Hexaware Webinar Series Presents:Know your customer better - Insights into CRM Analytics

Sundip Gorai – Vice President, Hexaware TechnologiesDec 4th, 12 pm Eastern Time

Our mission : To build value for clients through innovative use of technology and talent

BusinessAreas

Transportation

ERP/HRIT

InsuranceBFS

IndiaIndia’’s s Fastest Fastest

Growing MidGrowing Mid--Sized Sized

CompanyCompany

32 offices worldwide

18 Global locations

17 years of technology outsourcing expertise

55 Global 500 clients

166 Clients served worldwide

187 USD mil Revenues, 06

6900 Employees worldwide

A Global IT and BPO Service Provider

Strategies and Strengths

Core CompetencyManagement of business-

critical applications offshore

Organization TraitsConsultative approach, Responsive and Result-

oriented

Robust BackboneWorld-class infrastructure, Flexible delivery models, SEI CMMi Level 5, BS7799

Track Record88% Repeat Business

Offshore transition expertiseGlobal Delivery

Leading BFSI service provider with proprietary products (Operational Risk, Collections, Leasing, Wealth Management

# 1 Airlines services provider in India

8 of top 10 airlines are our clients

# 1 provider of HR-IT services in India

500+ projects, 750+ resources

Specialized Insurance service provider Content management, Fraud Mgmt, Work flow, SOX, BPO

LEADERSHIP

THROUGH

FOCUS

LEADERSHIP

THROUGH

FOCUS

ENHANCING

VALUE

ENHANCING

VALUE

Enhance Your Customer Experience Better

Hexaware CRM Analytics

Hexaware CRM Analytics Solution Offering

The analytic CRM Strategy Choice points

• Prebuilt Marts

• Stats and Mining Models

• Off the Shelf Tools

• Platform of Choice

Analytic CRM Strategy

Hexaware CRM Analytics Domain Coverage

360°Customer

View

Cross Sell Analysis

Campaign Analysis

Customer Profiling

CRM Functions

New Business Analysis

Service Request Analysis

Product Acquisition

Analysis

Behavior Analysis

• Derive Hindsight, Insight and Foresight into your CRM functions

• DW ,OLAP and Reporting –Hindsight

• Statistical and Data Mining Techniques – Foresight and Insight

• Data models

• Dimensions/Hierarchy/Metrics

• Slice/Dice/Roll-up/Roll-down

• Operational Reporting and Ad-hoc Analysis

Campaign Analysis

Campaign Analysis

Campaign Analysis provides an organization with a focal point to analyze the effectiveness of individual marketing campaign performance in terms of responsiveness of the target audience and associated conversion rates.• Campaign Evaluation

– Responses– Customers Acquired– Cost of acquiring a Customer– Value Generated

• Targeting /Contact method evaluation– Measure and manage media effectiveness for products

or regions– Perform cost-benefit analysis of your campaign

Campaign Analysis (continued)

What kind of questions the model will answer for you ?1. Which type of Campaigns generated the maximum

responses and helped in acquiring the maximum customers/no. of Accounts?

2. Which Campaign was most economical based on cost of acquiring a customer?

3. Which product family or product family combinations elicited the maximum responses across campaigns?

4. Which type of Marketing Method should be used for a particular segment of customers based on past behavior?

5. How many Campaigns have been launched for the promotion of a particular product over the last 5 years? What has been the amount invested so far?

Campaign Analysis Model

Campaign Analytics Report

Cross-sell Analysis

Cross-sell Analysis

1. Which is the most popular product family holding and What is theassociated customer profile?

2. How many customers hold product family A and B but do not have C?

3. What is the change in the product family holding over time?4. What is the cross sell opportunity for a particular holding and a

given customer profile?

Cross product holding analysis1. How are the customers distributed across a given product family

combination?2. Which combination of First and Second Product Family is most

common? What is the profile characteristic for such a combination?

3. Which are the clusters across product holding combinations?

Cross-sell Analysis Model

Cross-sell Analytics Report

Service Request Analysis

Service Request Analysis

• Analyze patterns in Service Requests– Inquiry Channel used– Customers Profile– Products for which Service Request was logged

• Analyze Service Slippages– Service Centers– Type of Requests– Type of Customers– Across Time

What kind of questions the model will answer for you ?1. What is the status of Service Requests? What is the percentage of

Delays in processing Service Requests? Is there any pattern in these slippages?

