1. Customer Segmentation
May 2011
White Paper
«Which segmentation to connect with your customers? »
MANAGEMENT CONSULTING
Your CONTACTS
Alexandre GANGJI, Partner Benelux
alexandre.gangji@weave.eu
+32 (0)477 597 398
Hub weave :
www.weave.eu
1

2. Executive summary
As market dynamics are changing, companies are looking for new segmentation to improve the way they
attract, manage and retain their customers. This re-segmentation exercises can be used to support strategic
objectives, such as identifying new market potential, or tactically, to improve retention, ROMI and cross-
selling. However, moving from traditional segmentation methods to more advanced methods require
marketing organizations to invest beyond their basic capabilities
Within the advanced methods, the value-based segmentation allows for prioritization of the customers
portfolio according to their economic value (past or future). One of the most effective value-based
approaches is Customer Lifetime Value (CLV). While CLV can be seen as complex, its pragmatic application
yields most benefits and the use of customer lifetime value as a marketing metric tends to place greater
emphasis on customer service and long-term customer satisfaction, rather than on maximizing short-term
sales
A multi-layer segmentation model leverages the particular benefits of each segmentation method by crossing
basic segments and value-based approaches. This results in operational segments allowing more efficient
targeting actions (acquisition, costs reductions, loyalty, etc.) and connecting with your customers
Segmentation projects are typically approached in seven steps, from defining objectives of the segmentation
exercise to assessing its effectiveness. Key success factors are to implement a standard data collection
process, to respect segment definition rules and to ensure continuous improvement of the segmentation
model
2

3. Effective segmentations allow companies to allocate scarce
resources where they'll deliver the highest payoff
Strategic Tactical
• Opportunity identification • Prospecting / lead generation
• Prioritization • Sales force allocation
• Market investments / divestments • Channel strategy
Domains of
application • Positioning • Communications programming
• Product / portfolio development • Pricing
• Market driving (vs. customer focused) • Compensation
• New identified market potential • ROMI
Potential impacts • Share of new products in total portfolio • Conversion rate
• Margin by segment • Cross-selling and up-selling
3

4. Advanced segmentation methods bring customer
strategy and Return On marketing Investments to the
next level
Level of adequacy
with customer Long-term
needs and benefit
Methods Description behaviors potential
Volumetric Quantitative analysis of historical prescription or purchased Low Low
volumes
Geographic Separates customers into different geographical units, such Low Low
as countries, states, regions, cities
Socio-demographic Separates customers on the basis of age, gender, social Low Low
class, and other factors
Needs-based Divides customers according to needs which are being Medium Medium
fulfilled by the products or services
Behavioral Based on identifying customer behavior characteristics that Medium Medium
help to understand why customers behave they way they do
Value-based Based on the present and future profitability of a customer High Medium
(for instance CLV)
Multilayer approach Approach crossing different segmentation methods and High High
dimensions (in particular a value-based segmentation with
other effective segmentation methods)
4

5. Advanced segmentation methods allow companies to connect
with their customers and ensure long term profitability
Basic Advanced
Long term customer
Segmentation Segmentation
profitability
1 2
Level of
marketing
intelligence
1 Although basic segmentation methods can be useful for companies with basic marketing capabilities,
they have proven limited sales and marketing efficiency
Behavioral and value-based methods focus on understanding customer decisions, behaviors, and financial
2
value. They have proven by many companies as very prevalent and effective segmentation methods
5

6. Executive summary
As market dynamics are changing, companies are looking for new segmentation to improve the way they
attract, manage and retain their customers. This re-segmentation exercises can be used to support strategic
objectives, such as identifying new market potential, or tactically, to improve retention, ROMI and cross-
selling. However, moving from traditional segmentation methods to more advanced methods require
marketing organizations to invest beyond their basic capabilities
Within the advanced methods, the value-based segmentation allows for prioritization of the customers
portfolio according to their economic value (past or future). One of the most effective value-based
approaches is Customer Lifetime Value (CLV). While CLV can be seen as complex, its pragmatic application
yields most benefits and the use of customer lifetime value as a marketing metric tends to place greater
emphasis on customer service and long-term customer satisfaction, rather than on maximizing short-term
sales
A multi-layer segmentation model leverages the particular benefits of each segmentation method by crossing
basic segments and value-based approaches. This results in operational segments allowing more efficient
targeting actions (acquisition, costs reductions, loyalty, etc.) and connecting with your customers
Segmentation projects are typically approached in seven steps, from defining objectives of the segmentation
exercise to assessing its effectiveness. Key success factors are to implement a standard data collection
process, to respect segment definition rules and to ensure continuous improvement of the segmentation
model
6

