This document discusses customer value optimization, which involves identifying a company's most valuable customers, getting more value from them, and acquiring more customers like them. It outlines several key concepts for achieving this, including measuring customer lifetime value, balancing new and existing customer growth, boosting customer engagement through personalization and experiences, and leveraging customer data. The full customer value optimization framework includes developing a customer-centric culture, adopting a company-wide approach, using CLV-based measurement, implementing a data strategy and customer profiling, conducting value-based segmentation, and delivering customized experiences.

1. Customer Value
Optimization
Alexandre Chaves
May/2022 UNLOCKING THE VALUE
OF YOUR BEST
CUSTOMERS
RADAR

2. Maximizing the total customer
value of a business, by:
 Identifying and
the most valuable customers,
 Getting more value from them,
 Acquiring more like them.
CUSTOMER LIFETIME VALUE
How to measure the future value of customers?
Customer Lifetime Value (CLV) allows companies to predict the
future value of their customers and thus identify the most
profitable ones based on past behavior.
LONG-TERM PROFITABILITY
How do companies achieve sustainable growth?
Balancing their goals between acquiring new, high-value
customers and developing long-time relationships with their
most valuable existing customers.
CUSTOMERS ENGAGEMENT
What is the driver of Customer Lifetime Value?
Customers that have an emotional relationship with a brand
have a Customer Lifetime Value 306% higher than average.
HYPER-PERSONALIZATION
How to improve Customer Experiences?
Hyper-personalization is the most advanced way in which brands
can adapt their targeting strategies to customer groups.
CUSTOMER EXPERIENCE
How to boost customer engagement?
Companies cannot determine the behavior of their customers,
but they can influence it by creating memorable experiences for
their best customers.
CUSTOMER FIRST-DATA
How to know your customers?
The richer your data, the better your will understand your
audience and the better the segmentation and personalization
based on this data.
What is Customer Value Optimization?

3. COMPANY
VISION
The BuildingBlocks ofCustomer Value Optimization
CUSTOMER
CENTRIC
CULTURE
COMPANY-WIDE
ADOPTION
CLV-BASED
MEASUREMENT
DATA
STRATEGY
CUSTOMER
FIRST DATA
ACTIONABLE
CUSTOMER
PROFILING
VALUE-BASED
CUSTOMER
SEGMENTATION
CUSTOM-
TAILORED EXPERIENCES
PRODUCTS
COMMUNICATIO
NS

4. CUSTOMER CENTRIC CULTURE
 WHAT OUR CUSTOMERS NEED TO GET DONE
NOW AND HOW CAN WE HELP?
 WHAT RELATIONSHIPS DO OUR CUSTOMERS
EXPECT US TO ESTABLISH WITH THEM?
 WHAT VALUE DO OUR CUSTOMERS NEED TO SEE
BEFORE THEY ARE WILLING TO PAY?
 MEASURE SUCCESS BY CUSTOMER LIFETIME
VALUE
PRODUCT CENTRIC CULTURE
 WHAT PRODUCTS/SERVICES CAN WE SELL TO
OUR CUSTOMERS?
 WHAT RELATIONSHIPS DO WE NEED TO
ESTABLISH WITH OUR CUSTOMERS?
 HOW CAN WE MAKE MONEY WITH OUR
CUSTOMERS?
 MEASURE SUCCESS BY PRODUCT
REVENUE/PROFITABILITY
Customer centricity Is a way of doing business with consumers in a way that provides a positive customer
experience before and after the sale in order to drive repeat business, customer loyalty and profits.
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Customer centricityx Product centricity

5. SWOT Analysisof Customer Centricity
Opportunities
 The costs of attracting a new customer are
accelerating;
 41% of an e-commerce revenue is created by just 8%
of its customers;
 Only 27% of new customers return;
 50% of customers will switch to a competitor after a
bad experience.
Strengths
 Long-term customer retention and loyalty;
 New customers tend to trust the experience of other
customers;
 Brand reputation;
 Reduction of marketing costs;
 Competitive advantage.
Weaknesses
 Product-centric culture;
 Limited and isolated customer data;
 It's hard to hire marketing analytics experts;
 Marketing to a generic customer.
Threats
 End of third-party cookies;
 Customers have more information and purchase
options available than ever before and expect
personalized and relevant experiences;
 The acceleration of online shopping generated by
COVID-19 has created new markets and changed
existing ones, increasing competition.