2. What is the inquiry channel commonly used?3. Which product generated the largest number of service calls?4. Which type of Customers log a large chunk of Service Requests? 5. Which types of service requests do customers frequently make? This

can throw light on the way the calls are attended to or the insufficiency in the information provided to the customers. The Organization can modify its approach either in the content or in the channel.

6. Time lag in closing the service requests –Which types have the maximum time lag?

7. Percentage of service calls meeting the standard service levels.8. Which Service requests exceed the Standard service levels? –

Comparison over different periods would provide improvement in customer service or persistency in lack of improvement.

Service Request Analysis Questions

Service Request Analysis Model

Service Request Analytics Report

360° Customer View

360° Customer View Data Model

360° Customer View – Sample Reports

Service call analysis report Customer profile analysis report

Opportunity analysis report ERM enquiry analysis report

360° Customer View – Sample Reports

Number of calls across time and services Total revenue for a profile across channel usage

Total opportunities for a business unit across countries Number of open/closed cases against channel usage

Other CRM Functions Analysis

New Business Analysis

What kind of questions the model will answer for you ?1. What is the profile of new Customers acquired? 2. Which is the product by which new customers usually establish a

relationship with?3. Which source channel is the most effective in acquiring

new customers? 4. Is there a relationship pattern between source channels and the profile

of new customers they acquire?5. How many customers has the organization not been able to retain

during the first month?6. Is the relationship value of such customers significant?7. Is there an upward trend in the relationship value of new customers?8. What percentage of new business is because of Cross Selling/Up

selling efforts?9. What percentage of Customers have been Acquired because of

Customer Referrals?

Product Acquisition Analysis

What kind of questions the model will answer for you ?1. Which is the most favored product by which customers usually

establish a relationship?2. Is there a trend between such a product acquisition history and

customer vintage?3. On an average how long does it take for a customer to acquire each of

the product in his portfolio? What is the sequence of such acquisitions?

4. How have the customers who have been acquired because of Campaign, faired over time?

5. What has been the retention rate in Year 2 of Customers who joined in Year 1?

Behavior Analysis

What kind of questions the model will answer for you ?1. Which Customers are dormant across all channels for the

past 2 months?2. What is the preferred channel for a customer based on usage?3. Who are my Profitable customers who also show high Usage –who

could be offered higher facilities?4. Who are my problem customers who log a large amount of complaints?5. Who fall in top 10 percentile of users by channel type?6. Which customers have shown an increase in Internet Usage?

Customer Profiling

What kind of questions the model will answer for you ?

•Questions based on Customer Demographics

– Age

– Income

– Profession

– Marital Status

•Questions based on Relational Characteristics – Product Holding, Vintage, Age on Book

•Questions based on Transactional Characteristics – Channel Usage and Profitability Score

– Industry

– Legal Entity

– Net Worth

– Equity Capital

Hexaware CRM Analytics –Product and Service Offering

What will you get?1. Pre-Built Data Model (E/R and Dimensional) to Jumpstart

your Analytics Implementation2. Pre-Built Catalogue of Metrics and Dimensions to aid your

analytic needs3. Pre-Built Reporting templates instantiated in the technology

of your choice for tactical reporting needs4. More than 100+ Critical Business questions covering

various sub-functions to support you in ad-hoc analysis5. Pre-Built Metadata/Pre-Built Reports 6. Onsite – Offshore execution model to reduce cost7. HR Consulting expertise

Hexaware CRM Analytics Data Model Richness

E/R Model•Approx 100+ entities•Approx 1000+ attributes

Dimensional Model•200 Dimensions•70 Measures•200 Reports

The analytic CRM Strategy Choice points

• Prebuilt Marts

• Stats and Mining Models

• Off the Shelf Tools

• Platform of Choice

Analytic CRM Strategy

Stat Analysis/Mining –Tools and Techniques

Mining Techniques

• Classification/Categorical Analysis• Clustering /Association/Neighborhood

Analysis• Decision Trees• Parameter Selection and Improvement• Forecasting• Simulations• Optimization• Others