7. Deep dive on value-based segmentation
Characteristics Benefits
• Most readily available metric Low
Revenue-based
• But often poorly correlated to real profitability
• Includes both actual and potential customer revenue
Market • Can be estimated using overall spend in the category and / or potential
opportunity / growth in revenue over time
revenue potential
• To be used when share growth is a leading objective
• Accounts for the cost to serve the customer (allocates costs such as S&M
acquisition costs, service / support, R&D, etc.)
Current / past
customer • Very useful in industries where the cost to serve varies significantly by customer
profitability
• Mostly feasible for companies with a small number of customers or with very
advanced customer management systems
Customer
• Recognizes the customer as a corporate asset
Lifetime Value
• Encompasses the holistic value a customer provides (influence on others)
Customer
Influence • Useful in industries where a few customers have a disproportionate share
of influence on others’ buying decisions (ex: Pharmaceutical companies) High
Source: « Marketers Must Understand Customer Value to Make
Segmentation Pay », Scott Wilkerson, Sept 2009,
7
www.managesmarter.com

8. Introducing Customer Lifetime Value, a powerful value-
based approach
Definition Areas of impact
• The Customer Lifetime Value (CLV) is the discounted
value of the future profits that will be generated by an
• There is significant added value of a CLV-based
individual customer
segmentation, at various levels
• As these future profits are uncertain, predictive – At campaign level: CLV can help you
models have to be developed. These models are capture cross-selling and cannibalisation
based on data and use analytics techniques effects
• Traditionally, companies / marketing managers focus – At client level: CLV can rank our
on analysing historical data (past sales, campaign segments in terms of profitability
returns, etc), although very significant gains could be – More generally, a CLV approach can help
produced through future customer behaviour you optimise marketing campaigns
forecasting
• To do so, predictive models have to be
• CLV can also increase predictability in terms of value
developed
and ranking, and be used aside traditional metrics to
• E.g., a customer that will generate €120, €80, €30, enhance customer insight
€50 and €10 of profits in the next five years, will have
a CLV of €238,11 if the discount factor is 10%. This
value is unfortunately unobservable for now, and a
predictive model needs to be developed
8

9. Case Study Areas impacted by a CLV analysis
Example Impacts
1 • From a traditional product-centric point-of-
view: The return of the campaign is
computed taking only into account the
return generated by the Car Insurance CLV captures cross selling and
Added value at
product cannibalization effects
campaign level
• From a CLV point of view: The return of the
campaign takes into account the effect on
the current accounts and the savings
accounts
Segment A Segment B Segment C
Car 600€ for 2 600€ for 2
300€ for 1 years
Insurance years years
10.000€ for 2
Current years 10.000€ for 2
Account years
(cross-selling)
Savings -10.000€
Account (cannibalism)
9

10. Case Study Areas impacted by a CLV analysis
Example Impacts
2 • From a product-centric point of view:
Segment A = Segment B = Segment C
• From a CLV point of view: Segment A < CLV can rank segments in terms of
Added value a Segment B < Segment C profitability
client level
Segment A Segment B Segment C
Car 600€ for 2 600€ for 2
300€ for 1 years
Insurance years years
10.000€ for 2
Current years 10.000€ for 2
Account years
(cross-selling)
Savings -10.000€
Account (cannibalism)
10

11. Case Study Areas impacted by a CLV analysis
Example Impacts
3 • The CLV change allow to optimize the
company’s profits by targeting the segment
A for campaign 2 and the segments B & C
Added value for for campaign 1 in priority CLV allows to optimize marketing
marketing campaigns
campaigns • Note that with our current tools, we wouldn’t
be able to determine which campaign is
most appropriate for the segments B and C
Segment A Segment B Segment C Consider that campaign 1 and 2
occur more or less at the same
Campaign 1 0€ 250 € 200 € time. We don’t want to contact
the same customers twice.
Which segment should be
targeted for which campaign?
Campaign 2 25 € 50 € 55 €
11

