming the Life Insurance Indust ry Li fest yle Based Analyt ics Analyt ics: A Powerful Tool for the Life Insurance Indust ry: Capgemini

46

Glossary

No of attempts Total no of calls/ at tempts made by SE in a particular t ime period

Policy Called Total no of unique policy count on which these calls are made by SE in a part icular t ime period

Effective Calls No of policies on which the SE has contact ed the policy holder and call has been closed

Not Contacted No of policies on which SE was not able to contact the customer because of wrong no or cell out of reach

Calls Backs No of policies where the policyholder has asked the SE to call back later

Policy Issued No of policy issued by the company to the policy holder in a part icular t ime period

Policy Logged-In No of policy logged-in by the company in a part icular time period

Pendings No of policies which have not been closed out of the total issues policies in a part icular t ime period

Contactibility % Percentage of calls closed to the total policy called

Valid Contacts % Percentage of policy having valid contacts. It excludes policies having wrong no or not reachable

Total talk time Total talk t ime in hours of all the SEs put together

Avg attempts per policy

Average attempts taken by SEs to close the call

Avg days b/ t attempts

Average days taken by SE between two consecut ive at tempts

Productivity per employee

Attempts made by each employee (employee includes departmental head and CPO officer)

47

Appendix

Policyholder servicing turnaround times as prescribed by IRDA for life insurance companies

Master Dashboard CONFIDENTIAL 10-07-13, 5:41 PM

Detailed Dashboards:

MTD QTD YTD Quarter # Call Month # Call Target Achieved MTD QTD YTD Quarter Month

No. of Attempts 5912 9195 9195 Q1-14 9,123 May-13 5,840 81% No. of Queries/Issues 888 2337 2337 Q1-14 May-13

Q4-13 10,569 Apr-13 3,283 46% Closed Queries 874 2318 2318 Q4-13 Apr-13

Policy Called # 5887 9117 9117 Q3-13 10,036 Mar-13 4,388 61% Pending Queries 14 19 19 Q3-13 Mar-13

Effective Calls # 1805 3004 3004 Q2-13 9,948 Feb-13 2,870 40% Q2-13 Feb-13

Not Contacted 1364 2355 2355 Q1-13 7,130 Jan-13 3,311 46% No of queries from customer 710 1808 1808 Q1-13 Jan-13

Call Backs # 2717 3756 3756 Dec-12 3,831 53% No of queries from prospects 40 187 187 Dec-12

Policy Issued/Logged In # 2427 4413 4413 Nov-12 2,844 40% No of queries from others 138 342 342 Nov-12

Pending # 2427 4183 4183 Oct-12 3,361 47% Avg queries per customer Oct-12

Sep-12 4,116 57% Sep-12

Contactibility % 31% 33% 33% Aug-12 3,748 52% Total Talk Time* Aug-12

Valid Contacts % 77% 74% 74% Jul-12 2,084 29% Closures within TAT %* Jul-12

Total Talk Time (hrs) 317 504 504 Jun-12 3,026 42% Jun-12

Productivity per employee 845 1314 1314

MTD QTD YTD Quarter Month NPS Score: 22% 31% 31%No. of Queries/Issues 229 481 481 Q1-14 May-13

Closed Queries 178 371 371 Q4-13 Apr-13

Pending Queries 51 110 110 Q3-13 Mar-13

Q2-13 Feb-13 % change from

No of queries from customer 196 407 407 Q1-13 Jan-13 Base

No of queries from prospects 25 59 59 Dec-12 March

No of queries from others 8 15 15 Nov-12 Responses: 77 140 140Avg queries per customer Oct-12

Sep-12

Closures within TAT %* 90% 92% 92% Aug-12 CES Score: 92% 92% 92%Jul-12

Jun-12

% change from

Base

March

Responses: 60 60 60

33% 936

592

848

1,317

974

698

876

874

-

Customer Centricity Measure

CES

NPSLast 12 Months Performance

-

59

107

-

-

252

229

- 237

81

349

# Queries Received

Emails

Emails Summary Quarterly Performance

# Queries Received

742

247

-

287

218

481

Consolidated Outbound Calls

Quarterly Performance Last 12 Months Performance

Target Achieved

46%

Call Summary

42%

49%

46%

Call Summary

Inbound Calls

Quarterly Performance Last 12 Months Performance

# Queries Received

888

1,449

1,544

1,293

# Queries Received

2,337

4,154

2,548

2,314

Customer Centricity

31 May 2013

Outbound Calls Inbound Calls Emails

Contact Center Dashboard

MTD QTD YTD

1 of 1

Detailed Dashboard CONFIDENTIAL 10-07-13, 5:42 PM

1

FTD MTD QTD YTD Quarter # Call MTD QTD YTD # %

No of Attempts 72 5912 8968 8968 93% p 155% p 130% p Q1-14 8,896 Total Pendings 1937 100%Q4-13 6,739 Within TAT 307 16%

Policy Called # 72 5887 8890 8890 96% p 183% p 329% p Q3-13 5,608 Outside TAT 1630 84%Effective Calls # 19 1805 2921 2921 62% p 114% p 75% p Q2-13 7,197 100%-200% TAT 387 20%Not Contacted 18 1364