Hexaware Webinar Series Presents:

Transcription

1 Hexaware Webinar Series Presents: Know your customer better - Insights into CRM Analytics Sundip Gorai Vice President, Hexaware Technologies Dec 4 th, 12 pm Eastern Time

2 A Global IT and BPO Service Provider India s Fastest Growing Mid- Sized Company 17 years of technology outsourcing expertise 18 Global locations 32 offices worldwide Transportation 55 Global 500 clients 166 Clients served worldwide 187 USD mil Revenues, 06 BFS 6900 Employees worldwide Business Areas ERP/HRIT Insurance Our mission : To build value for clients through innovative use of technology and talent

3 Strategies and Strengths L L E E A A D D E E R R S S H H I I P P T T H H R R O O U U G G H H F F O O C C U U S S 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 Core Competency Management of businesscritical applications offshore Organization Traits Consultative approach, Responsive and Resultoriented Robust Backbone World-class infrastructure, Flexible delivery models, SEI CMMi Level 5, BS7799 Track Record 88% Repeat Business Offshore transition expertise Global Delivery E E N N H H A A N N C C I I N N G G V V A A L L U U E E

4 Hexaware CRM Analytics Enhance Your Customer Experience Better

5 Hexaware CRM Analytics Solution Offering

6 The analytic CRM Strategy Choice points Analytic CRM Strategy Prebuilt Marts Stats and Mining Models Off the Shelf Tools Platform of Choice

7 Hexaware CRM Analytics Domain Coverage Behavior Analysis Product Acquisition Analysis Customer Profiling Campaign Analysis CRM Functions New Business Analysis 360 Customer View Cross Sell Analysis Service Request 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 Adhoc Analysis

8 Campaign Analysis

9 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

10 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?

11 Campaign Analysis Model

12 Campaign Analytics Report

13 Cross-sell Analysis

14 Cross-sell Analysis 1. Which is the most popular product family holding and What is the associated 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 analysis 1. 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?

15 Cross-sell Analysis Model

16 Cross-sell Analytics Report

17 Service Request Analysis

18 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

19 Service Request Analysis Questions 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.

20 Service Request Analysis Model

21 Service Request Analytics Report

22 360 Customer View

23 360 Customer View Data Model

24 360 Customer View Sample Reports Service call analysis report Customer profile analysis report Opportunity analysis report ERM enquiry analysis report

25 360 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

26 Other CRM Functions Analysis

27 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?

28 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?

29 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?

30 Customer Profiling What kind of questions the model will answer for you? Questions based on Customer Demographics Age Income Profession Marital Status Industry Legal Entity Net Worth Equity Capital Questions based on Relational Characteristics Product Holding, Vintage, Age on Book Questions based on Transactional Characteristics Channel Usage and Profitability Score

31 Hexaware CRM Analytics Product and Service Offering What will you get? 1. Pre-Built Data Model (E/R and Dimensional) to Jumpstart your Analytics Implementation 2. Pre-Built Catalogue of Metrics and Dimensions to aid your analytic needs 3. Pre-Built Reporting templates instantiated in the technology of your choice for tactical reporting needs 4. More than 100+ Critical Business questions covering various sub-functions to support you in ad-hoc analysis 5. Pre-Built Metadata/Pre-Built Reports 6. Onsite Offshore execution model to reduce cost 7. HR Consulting expertise

32 Hexaware CRM Analytics Data Model Richness E/R Model Approx 100+ entities Approx attributes Dimensional Model 200 Dimensions 70 Measures 200 Reports

33 The analytic CRM Strategy Choice points Analytic CRM Strategy Prebuilt Marts Stats and Mining Models Off the Shelf Tools Platform of Choice

34 Stat Analysis/Mining Tools and Techniques Tools Mining Techniques Classification/Categorical Analysis Clustering /Association/Neighborhood Analysis Decision Trees Parameter Selection and Improvement Forecasting Simulations Optimization Others 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