12. The CLV allows to make more profitable decisions than
Case Study
the propensity-to-buy
ILLUSTRATION
CLV vs. Propensity to
Buy
In this real case example, we
compare, for 500 customers,
the scoring of the propensity
to buy model (who will buy the
product?),
…with the ranking of the CLV
change (who will be profitable
if I contact him/her?)
12

13. Case Study Comparing historic profitability against CLV
ILLUSTRATION
1 Past information available 2 ‘Future’ information available for 3 Analyze correlation
for the project the project assessment between predicted
(1st quarter 2009) (2nd quarter 2009) and actual data
Past
Past Customer
Past Customer
Customer profits
Profits
Quarterly CPM Profits • Value: 47%
200901 • Value in Euro • Ranking: 19%
• Ranking
Actual
(based on the agreed scope)
Better Targeting
• Customer value in Euro
• Ranking of the customer
CLV CLV
Past activity, • Value in Euro • Value: 95%
CLV
customer • Ranking • Ranking: 81%
characteristics, etc.
CLV can reliably rank customer segments in terms of profitability
13

14. Case Study Pragmatic application of CLV yields most benefits
• Focus on profitable products only and strategic areas
Product scope
• Focus on multiple iteration campaigns only
(in order to identify and isolate the effect)
Campaign scope
CLV analysis
• Define a realistic horizon from 3 to 5 years with focus on
Product scope relative/marginal
profitability and
ranking
• Exclude fix costs
Profitability scope
• Focus on profitability per segment (vs. deep dive per client)
• Keep model simple (per month, per quarter vs. per day)
Modeling
14

15. Case Study Customer Lifetime Value modelling
Pi , j ,t
CLVi  t 0, j 1
h, J
(1  d )t
Where
h is the horizon of the prediction: how far we want to go in the future
J is the number of products/business lines considered
Pi,j,t is the profit generated by the customer i at time t because of the usage of the product j
d is the discount rate
Usually
h is taken via a business rule
J is a tradeoff between implementability and realism
Pi,j,t is predicted using statistical models
d is selected in agreement between the management, finance and the accounting
department
15

16. How to model the future profitability and activity of the
Case Study
customers?
Approach Pro’s and con’s
• Create “cells” or groups of customers • Pro: very simple and flexible. Good for long
based on the recency, the frequency and term predictions
RFM models the monetary value of their prior
• Con: many segments needed if used for
purchases, the model is then estimated
individual customer valuation
using decision trees or Markov chains
• Assume an underlying stochastic model • Pro: statistically elegant, extensively discussed
Probability (e.g. the Pareto/NBD model) in the academic literature
models • Con: PhD needed…
• Hazard functions • Pro: very flexible and extensively used in the
industry (but not for CLV modeling)
Econometric • Survival analysis
models • Con: work only for contractual setting (when the
end of the contract is observed)
• Vector Autoregressive (VAR) model • Pro: very flexible and easy, powerful for short
Persistence term predictions, can take into account many
models types of drivers
• Con: computationally expensive
Source: topology described in Gupta and colleagues 2006
in the special issue on CLV of the Journal of Service
16
Research

17. Markov Chains approach: an example in the retail
Case Study
banking industry
ILLUSTRATION
Year 2009
Potential Sleeping High Active Mature Potential
client client potential client client churner Lost
Potential
90% 0% 5% 3% 2% 0% 0%
client
Sleeping
0% 90% 4% 1% 0% 0% 5%
client
High-
0% 5% 60% 15% 5% 5% 10%
potential
Year 2008
Active
0% 10% 3% 70% 12% 3% 2%
client
Mature
0% 8% 1% 5% 70% 11% 5%
client
Potential
0% 15% 0% 7% 8% 30% 40%
churner
Lost 20% 0% 0% 0% 0% 0% 80%
17

18. Vector autoregressive models: an example in the retail
Case Study
banking industry
The model for the customer activity is
Yi ,t  f (Yi ,t 1 , X i ,U i ,t ),
with:
 Yi ,1,t 
• Yi,t the vector of the activity of customer i in the product categories at future time t,  
– is a function (regression) of Yi ,t   ... ,
Y 
• Yi,t-1 the matrix of the activity of customer i in the product categories at time before t,
 i , J ,t 
– The activity at time t is a function of the activity at time t-1, t-2, …., t-T.
– Used for modeling: loyalty, attrition.  Yi ,1,t T ... Yi ,1,t 1 
 
