Customer Analytics & Segmentation

http://www.datamine.gr
Customer Analytics & Segmentation
for telecommunications
Techniques & Applications
George Krasadakis
September 2005

Customer Segmentation
Overview & Definitions
Types of Segmentation
Segmentation Examples
Input Description
The Physical Customer Model
Effective Customer Metrics
Sample Segmentation schemes
The time dimension
Technologies & I.T. Infrastructure
Segmentation Lifecycle
Contents

Overview
Customer Segmentation is the process of splitting a customer database into distinct, meaningful, homogenous groups
based on a specific methodology
Customer
database
Goals &
objectives
Analysis &
Segmentation
Statistical models,
marketing
expertise
Profiling &
interpretation
The main objective of customer segmentation is to understand the customer base, and achieve sufficient customer
insight that will enable the right treatment on the right set of customers at the right time…through the right
channel
Efficient use of customer segmentation infrastructure & techniques is expected to result in:
Competitive advantages through flexible, targeted marketing actions & campaigns
Customer Satisfaction & Loyalty (Churn management)
Efficient Consumer Risk Management
Process Automation & Optimization
Effective Performance Monitoring, Executive information & Decision support

Definitions
Customer Segmentation: the process of developing effective schemes for categorizing and organizing
meaningful groups of customers
Macro-Segmentation targets in schemes that are simple, easy to understand, in order to become a common
corporate language regarding the customer base
Micro-Segmentation defines rather complex schemes, with shorter lifecycle and large number of variables and
filtering criteria, to be used by analysts or marketing experts. Supports decision making, marketing campaigns,
monitoring & performance studies
Customer Segmentation can be Market Driven in order to capture specific market attributes (consumer vs large
accounts), or Data Driven in order to capture actual structures or patterns based on customer characteristics and
behaviors
Customer Profiling is the process of analyzing the elements (customers) of each segment in order to generalize,
describe or name this set of customers based on common characteristics. It is the process of understanding and
labeling a set of customers
Business Intelligence is the set of technologies that enable companies to explore, analyze, and model large
amounts of complex data. Consists of statistical modeling, data mining and multidimensional data exploration
technologies - OLAP

Customer Segmentation: Types & Objectives
The goals for segmenting the customer base can be either strategic, decision supportive in nature (executive
information) or pure marketing-oriented for specific campaigns or promotional activities
At a macro level, the main objective is to understand the customer base, be able to present its synthesis using
meaningful groups of customers, monitor and understand change over time, to support critical strategies and functions
such as CRM, Loyalty programs, product development
At a micro level, to support specific campaigns, commercial policies, cross-selling & up-selling activities, analyze
and manage churn & Loyalty
Customer Segmentation can be further divided in the following groups:
Structural: ‘natural’ segments that are very basic and result from the nature of the business. Geographical, product
or commercial based segments (consumer or large accounts)
Categorical: Based on ‘physical’ customer characteristics such as gender or age
Behavioral: Based on indexes or scores that capture customer behavior in several dimensions

A simplified Example: 2 dimensions
Tenure (CLS)
Profitability
or
Revenue Highly
profitable,
‘New comers’
Non
profitable,
‘New comers’
Highly
profitable,
Loyal
Customers
Non or Low
profitable,
Loyal
Customers
0
Attempt usage stimulation
campaign, using further
micro segmentation
schemes
Good Customers that must
be retained: Add to
Loyalty program
The best set of
customers. Must be
treated differently through
all available customer
touch points (POS to CC)
Poor performing
customers. Must be
analyzed for promising sub
groups (age or
demographic profile along
with variances in usage)
Limitations of the above oversimplified segmentation scheme
No consideration of significant dimensions, such as Payment Behavior (Consumer Credit Risk)
Demographic, socioeconomic or lifestyle and usage information is missing
Use of scores or ranks can significantly improve the schema and its interpretation
It is static, no time dimension or Transition Probabilities defined

A simplified Example: 2 dimensions
Consumer Risk
Profitability
or
Revenue Highly
profitable,
Low-Risk
Customers
Low profitable,
Low-Risk
Customers
Highly
profitable,
High-Risk
Customers
Low profitable,
High-Risk
Customers
0
Attempt usage
stimulation campaign, use
further micro segmentation
schemes
Best Customers - must be
retained: Add to Loyalty
program
High revenue generation
but bad-payers. Must be
treated accordingly e.g
require credit card as
payment method
Poor performing, High
Risk customers: analyze
for understanding and
modeling behaviors
Limitations of the oversimplified segmentation scheme
No consideration of significant dimensions, such as Tenure
Demographic, socioeconomic and usage information is missing
Use of scores or ranks can significantly improve the schema

Input Description
The input should be sufficient in order to describe…
Overall customer picture, based on summary figures (using weighting techniques): tenure, average revenue,
aggregated AMOU, account analysis, activation requests – applications, total Revenue Ranking, Risk Assessment
Utilization - how the subscriber uses each service (traffic data), indexes, correlations
Spending & Payment behavior, including consumer risk assessment
…enabling analysis at several levels:
Physical Customer Level: demographics, socioeconomic data, aggregates & scores
Account & Product Level: listing along with specific properties, Services & usage patterns, processed traffic data,
Maintenance behavior & Contact History
Seasonal Patterns, trends, time dimension

