CRM and Omni Channel plays the critical role of identifying and delivering key messages in a timely and efficient manner

1. CONFIDENTIAL & LEGALLY PRIVILEGED
CRM – Enabling farming game or mining game?
The Business & Engineering of CRM
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Tenet 1 : What do you want CRM to be?
System of
Value
Appropriation
(Decision Support
System)
System of
Engagement
(Multi-Channel
Management)
System of
Record
Keeping
(Customer
Contact
Management)

3. Page 3
Customer
Loyalty
Customer
Experience
Customer
Relationship
• Retention Value in
nature
• Advocacy in
approach
• Integration in
nature
• Interactivity in
approach
• Operational in
nature
• One view of multi-
channel approach
Tenet 2: One can only influence the customer’s behavior by understanding one’s
value to the customer
Return on
Value Creation
Return on
Experience
Return on
Loyalty

4. Page 4
Delay in
Decision /
Action
Increased
Irritation
Increased
Desire
Increased
Commitment
Anchored in
Future Past
Skepticism
Low
High
Tenet 3: Challenge is to remain competitive by figuring out how to keep customers
longer, grow them into bigger customers, make them more profitable, and serve
them more efficiently
CRM Analytics Building Blocks – The mining game

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CRM Building Blocks – The Farming Game
Circle of Excellence
>
>
Retain Retain
& Grow
Reduce
Cost
Invest &
Grow
Profit
Potential
1. One View 2. Segment customers
3. Develop Segment Strategies
4. Engage High Potential Clients5. Analyze Effects of Contacts
6. Feedback Learning
1. One view of all activities
2. One view across service touch
points
3. Single Window for various services
1. Schema based on duration of
relationship
2. Schema based on customer
discontinuation rates
1. Primary Contact Strategy
2. Alternate contact strategy
3. Messaging
1. Access to personalized attention
2. Speaking opportunities
3. Customized offers
1. Recency and Frequency of contact
2. Ability to meet follow up requests
3. First Time Right measures
1. Contact Rules
2. Channel Efficacy
3. Marketing Mix Efficacy
4. Efficacy of Treatment Vector

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Behavioral
Targeting
Promotion Response
Models
Channel Efficacy
Personal
Visits
Campaigns
Programs
Execution Planning
Decision/Action Mandate: Take appropriate adhoc decisions without destroying long
term value of a relationship

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 Based on invoice data and user
behavior
 The customers are educated in the
services and products that they are not
using or have not used at all.
 Based on behavior the tips are altered
and re-shaped to encourage usage
Behavioral Mandate: CRM ensures that relationship remains the central theme

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 Gain customer permission to
remain in contact
 Send exit mails with relevant
messages and tempt with
offers
– New subscription plans
– Latest handsets
 Continue the dialogue and
listen to the response
 9 out of 10 customers
consider to come back
Influencing Mandate: Marketing today is a conversation, and the right conversation
changes everything
Tactic - Proactive win-back of customers

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Multiple channels in one integrated customer interaction that is
carried out realtime
External Databases
Web/WAP
Portal Modules
EmailsSMS, MMS,
WAP Push
Direct
Mails
Call Centre, IVR
& POS Modules
Campaign
Management
Client Data Sources
- fulfilment data
- enriched profile data
- behavioural data
- prospect contact data*
- segmentation data*
- permission data*
- basic customer data*
- offers and prices*
- usage triggers*
- segmentations*
1to1 Execution Platform
Systemic Thinking Mandate: Assetization of CRM leads to better leverage

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CRM Analytics Mandate: Ensuring high success rate through micro-segmentation and
precise targeting with relevant offer and value
Online
Queries
Standard
Reports
Visualization
Tools
Ad hoc
Queries
Spreadsheet
Analysis
Dashboards
Key
Performance
Indicators
Performance
Management
Balanced
Scorecards
Predictive
Modeling
Data
Mining
Segmentation
Analysis
Experimentation
Simulation
Cluster
Analysis
Risk
Analysis
What happened
REPORTING
Why did it happen
ANALYSIS
What is happening now
REAL-TIME
MONITORING
What is likely to
happen in the future
PREDICTION
Business Intelligence Business Analytics
Increasing use of Assisted
Insights generation

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Reqs
Marketer
Contacts
Customer
Centralized usage of tools / analytics via “shared services”
Results
Answers
Data Tools
People
Process
Process
Analytics
Centralized
marketing
execution
Cultivated
model
expertise
(trusted
advisors)
Foundation for
measurement
Systematic
execution
Operating Framework Mandate: Shared services enable Predictive CRM System to
become "Actionable and Consumable" by marketers.