– The activity at time t in the product category j is a function of the activity at time t- Yi ,t 1   ... ... ... .
1, t-2, …., t-T in the OTHER product category 1,…,J. Y 
– Used for modeling: cross-selling, halo effect, cannibalism.  i , J ,t T ... Yi , J ,t 1 
• Xi a vector of characteristic of customer i,
– The activity at time t is a function of the age,…,etc.
– Used for modeling: customer heterogeneity, customer segmentation.
• Ui,t a vector of actionable drivers (marketing actions),
– The activity at time t is a function of the marketing campaign m implemented at
time t-1,…t-T.
– Used for: marketing campaign optimization, target identification, etc.
18

19. Case Study Application to a retail banker settings
• The Customer Lifetime Value is the discounted value of the future profits that will be generated by an individual
customer
• The CLV of a customer i is a function of the profit, Pi,j,t, he/she will generate in the future t via the product j
J T Pi , j ,t
CLVi   .
j 1 t 1 (1  r ) t
• As Pi,j,t is unknown, a prediction model needs to be build:
– The future profits will be derived from the predicted future activity Yi,j,t, as Pi,j,t = sj x Yi,j,t, where sj is
the spread of product j and Yi,j,t is the outstanding amount on customer’s i account at time t.
– The future activity of the customers Yi,j,t will be predicted using an adaptation of a Vector Auto-
Regressive (VAR) model.
19

20. From Data Sources to CLV-based Strategy: Example
Case Study
from Belgian Universal Bank
Input Model Output ILLUSTRATION
Transactional information Customer Lifetime Value
Customer past transactions, Customer Activity Customer One measure in Euro per
purchases, etc. Model Profitability customer
Model
• Identification of the CLV-based score
Customer characteristics activity drivers Profit as a function Identification of the customers for
Socio-demographics information: • Customer activity of the customer a marketing action using the CLV
age, address, etc. forecast activity
CLV-based tool
Identification of the optimal
Price/Cost structure Customer Lifetime Value marketing actions using the CLV
Information on the relationship Model
between the customers’ activity
and the profits CLV Estimation based on the CLV documentation
discounted future profits Summary statistics
Expert knowledge
Information from the experts Management presentations
Findings summary and
Recommendations
Data Cleaning Selection Data mining &
modeling
Databases Data Warehouse Task Relevant Data Pattern Evaluation
20

21. Case Study What you need for starting a value-based project
• Data and information needed
– You need to know who your customers are
– You need to know what is your CURRENT customer profitability
– You need to know what your customers did in the past
• Maturity level needed
– Typically, you already implemented customer analytics type of project:
o Propensity to buy
o Attrition modeling
– The profitability of your customers is a key question
• Type of business
– Typically, with many customers, and a lot of past transactional data available
– Example of industries: Retail Banking, Telecommunication, Pharmaceuticals, Retailers
21

22. Case Study Lessons learned from past projects
• The risks
– Politics, conservatism, etc.
– Heterogeneity of your customer base: adapt your segmentation accordingly
– Endless arguments on the price and cost structure: use marginal revenues!
– Mature products vs. newly developed products: discard new products!
– Profitability approach might be conflicting with existing sales incentives (e.g. volume-
based)
• The key success factors:
– It’s a business project, not an IT one! The project has to be led by the marketing
department
 Have someone from marketing leading the project
 Knowing that the effort will be 80% IT
– Be pragmatic: use the 80/20 rule
– Be realistic: it is impossible to predict what will happen in 20 years with a 90%
accuracy
– The Key Question is “HOW WILL THE VALUE BASED SEGMENTATION WILL BE
ACTIONED?”
22

23. Case Study Conclusions
 Value-based segmentation allows to allocate resources where they'll deliver the highest
payoff
– Marketing actions can be implemented in a more optimal way
– Customers can be targeted more profitably
 Value-based segmentation models and CLV Models can be estimated using standard
procedures
– We model the future activity of the customers using an adapted Vector Auto-Regressive Model
– Markov-chains are an efficient alternative when an aggregated level (segment) is needed
 By taking into account the relevant drivers of customer activity, accurate and reliable
predictions are made:
– Owing to the model simplicity, the estimates can easily be interpreted
– For prediction, we achieved a correlation of 95% between predicted and actual over the first
three months
23

