customer analytics & segmentation

http://www.datamine.gr

Customer Analytics & Segmentationfor telecommunications

Techniques & Applications

George KrasadakisSeptember 2005


Customer SegmentationOverview & DefinitionsTypes of SegmentationSegmentation ExamplesInput DescriptionThe Physical Customer ModelEffective Customer MetricsSample Segmentation schemesThe time dimensionTechnologies & I.T. InfrastructureSegmentation 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 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


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)

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


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


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 & 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


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


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


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


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


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.



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



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 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


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


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


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