Dimensions & Filters
Customer
-Risk Class
-Revenue Class
-Socio -Economic data
-Demographics
-Location data (GI)
-Tenure (CLS)
-Traffic Patterns
-Contact Patterns
-Prior Classifications
Product - Services
-Accounts, status & types
-Services & Tariffs
-other properties
Input Description
Customer Segmentation is -by definition- multidimensional: must involve all the important aspects of each customer:
risk, tenure, profitability, or Customer value must be combined in order to explain or optimize a set of metrics or
specific behaviors
Measures
-total revenue
-Balance by type (source)
-frequencies
-’recent’ statistics
-’lifetime’ statistics
-AMOU
-ARPU
-Specific Traffic metrics (services
usage – destination analysis,
incoming vs outgoing etc)
-Churn Behavior
-Campaign Responses
-Customer Satisfaction
metrics
Segmentation schemes
Macro segmentation for
management & decision support
and performance evaluation
purposes
Micro segmentation schemes,
campaign specific, for product
development, up selling or cross-
selling program design, for
loyalty – churn management,
marketing actions

Composite input for advanced Segmentation
Powerful Segmentation schemes can be designed based on combination of market knowledge, concepts, and extensive
statistical or data mining modeling. Dimensions and measures such as:
Voice usage (Frequency, duration - variance of duration)
Systematic, Normal, Occasional
Service Sensitive, Price Sensitive, Balanced
Traffic Destination
Local, long distance, international, competitors
Incoming/Outgoing Traffic Balance
Passive, Active, Normal
VAS usage
Entry Level, Experienced, Power users
Traffic Density Analysis (scores of distinct IN/OUT MSISDNS)
MSISDN dependency levels
SMS versus Voice Balance (Incoming/Outgoing)
Heavy SMS, Heavy Voice
Activation history
New, Returning, Recycling, Multi-Contract
Contact Statistics
Systematic, Normal, Occasional

The Physical Customer Model
Physical Customer
Account #1
Line #1
Line #n
Invoicing, Payment
Traffic patterns
Traffic patterns
Demographics, customer
history, ratings, memberships
Account #2
Line #1
Line #n
Invoicing, Payment
Traffic Patterns
Traffic Patterns Scoring
Engine
Score, statistics,
weight factors
Score, statistics,
weight factors
Weight
Models
Overall scores
PartialScore
PartialScore
Physical Customer Identification is a critical point in customer segmentation & insight:
A physical customer may have several accounts with contradictive behavior regarding usage or payment. The physical
customer (a) must be correctly identified and (b) must be scored in the top level in an efficient way

The need for an Objective Customer Assessment
Physical Customer
Account #1
Line #1
Account #2
Line #1
A Physical customer can have several accounts of contradictive behavior. The concept of Primary Account and suitable
weighting mechanisms can efficiently address this complexity through an objective scoring at the top level
A very good account:
Tenure rank: top 10%
Revenue rank: top 20%
Credit Risk: bellow 10%
Could participate in a Loyalty
program
A bad account:
No traffic
Bad payment behavior with
frequent payment delays
(suspensions, reactivations)
Could be in a collection state
Confusing,
negative
outcome
for the
Customer

Efficient Customer Metrics
Billing & Payment Statistics
Total amount Billed, Open Balance Analysis
Billing Statistics (Averages,Variability)
Payment-related Statistics (Delays, Suspensions, Fraud History), Credit
Score (payment behavior)
Profitability or Revenue Rank Score
Account analysis (by status), Product & Services
Traffic analysis
Outgoing Calls / Duration versus SMS
Incoming Calls / Duration versus SMS
Most Frequent Destination Number (MFN)
Operator Significance Indexes
Distinct Number of IN/OUT MSISDNS
Call Duration distribution
Time of Day distribution
Day of the week distribution
Variability & Trend of average Call duration
Operator (Destination) distribution (IN & OUT traffic)
Cell distribution (GIS)
Distinct Number of Cells used (Mobility)
Data Calls frequency - duration
Special Services - frequency – duration
Customer Care Calls, Frequencies & Summaries by Service,
reason
Metadata
Statistically derived Scores, clusters and existing segmentation
schemes
Marketing Research data, customer satisfaction surveys, on-line
customer surveys, customer interaction data (CRM campaigns,
Loyalty program memberships & usage, special offers)
Micro-Macro segmentation, clustering memberships, control-placebo
group memberships

Customer metrics versus time
Modeling Customer Metrics on time scale is a challenging task due to:
Complex, Seasonality patterns, segment depended, cycling behaviors, different life cycles for each segment
Market Trends, competition & significant changes (e.g. number portability)
Complex environment (services, tariffs, multiple accounts for each customer, contract versus prepaid markets)
datamine’s approach in modeling change is based on capturing the complete picture of each
customer at certain (predefined) moments of its lifecycle along with detailed history per customer:
Picture of the customer on 6th and 10th month of it’s life (key metrics on traffic, averages on billing and payment, risk
scores, rank) in order to capture key metrics in a mature state and also prior the critical first contract expiration.
Running averages, comparable with the above, yearly averages along with variance and variation coefficients
Trend measures, and seasonal components on frequent time intervals
datamine’s approach is based on a flexible infrastructure that maintains sufficient historical information using intelligent
techniques (scores, aggregates and/or random sampling on the actual transactional data) thus providing the capability
of reproducing the state of the customer base and each single customer for any given time point, resulting in
powerful reporting capabilities and customer base monitoring / comparative functionality.