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Data Flow Mandate: Ensuring highest data quality and governance throughout the
stream.
C-Sat Data
Agent Logs
CRM Data
Call Transcripts
Switch Data
Data Linking
& Cleaning
Text Mining
Framework
Derived
Attributes
Framework
Common Text
Representation
Indexed XML/
CSV files
Data
warehouse
Data Sources
Data Processing &
Conversion Stage
Data Storage Stage Analysis & Reporting
Stage
Assisted Insight
generation
Decision Matrix
Reporting &
Automation
Social Signals
Digital Pathways

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CRM Building Blocks – Successful adoption drives 75% of the value

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Data Gathering & Storing
Create a centralized data asset to
store internal data across all
businesses and external data
from select bureaus
Develop consistent standards and
procedures around data
structures & layout
Define variables/fields within
which data will be collected
Establish pre-specified timing to
refresh
Create a repository of customer
life-cycle models
Build a sustainable Analytics Capability which improves shareholder
value through creating best in class customer lifecycle analytical models by
leveraging internal and external data
Strategic Imperative
Efficient Process
Decide strategies for model
building
Prioritize between
generic models vis-à-vis
custom models
Share best practices
Identify Opportunities across
business and product lines
for building new models
Analytical Engine
Develop models for ongoing use
Update models regularly
Establish uniform guidelines
and access procedures
Monitoring & Refining
Measure current performance
Layout a well-defined success
metrics to track improvement
across each element of
customer life-cycle
Manage a robust monitoring
system to track against
metrics
Prepare management reports
to clearly articulate
shareholder value generated,
and link to management
policy and growth decisions
Enablers-Funding, Governance and Resources
Generate executive sponsorship and clear ownership to reinforce
analytics capability development; centralized funding and resource
deployment
Building Analytics Culture – Deepening the association with competencies in Market Testing, Focused Curiosity, Data
Orientation & Gamification
CRM Analytics Building Blocks: Creating easy to use, comprehensive yet actionable
environment

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CRM in summary needs to enable Social Learning Cycles at C-Suite
Uncodified
(Tacit)
Codified
(Explicit)
Undiffused
(personal)
Widely Shared
(Collective)
Diffusing
Absorbing
Scanning
Problem Solving
Combining
Articulating
Exemplifying
Assimilating

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Listen –
Listening Platform
Engage –
Engagement Platform
Dialogue –
Tagging Solution
Conversation –
1to1 marketing
Communicate –
Marketing Service Ops
Technology in summary need to act as conversation Moderators: Bringing in
Products, Services & Tools together

17. CONFIDENTIAL & LEGALLY PRIVILEGED
CRM Analytics
The Building Blocks for Mining Game
© adiyanth – Distribution Restricted

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In Summary... Analytical CRM Framework should improve the sales & marketing
processes in a way that works for both customers & service providers
Campaign
Data
Offer/
Promo
Data
Response
Data
Txn Data
CAMPAIGN SELECTION DECISION
SUPPORT SYSTEM
1. An Integrated business analytics
framework
2. Activity based costing methods
3. Optimized customer
segmentation
4. The ability to identify, measure
and disseminate the profitability
of customer segments
Modeling Layer
Data Management Layer
CLV Layer
Analytics’ Layer will enable:
1. Consistently manage, analyze,
and report on relevant data
2. Calculate Cost-to-Sale & Cost-to-
Serve per customer
3. Make the most compelling offers
to the right customers