Tools

• Statistical Analysis - SAS, SPSS, Statistica, Wincross

• Predictive Modeling - SAS, SPSS, KXEN, EVIEW, Oracle Datamining

• Decision Tree/Segmentation - Knowledge Studio, CART, SAS, SPSS

• Forecasting and Simulation - Crystal Ball, SAS, SPLUS

• Optimization - LINDO, Evolver, RISK Optimizer• Risk and Decision Analysis - @ Risk, SAS,

SPSS • Campaign Management – UNICA

Industry Solutions

• Banks, Capital Markets and Insurance • Retail • Consumer Products • Pharma and Healthcare

Data Mining Value Proposition for CRM

Data Mining Process

• Response propensity

• Purchase score

• Credit score

• Relationship value

• Other business criteria

Business flow of campaign management

Analytic CRM project design and Execution

• Assignment model

• Right product/right customer/right channel using SAS linear programming

Campaign objective Campaign description Customer selection

• Data Mining Techniques• Model Selection

Response propensity score for maximum likelihood of response

Tele marketing

• Campaign effectiveness• Evaluation model

(response and returns to cost)

SAS Datasets

SAS Datasets

SAS Datasets

SAS Datasets

Closing the Loop –Learning Feedback

SAS Datasets

SAS DatasetsSAS Datasets

SAS Datasets

SAS Datasets

SAS Datasets

Reporting AND analysis (use SAS BI/ micro strategy/MS excel)

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Owner of campaign budget ,costs andSegmentation other constraints

Customer attributes –age/gender/Relationship details

Customer exclusions criteria

Filtered Customer List

Customer selectionBased on other attributes

Select customers who did not respond to past campaign and are poor on other parameters

Split customer list

• Data Mining Techniques• Model Selection

Response to multiple campaigns stored as history

Siebel schedules

and executes campaign

11

• Cross Sell

• Upsell

• Churn

• Retention

• Segmentation

• Customer transition to higher segment

Direct mail

Walk in to advisors

Other medium

Filtered customer list based how old is relationship

n

CRM Integration Process Flow Expertise

Executing the campaign (manual)

Learning from the campaign•ROI modeling, campaign•Optimization

Reporting and analysis (SASOLAP /microstrategy/excel)

Campaign objective, budget and description

Modeling and feeding scores

Customer exclusion

List generation with optimization model

Customer selection

SourcesMassaged

Data

ETL ETL

Data Preparation

Campaign optimization

Planning the campaign

Campaign management targeting the customer/segmentation manager/

Staging data store

Transition SAS Process

Stat Analysis/Mining –Applications and Techniques

Applications Mining Techniques

• Prospect targeting• Call planning• Marketing optimization• Sales force optimization• Propensity to churn• Customer Segmentation• Performance attribution • Funds/fees analysis• Fund benchmarking• Revenue forecasting• Demand forecasting• Probability of default • Loss given default• Probability of claim• Underwriting Scoring• Fraudulent identification• ALM /FTP models (core segregation,

attrition, matched maturity)• Economic capital modeling• Clinical Intelligence• Resource utilization analysis

• Classification/Categorical Analysis– Logistic Regression, Support Vector

Machine, Naïve Bayes, Adaptive Bayes• Clustering/Association/Neighborhood Analysis

– “K” Means, “K” Nearest Neighbor, “O” Cluster, Association

• Decision Trees– CART/CHAID

• Parameter Selection and Improvement– Factor Selection/Non-Negative Matrix

Factorization/Attribute Importance• Forecasting• Optimization• Others

– Regression, ANOVA

CRM Analytic using Mining models

Customer Segmentation Targeting the “right customers”

Objective• To select the appropriate customers for

selective offer targeting Methodology• Classification using decision

trees/logistic regression• Selection of prospects based on their

response to previous campaigns Benefits• Identification of customers most likely to

respond positively to offers• Reduction in the cost of campaign due

to improved targeting• Higher response rate• Offers to new customers on the basis of

their resemblance to existing customers

Objective• Find out typical customer segments

and their characteristics (profitable, loyal, etc.)