35 Data Mining Value Proposition for CRM

36 Data Mining Process

37 Analytic CRM project design and Execution Cross Sell Upsell Churn Retention Segmentation Customer transition to higher segment Campaign objective Campaign description Customer selection Closing the Loop Learning Feedback 14 Campaign effectiveness Evaluation model (response and returns to cost) 12 SAS Datasets SAS Datasets Response to multiple campaigns stored as history 13 Reporting AND analysis (use SAS BI/ micro strategy/ms excel) 11 Siebel schedules and executes campaign Owner of campaign budget,costs and Segmentation other constraints 10 SAS Datasets 9 Assignment model Tele marketing Direct mail SAS Datasets Right product/right customer/right channel using SAS linear programming Walk in to advisors Other medium Customer attributes age/gender/ Relationship details 6 Data Mining Techniques Model Selection 4 Customer exclusions criteria SAS Datasets SAS Datasets Customer selection Based on other attributes SAS Datasets 8 n SAS Datasets Filtered Customer List Select customers who did not respond to past campaign and are poor on other parameters Business flow of campaign management 5 Response propensity Purchase score Credit score Relationship value Response propensity score for maximum likelihood of response Data Mining Techniques Model Selection Other business criteria Split customer list SAS Datasets Filtered customer list based how old is relationship 7 SAS Datasets

38 CRM Integration Process Flow Expertise Transition SAS Process ETL ETL Sources Staging data store Massaged Data Campaign objective, budget and description Customer selection Modeling and feeding scores Data Preparation Planning the campaign Customer exclusion Learning from the campaign ROI modeling, campaign Optimization Campaign management targeting the customer/ segmentation manager/ Reporting and analysis (SASOLAP /microstrategy/excel) Executing the campaign (manual) Campaign optimization List generation with optimization model

39 Stat Analysis/Mining Applications and Techniques Applications 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 Mining Techniques 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

40 CRM Analytic using Mining models Customer Segmentation 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 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

41 CRM Analytic using Mining models Prediction of Credit Card Defaults Objective To predict the probability of a credit card customer defaulting on payments Methodology 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 Reducing Customer Attrition Objective To retain profitable customers by predicting their possibility of attrition Methodology 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

42 The analytic CRM Strategy Choice points Analytic CRM Strategy Prebuilt Marts Stats and Mining Models Off the Shelf Tools Platform of Choice

43 Hexaware SAS Implementation Capability SAS Enterprise Intelligence Platform SAS Banking Intelligence Solutions SAS Banking Intelligence Solutions SAS Insurance Intelligence Solutions SAS Insurance Intelligence Solutions SAS for Enterprise Risk Management SAS for Enterprise Risk Management SAS Customer Intelligence SAS Customer Intelligence SAS Data Integration SAS Data Integration SAS Intelligence Storage SAS Intelligence Storage SAS Enterprise BI Server SAS Enterprise BI Server SAS Enterprise Miner SAS Enterprise Miner Analytics BI Infrastructure and Data Mining Consolidation and Maintenance

44 The analytic CRM Strategy Choice points Analytic CRM Strategy Prebuilt Marts Stats and Mining Models Off the Shelf Tools Platform of Choice

45 Tool Evaluation and Selection Strategy Analytics and Datawarehousing Performance Analytic Layer Analytic Layer Analytics Reporting Mart Creation Mining Models Decision Support ODS Data warehouse Source Transformation Feedback loop ETL Layer Staging Area ETL Layer Direct connection Meta data layer Source Systems Domain Apps, Legacy Systems Data Source Transaction Applications Layer

46 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

47 Service Offerings

48 Credit Risk and Credit Scoring Market Risk Operational Risk Structural Risk Analytics and Data Mining Data Mining, Statistics, and Quantitative Models CRM Analytics Prebuilt Predictive and Quantitative Models Prebuilt Metrics and Analytical structures Prebuilt Data Model Implementation Methodologies Regulatory compliance Enterprise Risk Management Analytics Demographics Productivity Compensation and benefits Staffing and recruiting Training and Development Customer Loyalty Customer Acquisition/Retention Cross Sell /Up- sell Campaign Management Enterprise Performance Analytics Credit Card Analytics Mortgage Analytics Capital Market Analytics Performance Analytics Cargo Analytics MRO Analytics Route Optimization Human Capital Management Specialized Financial Analytics Airline Analytics

49 BI and A Innovation Lab Accelerators Solution Accelerators Some of the tools developed by the Innovation team that would be part of all projects are 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 Other solution accelerators in usage TRACE IT Teradata Analytic Excel Comparator SAS Web Compiler Web Services Manager Excel Formatter Mapping Change Propagator

50 BI and A Innovation Lab Jump Start Analytics 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 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

51 Customers

52 Q & A Q & A You can also reach us at biinnovations@hexaware.com

53 Thank You For Attending For a recording of this webinar please visit: Download Case Studies & Whitepapers at: Upcoming Webinars Hexaware Webinar Series Data Mining- Seeing the Future and Knowing the Patterns Of Your Business Using Your Organization Data How can banks leverage analytics across various perspectives protecting their current investment in technology? Register Today!