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Base Camp: Data Aggregation Services
a. Scrubbing
b. Cleaning
c. Validation
Anticipational Stage: Exploratory Data Analysis
a. Descriptive Look
b. Correlational Feel
c. Deep Dives
Aspirational Stage: Insight Generation
a. Significant Few from Important many
b. Critical hand-full from Significant Few
c. Winner from the Next BestHalf-way: Statistical Services
a. Predictive capability
b. Relative Ranking capability
c. Conditional Modeling
Summit: Integration and Decisioning Services
a. Lifetime Value
b. Optimizing
c. List Generation
Analytical CRM Building Block – The Mining Game / Mountaineering Approach
…………Then, New Data comes in and we start all over again

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1. Data + Information + Technology = Decision Support
2. Analysis + Turn Around Time + Flexibility = Agility*
3. Problem Statement = f(Economics, People, Flexibility,
Quality)
* Agility is defined as the ability to swim with the flow or trend
Three Driving Principles of Analytics Development and Deployment

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Sales
Strategies
Marketing
Strategies
Customer Service
Strategies
Risk Underwriting
Strategies
Typical Analytics Usage Structuring
Analytics Technologies
Decision Support System
Fine Tuning

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Metadata
Service
Smart Client
Co-Creation
Services
Analytical Services
BI/Reporting Services
Browser
Access Services /
Configuration
Services
Metadata File Systems Databases
Integrated Approach to enable Analytics delivery based on SaaS-centered Business
Model and SOA Platform

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Stand-alone
Models
Integrating
Models
Incorporating GUI
Integrating all SOA
features
Product Component
Service
Component
High
Low
HighLow
Creating Modeling
Systems
Development Approach – We believe SaaS approach is most cost effective

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CRM Analytics Wireframe - Components
Existing CRM system
Acquisition and
Attrition modeling
Customer Data
Integration &
Cleansing
Customer list
CSAT Surveys, Market
Surveys and Demographic
Profiling exercises to
collect dataOperational Systems
Enriched Data
Segmentation &
Profiling
Customer cross sell,
up sell models
Customer life time
value analysis
Behavior and
Collection
Scorecards
Update Operational System with model
scores for further processing
Update CRMS with
model output
Advanced Analytical Models
CustomerBehaviourAnalysis

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Quantitative Modeling is the workbench for creating Business Information from Data
– Forecasting System to forecast business performance (N=G)
– Predictive Modeling System to predict segment behaviors impacting
business performance (N = S)
– Optimization System to optimize various business levers at customer level
(N = 1)

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Forecasting System: Integrated Segment-Level Forecasts
5-Year NPV Forecasts
Industry Trends
Purchase Patterns
Customer Loyalty
Brand Perceptions
Macro-Economic Trends

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Predictive System – Multi Stage Customer-Level Behavior Modeling
The output of a Predictive Modeling System can generate reason codes, scores and
relative ranking of customers against each behavior
Activation:
1st Purchase Model
Dormancy Model
Usage:
Incentive Modeling
Credit Risk Severity
Shadow Limit Models
Dormancy:
Opportunistic Behavior
Alternate Value
Proposition
Attrition:
Silent Attrition
Closures
Solicitation:
Response Modeling
for DM Campaigns
Acquisition:
Approval Model
NPV Model
1st Payment Model
Life Time Value

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Optimization System – Life Time Value Optimization
Constraint A
Constraint B
Constraint COptimal
Solution
Multiple Objective Optimization
• Resource Allocation
• Fine Tuning Marketing Spends
• Fine Tuning Cost Structure
A
B
C
D
E
F
G
H
Traveling Salesman Problem
• Identifying Least Cost route
• Sequencing of sales follow up routes
• Sequencing operation activities based on LEAN
principles
The output of Optimization Systems identify optimal solutions and also
provide framework to conduct sensitivity analysis