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
CLS (months)
(%)ofpopulation
REMAINING(%) VOL(%) NOVOL(%)
Measure Key Customer metrics on 5th to 6th
month
Measure Key Customer metrics on 10th
month, apply segmentation and contact
valuable customers for upgrade
Study the synthesis of the remaining
customers and compare with the initial
population

Allows dynamic report generation of the style:
Select the top 70% of the customers (in terms of revenue) that have exactly one active account, running the 8th to 10th
month, having credit risk below 20%, and outgoing traffic more than 80% to competitors …….. and built a campaign
targeting in both customer satisfaction and word-of-mouth effects
Or
Select top 30% of the customers with more than one active account, with less than 40% credit risk, that have reduced their
traffic or revenue more than 40% in the last x months….. and try specific usage stimulation campaigns or perform
random sampling to identify the satisfaction levels
Similarly
Select top 30% of the customers with more than 30% of their outgoing traffic to prepaid, with less than 40% credit risk, that
have used MMS service more than xx times in the last x months….. and try the effect of offering free web access or
other hi-tech services in competitive pricing

Data Modeling technologies
Descriptive Statistics (exploratory data analysis): cross tabulation analysis, using combination of filtering criteria,
OLAP tools, advanced visualization – graphics techniques
Statistical Modeling: univariate & multivariate statistical techniques, cluster analysis, scoring models, combination
of statistical techniques
Data Mining techniques: specialized algorithms such as Decision trees or Neural Networks
I.T. infrastructure
A ‘mature’ Data Warehouse, providing reliable, ‘clean’ customer information, from the top level (the physical
customer) to Call Detail (CDR) and Contact History level
Statistical and/or Data Mining Systems, any of commercial product such as SPSS Clementine, SAS Enterprise
Miner or Microsoft SQL Server 2005 Business Intelligence Studio
Specialized OLAP - like systems with sufficient list management functionality and segmentation deployment
procedures
Technologies & I.T. Infrastructure

I.T. Infrastructure
Flattened customer
data structures
Reliable customer data
with time dimension
Physical
Customer,
Account & Contact,
Customer Scores
Billing data
Payment behavior
Segmentation data
Utilization profile &
Aggregate Traffic patterns
Statistical
Modeling
Billing &
Provisioning Systems
Customer Profiling
Account data
Services & tariffs
Billing &
payment history
Customer Care,
Operational CRM
Contact History,
Complaints,
Activation Requests
REPORTING
datamart
CRM
datamart
Reporting Tools
OLAP
Customer Base
KPIs monitoring
Customer
Segmentation
System
Customer
Viewer
Traffic Data
CDR raw data,
QoS data
TRAFFIC
processes
Operational
CRM Platform
Marketing Data
Products & services
properties,
Campaigns, Micro& Macro
segmentation schemes
ETL
processes
Data cleansing,
Transformation to ‘flat’ data
structures
Descriptive statistics, traffic
patterns
Statistical models, churn prediction, credit scoring, fraud
cases, segment-cluster-campaign memberships
MARKETING DATABASE
Sales Automations
DATA PROVIDERS DATA WAREHOUSE - ANALYTICS DSS AREA - DATA CONSUMERS

Segmentation lifecycle
Goals & Objectives
Customer Segmentation
Profiling & Interpretation
Business Applications
Define Business Objectives: segmentation can be sales driven, product driven,
profitability or service positioning driven • Set the basis of the analysis (time frame,
subset of customers) • Built the working team
Review data requirements & examine availability • collect, analyze data & assess data
quality • perform preliminary data analysis cleanse data • Select segmentation
techniques (predefined or statistical) • Begin Segmentation • Analyze data • build
statistical models • (re) design customer metrics • perform segmentation
Interpret segments • understand the typical customer within each segment • analyze
performance indicators for each segment examine segment behavior versus time
(customer base synthesis)
Apply the derived segmentation schemes to support specific business needs • Monitor
the customer base evolution in terms of segments • measure segment transition
probabilities • monitor the homogeneity of each segment
Close the Loop: collect response and performance information • assess segmentation
synthesis - profiling
Performance Assessment

22 Ethnikis Antistasis Avenue,
15232 Chalandri,
Athens, Greece
Tel (+30) 210.68.99.960
Fax (+30) 210.68.99.968
g.krasadakis@datamine.gr
George Krasadakis
Customer Analytics Manager

Customer Analytics & Segmentation

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