6. With individual level we can …
• … identify future top customers.
• … identify possibly profitable, but inactive customers.
• … minimize spending for unprofitable customers.
• … optimize acquisition channel.
• … optimize and benchmark customer development.
With aggregated level we can …
• … value a company by forecasting current and future
customer behavior.
• … benchmark the value of the customer base over time.
• … Improve financial valuation of companies (due
diligence)
Why is Customer LifetimeValue relevant?

7. How to have relevant
conversations with them?
Answer: Hyper-Personalization
Who are my most
profitable customers?
Answer: Customer Lifetime Value
Why do they need my
products or services?
Answer: Jobs to be Done
Why would they buy from
me again, not a competitor?
Answer: Customer Experience
What do you need to know aboutyour Customers?

8. Answering your BusinessQuestionsthrough Data
CLV - Customer Lifetime Value
Who are the most valuable customers?
Profiling / Clustering
What are the ideal customers profiles?
RAD Segmentation
What marketing tactic should I apply to these customers?
Churn Propensity
Which customers are likely to leave?
Recommendation
What products might they be interested in?
Affinity / Conjoint Analysis
Which product features meet their needs or preferences?
Price sensitivity
How much are they willing to pay for these products?
Uplift analysis
Which customers are more likely to buy if they receive a specific treatment?
Attribution
What are the most efficient channels to engage these customers?
Sentiment Analysis / Topic modeling
What do customers think about my business or products?
BUSINESS QUESTIONS ANALYTICS TECHNIQUE

9. Segmentation Personalization
Targeting Automation
Positioning Integration
Lifetime Value
Jobs to be Done
Buying Behavior
Business
Understanding
Qualitative
Research
Data Collection
Customer
Understanding
Marketing
Strategy
Marketing
Mix
Data
Strategy
Data
Activation
Data Enrichment
Customer
Experience Measurement
Experimentation
Product
Place
Promotion
Price
Data-Driven Marketing Framework

10. Our
customers’
imperatives
Our
end-to-end
services
Delivering a complete framework that enables companies
to achieve sustainable growth by maximizing the value of its customers.
Our
purpose
Our
differentiated
strategy
Human-based
relationship
model
Efficient,
flexible and
affordable
Continuous
innovation in
AI & CVO
To be the leading provider of end-to-end modular CVO solutions.
RADAR strategy – Bringing itall together
Customer
First-Data
Tailored to
business
needs
Knowledge of
the region
360-Degree
Customer
Profiling
Dynamic
Segmentation
Predictive
Targeting
Affinity-based
Recommendati
on
Smart Data
Activation
Identify the
“best
customers”
Create
memorable
experiences
Acquire more
like them
Get more value
out of them
Pay-as-you-
grow
CUSTOMER UNDERSTANDING CONTEXTUALIZED & RELEVANT
CONVERSATIONS

11. PREDICTIVE
TARGETING
CUSTOMER-FIRST DATABASE
DYNAMIC
SEGMENTATION
360-DEGREE CUSTOMER
PROFILING
SMART DATA
ACTIVATION
AFFINITY-BASED
PERSONALIZATION
Unlocking the Power of Customer-First Data
COLLECT, TRANSFORM AND ENRICH
YOUR CUSTOMER DATA.
CREATE A PORTRAIT OF
YOUR CUSTOMERS
DYNAMICALLY GROUP CUSTOMERS
WITH SIMILAR PATTERNS
TOGETHER
DISCOVER WHICH SEGMENTS IT MAKES SENSE TO
FOCUS EFFORTS AND RESOURCES BASED.ON
EXPECTED FUTURE RESULTS.
SEND HIGHLY CONTEXTUALIZED AND RELEVANT
COMMUNICATIONS TO THE BEST CUSTOMERS AT
THE RIGHT PLACE, TIME AND CHANNEL
TAILOR OFFERS AND PROMOTIONS FOR YOUR BEST
CUSTOMERS, RECOMMENDING PRODUCTS AND CONTENT
THAT ARE RELEVANT TO THEM.
INTERNAL EXTERNAL
STRUCTURED UNSTRUCTURED
DATA SOURCES