24. Executive summary
As market dynamics are changing, companies are looking for new segmentation to improve the way they
attract, manage and retain their customers. This re-segmentation exercises can be used to support strategic
objectives, such as identifying new market potential, or tactically, to improve retention, ROMI and cross-
selling. However, moving from traditional segmentation methods to more advanced methods require
marketing organizations to invest beyond their basic capabilities
Within the advanced methods, the value-based segmentation allows for prioritization of the customers
portfolio according to their economic value (past or future). One of the most effective value-based
approaches is Customer Lifetime Value (CLV). While CLV can be seen as complex, its pragmatic application
yields most benefits and the use of customer lifetime value as a marketing metric tends to place greater
emphasis on customer service and long-term customer satisfaction, rather than on maximizing short-term
sales
A multi-layer segmentation model leverages the particular benefits of each segmentation method by crossing
basic segments and value-based approaches. This results in operational segments allowing more efficient
targeting actions (acquisition, costs reductions, loyalty, etc.) and connecting with your customers
Segmentation projects are typically approached in seven steps, from defining objectives of the segmentation
exercise to assessing its effectiveness. Key success factors are to implement a standard data collection
process, to respect segment definition rules and to ensure continuous improvement of the segmentation
model
24

25. We recommend a multi-layer approach crossing value-based
segmentations with other segmentations methods
Prioritization of your customers Leveraging categorization
portfolio according to their data from traditional
economic value segmentation methods
A
Value-based approach B
• Annual turnover Basic
Segments Segments
de marché
• Sourcing / production segmentation
costs Value
Valeur XL L M S • Behavior-based
• Network costs • Consumption level
• Segment management ++ • Expectations level
cost • Usage
• Lifetime of a customer + • …
• …
-
A + B
Operational segments
• Same economic value
• Similar profiles
•… Obtaining granular and
actionable segments
25

26. Crossing basic segments with value-based approaches
allows to define operational and actionable segments
Multi-layer components Strategic segments Sub-segments Impact
Behavior-based Value-based • A global and
Geographic A.1 A.2
Group A consistent go-to-
A.3 market strategy
Socio-demographic within and across
product lines
B.1
Behavioral Group B B.2
B.3 • Integration of client’s
long-term potential
Value-based
C.1 C.2 C.3
… Group C • Aggregation of
C.4 C.5 customer value
• Valuation • Market and product planning • A ‘ROMI’ approach
Purpose • Prioritization • Campaign planning and execution
• Resource allocation • Marketing communications planning
• Channel assignments • Granular view of target markets
and motivators
• Easy to understand and to
Benefits use • Actionable: basis for offer
development, campaign
• Sustainable segments to
targeting and market/brand
achieve a differentiated
positioning
position
26

27. Executive summary
As market dynamics are changing, companies are looking for new segmentation to improve the way they
attract, manage and retain their customers. This re-segmentation exercises can be used to support strategic
objectives, such as identifying new market potential, or tactically, to improve retention, ROMI and cross-
selling. However, moving from traditional segmentation methods to more advanced methods require
marketing organizations to invest beyond their basic capabilities
Within the advanced methods, the value-based segmentation allows for prioritization of the customers
portfolio according to their economic value (past or future). One of the most effective value-based
approaches is Customer Lifetime Value (CLV). While CLV can be seen as complex, its pragmatic application
yields most benefits and the use of customer lifetime value as a marketing metric tends to place greater
emphasis on customer service and long-term customer satisfaction, rather than on maximizing short-term
sales
A multi-layer segmentation model leverages the particular benefits of each segmentation method by crossing
basic segments and value-based approaches. This results in operational segments allowing more efficient
targeting actions (acquisition, costs reductions, loyalty, etc.) and connecting with your customers
Segmentation projects are typically approached in seven steps, from defining objectives of the segmentation
exercise to assessing its effectiveness. Key success factors are to implement a standard data collection
process, to respect segment definition rules and to ensure continuous improvement of the segmentation
model
27

