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http://www.datamine.gr
Customer Analytics & Segmentationfor telecommunications
Techniques & Applications
George KrasadakisSeptember 2005
http://www.datamine.gr
Customer SegmentationOverview & DefinitionsTypes of SegmentationSegmentation ExamplesInput DescriptionThe Physical Customer ModelEffective Customer MetricsSample Segmentation schemesThe time dimensionTechnologies & I.T. InfrastructureSegmentation Lifecycle
Contents
http://www.datamine.gr
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 expertiseProfiling &
interpretation
The main objective of customer segmentation is to understand the customer base, and achieve sufficient customerinsight 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 & campaignsCustomer Satisfaction & Loyalty (Churn management)Efficient Consumer Risk ManagementProcess Automation & OptimizationEffective Performance Monitoring, Executive information & Decision support
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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
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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
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A simplified Example: 2 dimensions
Tenure (CLS)
Profitabilityor
Revenue Highly profitable,
‘New comers’
Non profitable,
‘New comers’
Highly profitable,
Loyal Customers
Non or Low profitable,
Loyal Customers
0
Attempt usage stimulationcampaign, 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 performingcustomers. Must be
analyzed for promising sub groups (age or
demographic profile along with variances in usage)
Limitations of the above oversimplified segmentation schemeNo consideration of significant dimensions, such as Payment Behavior (Consumer Credit Risk)Demographic, socioeconomic or lifestyle and usage information is missingUse of scores or ranks can significantly improve the schema and its interpretationIt is static, no time dimension or Transition Probabilities defined
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A simplified Example: 2 dimensions
Consumer Risk
Profitabilityor
Revenue Highly profitable, Low-Risk Customers
Low profitable, Low-Risk
Customers
Highly profitable, High-Risk Customers
Low profitable, High-Risk Customers
0
Attempt usagestimulation 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.grequire credit card as
payment method
Poor performing, High Risk customers: analyze
for understanding and modeling behaviors
Limitations of the oversimplified segmentation schemeNo consideration of significant dimensions, such as TenureDemographic, socioeconomic and usage information is missingUse of scores or ranks can significantly improve the schema
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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
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Dimensions & FiltersCustomer
-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 Satisfactionmetrics
Segmentation schemesMacro segmentation for management & decision support and performance evaluation purposesMicro segmentation schemes, campaign specific, for product development, up selling or cross-selling program design, for loyalty – churn management, marketing actions
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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, OccasionalService Sensitive, Price Sensitive, Balanced
Traffic DestinationLocal, long distance, international, competitors
Incoming/Outgoing Traffic BalancePassive, Active, Normal
VAS usageEntry 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 historyNew, Returning, Recycling, Multi-Contract
Contact StatisticsSystematic, Normal, Occasional
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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
Partia
l Sco
re
Partia
l Sco
re
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
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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 trafficBad payment behavior with frequent payment delays (suspensions, reactivations)
Could be in a collection state
Confusing, negative outcomefor the
Customer
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Efficient Customer Metrics
Billing & Payment StatisticsTotal amount Billed, Open Balance AnalysisBilling Statistics (Averages,Variability)Payment-related Statistics (Delays, Suspensions, Fraud History), Credit Score (payment behavior)Profitability or Revenue Rank ScoreAccount analysis (by status), Product & Services
Traffic analysisOutgoing 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
MetadataStatistically derived Scores, clusters and existing segmentationschemesMarketing 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
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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 segmentMarket 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.
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Customer metrics versus time
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)
(%) o
f pop
ulat
ion
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
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Customer metrics versus time
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
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Data Modeling technologiesDescriptive 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. infrastructureA ‘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
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I.T. Infrastructure
Flattened customer data structures
Reliable customer data with time dimension
Physical Customer,
Account & Contact,Customer Scores
Billing dataPayment behaviorSegmentation dataUtilization profile &
Aggregate Traffic patterns
StatisticalModeling
Billing & Provisioning Systems
Customer Profiling Account data
Services & tariffs
Billing & payment history
Customer Care, Operational CRM
Contact History,Complaints,
Activation Requests
REPORTINGdatamart
CRMdatamart
Reporting Tools OLAP
Customer Base KPIs monitoring
Customer Segmentation
System
Customer Viewer
Traffic DataCDR raw data,
QoS data
TRAFFIC processes
Operational CRM Platform
Marketing DataProducts & 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
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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 dataquality • 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 • analyzeperformance 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 transitionprobabilities • monitor the homogeneity of each segment
Close the Loop: collect response and performance information • assess segmentation synthesis - profiling
Performance Assessment
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22 Ethnikis Antistasis Avenue,15232 Chalandri, Athens, Greece
Tel (+30) 210.68.99.960Fax (+30) 210.68.99.968
[email protected]://www.datamine.gr
George KrasadakisCustomer Analytics Manager