The document discusses an optimization framework for using customer data to maximize customer lifetime value through identifying high-value customers, segmenting customers based on lifetime value, personalizing experiences, and providing insights to marketing teams so they can focus on their most profitable customers. The goal is to help companies unlock the power of their first-party customer data to improve customer retention and acquisition.

1. Customer Value
Optimization
Framework
How to turn data into marketing actions to
maximize
the value and create lifetime
relationships with your
most valuable customers
UNLOCKING
THE POWER OF CUSTOMER DATA

2. Every customer matters, but not all customers are
created equal, which is why businesses should
take extra care with high-value customers. If the
people who provide your business with more
revenue and higher margins don’t receive a higher
level of CX in return, they’ll likely go to a
competitor that they feel better recognizes their
value.
According to Adobe, 41% of an ecommerce
store’s revenue is created by only 8% of its
customers. It is crucial for any business to identify
who these customers are, rewarding them for
their loyalty and providing them with memorable
experiences.
Do you know who your
high-value customers
are ?
.1
8% 92%
VISITORS VISITORS
41%
REVENUE
59%
REVENUE
Revenue distribution (US)
Fuente: https://www.adobe.com/ca/experience-cloud/digital-insights/digital-economy-index.html

3. Customer Value Optimization means
maximizing the total customer value of your
business.
To achieve sustainable growth, companies must
balance their business goals between acquiring
new customers, turning one-time buyers
into loyal
customers and developing lifetime relationships
with their most valuable customers, by creating
memorable experiences to them.
Customer Value
Optimization
.2
Customer
Value
Optimization
Identify the
“Best customers”
Get more value
out of them
Create more
like them

4. The customer value optimization framework is an
innovative approach to executing your marketing
strategy to improve customer lifetime value
(CLV) by executing profitable customer acquisition
and customer retention strategies.
Customer Value
Optimization
Framework
.3
Customer
Value
Optimization
Client Company
Lifecycle
Segmentation
Lifetime value
Data Enrichment
Customer First Data
Personalization
Memorable
Experiences
Lifetime
Relationships

5. Marketers have become reliant on third-party
data, but with consumer privacy taking center
stage, companies need to unlock the power of
customer first data.
Customer-First Data is data sourced directly from
a prospect or customer. It includes both zero-
party data (information someone gives to you
proactively, like their email address, phone
number, or birthdate) and first-party data
(information observed by a brand about someone
on their owned properties, like what products they
clicked on or links they visited from an email).
Customer First Data
.4
ERP
, CRM, Ecommerce
Marketing Tools
Profiling and Segmentation
Recommendation
Onsite, email, mobile apps
Quantitative, qualitative, satisfaction
Page views, mobile apps use
IoT device
Connectors
ATTRIBUTES MOTIVATIONS
BEHAVIOR EXPERIENCE
Business Rules
Survey Tool
Tracking Tool
How do they
interact?
How do
they feel?
How are
them?
Why do
they buy?
SINGLE
CUSTOMER
VIEW

6. Data enrichment is a broad term referring to any
process that results in improvements to raw data
in order to make that data more valuable to the
business.
After creating a single source for all your
customer data, we use our ML models and your
business rules to transform raw data into
multiple lifecycle predictors that will be used to
calculate the individual CLV of your customers
and prospects.
Data
Enrichment
.5
ML Model Description
RFM Segments customers according to their buying behavior, based on recency, frequency and Avg.
Order Value.
Engagement Score Segments customers according to level of engagement, based on recency and relevance of
interactions.
Customer Profile Groups customers according to their behavior in interactions, preventing predictive models from
disregarding customer heterogeneity.
Similarity to ICP ICP stands for Ideal Customer Profile. This model identifies the attributes that best describe the
profile of the most valuable customers and also calculates the similarity of each customer with this
profile.
Product Affinity Calculates individual customer affinity with all products and content based on available data and
user-defined recommendation rules.
CX score Value that measures customer experience, based on data from all available satisfaction surveys.

7. Customer Lifetime Value (CLV) is a measurement
of how valuable a customer is to your company.
CLV is the total worth to a business of a customer
over the whole period (past and future) of their
relationship.
Data4sales implements customer segmentation
based on individual CLV. For this reason, we use
all available data as the basis of our CLV
calculation model to ensure the accuracy and
reliability of our predictions.
Lifetime Value
.6
Lifetime
Value
Product
Affinity
CX Score
RFM
Engagement
Escore
Customer
Profile
Similarity
to ICP

8. We built an actionable lifecycle-based
segmentation model that will allow your
marketing experts to know which customers it
makes sense to focus efforts and resources, based
on what they are (or may be) worth to your
business.
Lifecycle Segmentation
.7
HIGH
MEDIUM
POTENTIAL VALUE
LOW
HIGH
HISTORICAL
VALUE
MEDIUM
LOW
PROSPECTS
HIBERNATING
Recover
JUST PASSING
THROUGH
Business as Usual
FALSE STARTS
Business as Usual
AT RISK
Retain
NEED ATTENTION
Retain
JUST ARRIVED
Build Awareness
CHAMPIONS
Reward
LOYAL CUSTOMERS
Grow
PROMISING
Loyalize
BRAND LOYALISTS
Sell
Nurture
OCCASIONAL
BUYERS
CANT LOOSE THEM
Recover

9. Our machine-learning Personalization Engine
puts your data to work and provides the
opportunity for
your marketers to send highly contextualized
and relevant communications to specific
customers at the right place and time, and
through the right channel, engaging customers in
a way significantly, deepening existing
relationships and building new ones and
improving the customer experience.
Personalization
.8
PERSONALIZATION
ENGINE
Products
Channels
Measurement
Contents
Timing
Text Analytics
RECOMENDATIONS
CUSTOMERS
PREFERENCES
CUSTOMERS
EXPERIENCE
Business Rules
Behavioral
Data
Surveys
Attributes
Motivations

10. We provide all the insights your marketing team
needs to understand who your best customers
are and what they expect from your company. All
information generated by the Data4Sales platform
will be updated daily and will be available at
customer level through our API.
Your marketing team can use this data to
customize campaigns, personalize your
customers' online experience, update the CRM or
perform exploratory analysis, using our
visualization tools or through third-party tools
such as Tableau and Power BI.
Insights
.9
ATTRIBUTES
BEHAVIOR
PREDICTIONS
CUSTOMER
EXPERIENCE
CUSTOMER
INSIGHTS
Imported
Calculated
Motivations
Geolocation
Profile
CLV
Next Best Action
Next Best Offer
Preferences
Lookalike
Metrics
Moments
Buyer Status
RFM
Engagement Level
NPS
CES / CSAT
Ratings
Sentiment
Topics

11. We help companies of any size or industry to
drive customercentric transformation and
optimize customers value.
Our team of customer analytics experts is
constantly working on developing and optimizing
our predictive models so your team can focus on
what really matters, creating memorable
experiences for your customers.
By combining the power of machine learning
with the expertise of your marketing team, you'll
be able to create relevant, longterm relationships
with your best customers.
Contact us
comercial@data4sales.com
+598 92983-101
@data4sales
data4sales.com
Why
Data4Sales