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Data Warehouse Frameworks
The DSS Roadmap includes several data warehouse frameworks, with a focus on architecture, data,
infrastructure, support and tools.
DATA WAREHOUSE
ARCHITECTURE
Architectures are built on several different
levels, providing companies with the scalability
to build enterprise solutions
DATA WAREHOUSE
DATA
Data solutions/practices capture meta data, which
provides information about the data. This approach
helps ensure that data is not only accurate, but also
applies to the specific business need.
DATA WAREHOUSE
Infrastructure
Data methodologies focus on managing data to meet
specific business needs. These methodologies are vital
to helping corporate decision makers access critical
information for business decisions.
DATA WAREHOUSE
Support
By using DSS roadmap, companies can help determine
and plan the support needed to implement DSS, as well
as the resources needed to maintain the systems
DATA WAREHOUSE
TOOLS
With experience in dozens of platforms
and technologies, experts will help you
determine the best tools to get the job
done, quickly and effectively.
Business Justification/
Business Pilot Case:
This first step considers your
objectives and whether the cost of
building a system can be justified
from a business perspective. We
will help you document a pilot
business case to determine how
DSS can impact and support your
business goals
Business
Justification
Business
Pilot Case
Technical
Goals
Decision Support
PROJECT
LTV Modeling for
Decision Support
+
Data Practices Wireframe

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Data Management Layer

31. Page 31© adiyanth – Distribution Restricted
Technology Framework of CRM Delivery
Framework for CRM
Business Intelligence
Load/Transform
Business Insights
Analytics Engine
Analytical Models
Algorithms for predictive analysis
Web Services
Architecture
Client Web services
Outbound Web Services
Presentation Services
Portal For Clients
Authorization and Authentication
Personalized information
Analytical Databases
Roles and Identity Management
Dashboards
Billing
Billing per service used
Billing Statements
Interfaces to SAP
Invoice Generation
Data from Clients
Client Web services
Web Forms Architecture
Offline modes
Adhoc Analysis/Reporting

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 360º view of a person
– Person centric, experience based
– Single, longitudinal view of individual regardless of:
•Role(s)
•Communication channel
 To engage prospects and members individually
–To educate and inform
–To help them manage their needs/wants
–To encourage and enable them to participate in their purchase journey
–To intervene when appropriate
–To manage those interventions to successful behavioral outcomes
 To get and keep their attention in a very complex world of competing
influences
The End Vision

33. CONFIDENTIAL & LEGALLY PRIVILEGED
For more details reach out to:
Aditya Madiraju
aditya.madiraju@adiyanth.com
+91 888 494 8072
+91 997 163 3884
Aditya Madiraju has a passion
for data and the strong
desire—as well as drive—to
help companies transform the
way they do their business —
”compete and win” on
analytics.
Aditya’s clients appreciate his
unique ability to identify &
triangulate their most
challenging business issues;
then design and implement a
foundational data driven
process to address them. His
achievements, includes
establishing a network of data
services in partnership with
marketing service centers and
the agency that fulfills the day-
to-day marketing execution
and the long term analytical
needs of his clients. His
innovative solutions help
clients navigate the complex
and often confusing process of
planning and achieving return
on marketing investment.
Aditya held many data related
roles of varying responsibilities
at BFSI organizations , where,
he was on the front lines
instituting data-based
capabilities.

34. Page 34
Analytical CRM Engineering

35. 35
Content Decisioning
Engine
Consumer
Profile
Incremental
Update
Interaction
Management
Analytics
& Reporting
Preference Center/
LookUp
Call
Center
Web Site
Analytics
IMPACT
Data Warehouse
Prospect
Lists
Property Data
Listening
Platform
Listening
Data Mart
Intellisight
CRM Operational System

36. Page 36
Impact Pro Engine
ConsultantConsumer
Marketing Data Sources Third Party Data Sources
Analytic Mart
Communication
Engagement Cloud
Call
Center
Sales &
Marketing
Network
Development
Member
Care
Incident
Management Diagnostic
Tools
Underwriting
Product
Development
Interaction Data
Back into
Warehouse
Interaction Data
Back into
Warehouse
Consumer Engagement Platform
CMDB
Consumer Engagement Platform