28. The typical process to an effective segmentation : a 7-
steps approach
• Which business line(s) and customers are
concerned, what is the time frame of the study ?
• What is the depth and the final objective of the
• Define your strategic objectives and the 1 study (strategic or tactical applications) ?
associated key performance indicators 2
Define business
• Determine the relevant segmentation Determine
objectives
assessment methods based on those objectives segmentation • Identify the data source(s) to be used
scope (internal data collection for mature
segments, customer survey for new
7 customers, etc.)
Assess 3 • Identify audience and determine survey
• Review and follow your key segmentation and / or data collection tools, content (for
performance indicators (market
Collect qualitative or quantitative data), and
share evolution, sales growth, new effectiveness data administration plan (through reps, etc)
customer base, etc) • Ensure / Check data quality and reliability
• Assess segmentation update needs
6 • Analyse data
4
• Summarise customer responses into
Go-to-market Analyse the predictive models of segment and customer
• Develop strategies to serve individual segmentation behaviour
segments
5
Identify • Review current market and product
• Develop product segmentation based on offerings
value to customer and value to business segment
profiles • Define current customer base by segment
• Develop recommendations for pricing dimensions (products consumed,
actions based on segment specific geography, etc) and define “clusters” of
purchase behaviour and buying process • Characterise each segment by determining customers with similar needs for products
customers key differences and similarities (cluster consumption
analysis)
• Determine each segment size and purchasing
power / profile
28

29. Key success factors
Organize Respect Customize Develop
Data collection & 5 golden rules of Segmentation strategy Continuous improvement
segmentation process segment definition approach
Standardize common To be useful, the • “De-average” the market Segmentation is a
processes to achieve segments you continuous, rather than
• Assess the potential of
synergies and build an identified should be: linear, process:
each segment (size,
organizational
growth, uniformity,
structure to manage it • Markets and
competition, etc)
segments are
• Determine data • Homogeneous within, • Select the best dynamic and unstable
requirements heterogeneous segments to serve : over time
across according to their Conduct
• Collect data from profitability or your Segmentation
adequate sources • Measurable competitive advantage
• Manage and analyze • Identifiable (align segment
Implement
data characteristics with your Strategies
• Actionable capabilities and
Measure
Effectiveness
• Substantial competencies)
• Define adequate
business models to
serve them profitably
29

30. Standard approach to segmentation design and roll-out
Our approach ensures understanding and buy-in from all stakeholders impacted by a new
segmentation
Actionable
segmentatio
3. Roll-out & n approach
Scoping 1. Pilot 2. Go / No Go
follow-up to retain and
acquire new
customers
Double track testing Pilot results Communication of results
• One sample with traditional • Communicate and discuss • Ensure visibility of results at
marketing campaigns the comparison of value- group level (marketing
• One sample with value- based approach to previous boards...)
based segmentation traditional approaches • Ensure understanding and
A posteriori analysis • Demonstrate value-based buy-in of all stakeholders
• Previous campaigns to be benefits through business • Refine and extend first
analyzed case approach segmentation approach to
Initial analysis of results Go overall project scope
• Compare value-based • Results and benefits are in • Assess impact on
approach to previous line with expectations – organization
traditional approaches proceed further • Share regular feedback/follow
Collaboration and No Go up and assessment of value-
communication with • Results and/or benefits are based segmentation
clients teams not in line with expectations deployment with entities
– stop the project • Collaboration and
communication with client
teams
30

31. Sample of references
• CLV model definition and approach
Retail banking Launch of a pilot to implement
• Pilot phase
reference value based segmentation
• Results analysis and recommendations
• Qualitative assessment of internal and external data
Definition of operational
• Analysis of customer’s expectations
segmentation to enhance
• Definition of operational segmentation
sales performance
• Definition of action plan
• Analysis of agent’s expectations
Definition of operational
• Analysis of customers' expectations
segmentation of GDF Suez
• Identification of segmentation axis
network
• Analysis of partners' segmentation
• Definition of operational segmentation
Definition of operational
• Definition of a customer relationship policy for each segment
segmentation to enhance
• Definition of needs for the new CRM software
customer value
• Definition of product offer processes
Definition of multi-channel • Definition of multi-channel customer relationship policy for after sales operations for
customer relationship policy each segment (Grand voyageur, Seniors, 12-25, Escapade…)
• Definition of macro-segmentation (4 segments)
Definition of macro-
• Definition of a customer relationship policy for each segment for acquisition and
segmentation and customer
relationship policy retention
• Deployment of new customer relationship policy
Definition of segmentation • Definition of operational segmentation
and customer relationship • Development of new services to improve relationships with insured, health
policy professionals and employers
31