The document discusses customer relationship management (CRM) analytics and how it is evolving from simply tracking customer data to using data mining and predictive modeling to gain insights. It provides an overview of developing CRM analytics capabilities, including establishing data governance, building analytical models, prioritizing custom vs generic models, and monitoring metrics. The document also outlines how CRM analytics engagements are structured and operated, with strategists, statisticians, and analysts working on pilot programs, opportunity matrices, and engagement styles.

1. Critical Thinking Series
What is this New Animal called CRM Data Analytics??
a. Is this a New Wine in Old Bottle?
or
b. Old Wine in New Bottle?
or
c. New Wine in New Bottle?
Data-Driven Solutions and
CONFIDENTIAL & LEGALLY PRIVILEGED
Services

2. The CRM challenge …
How do we measure improvements....
Competing objectives… Not sure, how to prioritize
The New CRM can handle it……
Poor Data Quality………….we don’t capture that data
Marketing is “common sense”…… we don’t require such
complicated systems
Anyway, who is asking for it?
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3. We were taught CRM is a farming game
1. Customer View 2. Segment Customers 3. Develop Segment Strategies
Retain Retain
& Grow
Profit
Reduce Invest &
Cost Grow
Potential
1. Customer Type 1. Just Acquired Customers 1. Product Sequencing
2. Product Holding 2. New Customers (0.5 – 3 yrs) 2. Relationship Enhancers
3. House Holding 3. Seasoned Customers (3-5 yrs) 3. Loyalty Boosters
> 4. Life-time Customers (>5 yrs) 4. Attrition Reducers
Process View >
6. Feedback Learning
5. Analyze Contact Results 4. Engage High Potential Customers
1. Refine Strategies
1. Automated Decisions
2. Development of new 1. Preferred Channel 2. Customer Walk-ins
products/Services 2. Value-Enhancing contacts 3. Proactive Customer Service
3. Co-Creation 3. Contact Efficiency 4. Responsive & Personalized Service
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4. Increasingly, CRM is becoming a mining game Summit: Integration and Decisioning Services
a. Lifetime Value
b. Optimizing
c. List Generation
Aspirational Stage: Insight Generation
a. Significant Few from Important many
b. Critical hand-full from Significant Few
c. Winner from the Next Best
Half-way: Statistical Services
a. Predictive capability
b. Relative Ranking capability
c. Conditional Modeling
Anticipational Stage: Exploratory Data Analysis
a. Descriptive Look
b. Correlational Feel
c. Deep Dives
Base Camp: Data Quality Services
a. Scrubbing
b. Cleaning
c. Validation
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5. OK, we run campaigns here!
...Can it be delivered through what has already been started?
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6. Oh Really? How?
Why are these
customers
classified as
High preferred?
Which are the
behaviors I want to Customers as
change? Advisor
Engaged
Customers
Develop and
ROI Migrate Preferred
Nurture and Customers
manage
profitable What are the
Segment and Customers What is the next WOM referrals
manage product to be
Define Customer
campaigns sold? Who are the
Service Goals
customers
What are the increasing the
range of services positivity of the
Low associated with brand
Low Customer Focus Levels High
By Answering these questions critically
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7. Monthly Digital Dashboard and Scorecard
Supporting Analytics Value Enhancers
Campaign
Owners Management
 Systematically identify the need
Granular Reporting at
Global, Regional and
Country Level
 Follow the process to identify & develop adopters
 Create an adoption matrix to manage the adoption of
Channel
Owners campaigns
Daily/Weekly Channel Reporting
Reporting Stack Decision Stack
 Developed the feedback loop to weed out irritants
Delay in Increased
for quicker adoption High implementation Commitment
Skepticism
 Phase two project: on-site reviews of model
deployment Behavioral Stack
Increased Increased
Low Desire Irritation
And Doing More…… Future
Anchored in
Past
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8. …Standardizing expectations from Analytics delivery
Consistent
evaluation
criteria across
analytical
environments
Comparable
capabilities in
model
development
& application
Enhanced Transparency
through discussion of
best practices
Share…..Teach……Learn……
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9. Global Local
Challenges Challenges
MEASURE PRIVACY ROMI
The Data Team engages with
MONITOR COMPETITION
SKILLS clients to determine the
optimal audience, segment,
tactic, and timing for each
GLOBAL WRONG
AFFECT
CAMPAIGNS FOCUS campaign.
A Data Team to support clients with Planning
all campaign support from Personal
campaign planning, execution, to Visits
Execution
insight and analytics.
Campaigns
Programs
Marketing Mix Promotion Forecasting
Models Response Models Models
TechEd Attendance Model – Individuals RPS Model – Organizations
Shared services continue to search for new
ways to increase customer Continued Action
Rate (CAR) and ultimate end action.
…and saying we will support you all the way!
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10. ….. and finally delivering CRM Analytics Building Blocks: Creating easy to use,
comprehensive yet actionable environment
Strategic Imperative
Build a sustainable Analytics Capability which improves shareholder
value through creating best in class customer lifecycle analytical models by
leveraging internal and external data
Data Gathering & Storing Efficient Process Analytical Engine Monitoring & Refining
Create a centralized data asset to Decide strategies for model Develop models for ongoing use Measure current performance
store internal data across all building
businesses and external data Update models regularly Layout a well-defined success
from select bureaus Prioritize between metrics to track improvement
generic models vis-à-vis Establish uniform guidelines across each element of
Develop consistent standards and custom models and access procedures customer life-cycle
procedures around data
Share best practices Manage a robust monitoring
structures & layout
system to track against
Define variables/fields within Identify Opportunities across metrics
which data will be collected business and product lines
Prepare management reports
for building new models
to clearly articulate
Establish pre-specified timing to
Enablers-Funding, Governance and Resources shareholder value generated,
refresh
Generate executive sponsorship and clear ownership to reinforce and link to management
Create a repository of customer analytics capability development; centralized funding and resource policy and growth decisions
life-cycle models deployment
Building Analytics Culture – Deepening the association with competencies in Market Testing, Focused Curiosity, Data
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11. Great!! How are CRMAnalytics engagements operated?
Work Hours Client Contact
Strategists Strategists
Statisticians Statisticians
Request is taken Engagement style is
finalized
Analysts Analysts
Opportunity Matrix Pilot program is
is created at the end devised
of pilot program
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12. 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.
For more details reach out to:
Aditya’s clients appreciate his Aditya Madiraju
unique ability to identify &
Aditya.Madiraju@adiyanth.com
triangulate their most
challenging business issues; +91 997 163 3884
then design and implement a +91 888 494 8072
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, CONFIDENTIAL & LEGALLY PRIVILEGED
he was on the front lines
instituting data-based
capabilities.