Methodology• Clustering• Classification (if we know the

segments)Benefits• Identification of profitable segments• Insight into customer purchasing

behaviour• Offers to new customers on the basis

of their resemblance to existing customer segments

CRM Analytic using Mining models

Prediction of Credit Card Defaults Reducing Customer Attrition

Objective• To retain profitable customers by

predicting their possibility of attritionMethodology• Classification using decision

trees/logistic regression• Association Rule mining used to identify

right offers that will reduce customer attrition

Benefits• Insight into attrition patterns• Identification of profitable customers

most likely to cancel the credit card services

• Reduction in the cost of retention due to precise offers that are likely to be accepted

Objective• To predict the probability of a credit

card customer defaulting on paymentsMethodology• Logistic Regression• Potential defaulters identified on the

basis of demographic factors and purchasing patterns

Benefits• Minimization of credit card defaulters

by identifying potential candidates at the issuing stage itself

• Credit card limits can be adjusted to minimize amount of default

• Selective procedure for issuing credit cards and upgrades that mitigates default risk

The analytic CRM Strategy Choice points

• Prebuilt Marts

• Stats and Mining Models

• Off the Shelf Tools

• Platform of Choice

Analytic CRM Strategy

Consolidation and Maintenance

BI Infrastructure and Data MiningAnalytics

SAS Enterprise Intelligence Platform SAS

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Hexaware SAS Implementation Capability

The analytic CRM Strategy Choice points

• Prebuilt Marts

• Stats and Mining Models

• Off the Shelf Tools

• Platform of Choice

Analytic CRM Strategy

Tool Evaluation and Selection Strategy

Analytics and Datawarehousing

Decision Support

Source Transformation

Data Source

PerformanceAnalytic Layer

ODS Data warehouse

Feed-back loop

ETL LayerStaging Area

ETL Layer

Source Systems –Domain Apps, Legacy Systems

Transaction Applications Layer

Analytics Reporting Mart Creation Mining Models

Analytic Layer

Direct connection Met

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How we differentiate?

• A BI strength spanning the past ( the data warehouse), present (patterns in data) and future (prediction) of data and support and maintaining the same

• Capability to build BI COE and articulate enterprise intelligence strategy Driven by strength of

– Analytic IPs

– Accelerator toolkits

– Agile Framework

– Domain and Technology thought leadership driven consulting

• Relationship based data strategy for client from conception to maintenance

Service Offerings

Analytics and Data Mining

Implementation Methodologies

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Enterprise Risk Management

Analytics

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Regulatory compliance

Prebuilt Data Model

Prebuilt Metrics and Analytical structures

Prebuilt Predictive and Quantitative Models

CRM Analytics

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Human Capital Management

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Trai

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Specialized Financial Analytics

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BI and A Innovation Lab – Accelerators

Some of the tools developed by the Innovation team that would be part of all projects areSolution Accelerators

Other solution accelerators in usage• TRACE IT• Teradata Analytic • Excel Comparator • SAS Web Compiler

• Web Services Manager• Excel Formatter• Mapping Change Propagator

XML-based query engine for Data Integration environment to ensure ETL coding standards and locate coding errors; saves up to 90% time

A tool for determining duplicate reports, identifying set-subset related reports, location of dead variables, etc., within any BI environment; saves up to 70% time

BI environment metadata foundation tool to create semantic layers such as universes and models in an automated fashion; saves up to 40% time

BI and A Innovation Lab – Jump Start Analytics

Some of our prepackaged analytics includes prepackaged data model, prepackaged reports, prepackages cubes, metric and dimensions. Hexaware has prepackaged analytics in following areas

Jump Start Analytics

Our HR analytics enable organizations to get meaningful insights from HR data collected from various enterprise-wide HR and Non HR systems.

Our CRM analytics enable organizations to get deep insights on customer data collected across various channel systems.

Our niche analytics covers various niche areas that includes Credit Card analytics, Mortgage analytics, Credit Risk Analytics and Capital Market Analytics.

Our Airline Analytics enables organizations to strategize their business activities by analyzing various data such as customer loyalty, routes, and cargo.

Our Enterprise Risk Management subsidiary Risk Tech provides Analytics implementation and services related to the banking risk domain

Customers

Q & A

Q & A

You can also reach us at

biinnovations@hexaware.com

Thank You For Attending

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