12. CUSTOMER-FIRST DATA
Calculations
Machine Learning Business Rules
Natural Language
Processing
Identity Management
Purchases, Payments, Returns,
Refunds
Surveys, Chats, Posts, Forms
Website, Email, Apps,
Messages, IoT
Attributes, Financial,
Services, Loyalty
Business Understanding
What are the Value Propositions?
Qualitative Research
What data is needed?
Data Acquisition Plan
How to get the missing data?
Data Assessment
What data is available?
DATA
ENRICHMENT
DATA
TRANSFORMATION
DATA
COLLECTION
DATA
STRATEGY
Transaction History Customers Feedbacks
Customers Engagement
Customer Records
INTERNAL EXTERNAL
Data Cleaning Data Normalization Data Aggregation
Unify customer IDs Standardize data Summarize data
Organize data
Regressions, Classifications,
Correlations
Metrics, KPIs, Scores Sentiment, Topics Tags, Attributes
Creating a Customer-First Database

13. Customer Profile
BUYING BEHAVIOR
HOW do they buy?
• Frequency
• Status
• Spending
• Basket size
• Habits
• Products Reviews
• Metrics
• Sentiment
• Topics
• Touchpoints
• Demographics
• Firmographics
• Geographics
• Psychographics
• Lifestyle
PERSONA
WHO are they?
JOBS TO BE DONE
WHY do they buy?
EXPERIENCES
HOW do they feel?
• Goals
• Needs
• Wants
• Interests
• Pain points
360-Degree Customer Profiling

14. Tactic Target Goal Metrics
Retention
High-value frequent
customers
Keep them coming
CLV (Customer Lifetime
Value)
5 MARKETING TACTICS TO INCREASE CUSTOMER LIFETIME VALUE
R.A.D.A.R. Framework
Acquisition
Unknown customers with a
suitable profile to the
business value proposition
Identify and engage
ICP (Ideal Customer Profile)
similarity
Development
New customers with growth
potential
Increase orders frequency
and value
WS (White Space)
Activation High-engaged prospects Convert first transaction ES (Engagement Score)
Reactivation
Inactive customers with high
potential value
Win-back
PCLV (Potential CLV)

15. Metric Definition Calculation
Lifetime Value (CLV - $) An estimation of the amount of Revenue /
Margin a customer will generate over the
course of next x months, based on the past
purchases.
Individually -> Using the purchase history
data
Profile - Calculating the average CLV of
the customers in the profile
Potential CLV(PCLV - $) An estimation of the CLV of a customer if a
new purchase takes place.
Simulating a new purchase based on
customer AOV.
Share of Wallet (SOW - %) The percentage of a customer’s PCLV covered
by the customer's CLV.
CLV / PCLV * 100
Whitespace (WS - $) The difference between customer’s PCLV and
CLV.
PCLV - CLV
Value-basedPredictive Metrics

16. CLV Model Accuracy

17. PredictiveTargeting

18. Our machine learning-powered recommendation engine, optimized by your business rules, puts your data to work and
provides the opportunity for your marketers to engage customers in a personalized way, deepening existing relationships and
building new ones and improving the customer experience.
Message
Subject
Product
Offer
Channel
Content
Format
SKU , collection, brand
Color, size, price
Promotions
Time
Discounts
RECOMMENDATION
ENGINE
Basket
Analysis
Customer
Profile
Product
Reviews
Surveys
Behavioral
Data
Business
Rules
Affinity-basedPersonalization

19. Smart Data Activation
What brands SAY What brands SELL What brands DO
HYPER-PERSONALIZATION
Message Content Channel Promotion
Value
Proposition
Price Journeys Service Level Benefits
What can be personalized?
How to create memorable customer experiences?
MARKETING PRODUCTS SERVICES
Turning your customer data into action