BUSINESS INTELLIGENCE OVERVIEW & APPLICATIONS

1. BUSINESS INTELLIGENCE
OVERVIEW & APPLICATIONS

2. http://www.datamine.gr
Definitions
The technology that enables companies to explore, analyze, and model large amounts of complex data.
Consists of statistical modeling, data mining and multidimensional data exploration technologies (OLAP).
Business Intelligence (BI) is normally built on one or more well defined data marts. BI models could also feed the
data sources (DW) with metadata.
Application of BI on customer data leads to Customer Intelligence or Customer Analytics
Must be thought as an integrated process of every CRM solution (thus defining Analytical CRM)
BI is based on several technologies & scientific areas such as information technology, OLAP, data mining,
statistical modeling, text mining, visualization techniques

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BI, Overview of Applications
Decision Making processes: well defined customer segments and KPIs can be analyzed versus time and in
combination to several customer & product attributes: changes in customer base (quantitative and/or qualitative),
revenue and profitability monitored in detail. What if scenarios with ROI models, on marketing, CRM or other actions.
CRM & Customer analytics: Infrastructure that provides overall assessment on each single customer: profitability,
loyalty, credit risk, usage patterns. Traffic information modeled and analyzed versus time along with customer profiles
enable churn management, monitoring & prediction or Credit Risk assessment.
Customer Loyalty: Business logic that encapsulates customer insight in order to build long lasting customer
relationships: the right offer to the right customer through the right channel can help maintain high levels of Customer
satisfaction. More accurate measurement of customer satisfaction is also possible through BI techniques (directly
through targeted e-surveys or indirectly from QoS figures), regular stratified random sampling providing feedback to data
warehouse or marketing database.
Campaign Management: Customer selection, eligibility criteria, Target group definitions and profile analysis, campaign
execution automation processes can be optimized using BI infrastructure.
Marketing & Sales: Customer usage patterns, profiles and customer base trends may reveal significant cross-selling
or up-selling opportunities. Marketing functions can greatly benefit from BI in designing new products, performing what if
scenarios or closed group studies. Performance of marketing actions, special offers or campaigns can be assessed in
details using customer responses and changes in usage patterns: The Closed Loop Marketing

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Business Intelligence, Architecture
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
Manager
Customer
Viewer
Traffic Data
Raw server log
data-clickstream,
Browsing patterns
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
DATA WAREHOUSE
Sales Automations
DATA PROVIDERS DATA WAREHOUSE - ANALYTICS DSS AREA - DATA CONSUMERS

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Business Intelligence, Applications
Data Modeling
Customer Metrics
CRM data mart
Customer Analytics
Front End Apps
Data understanding and basic statistical analysis (descriptive) • Identify
critical entities, data quality problems, limitations & special cases • Collect &
organize reliable information from every business function of the
enterprise • Design data cleansing & validation procedures, flat data
structures, synchronization mechanisms • physical customer
Analyze data • build statistical models • design customer metrics, extend
data structures, incorporate logic into synchronization mechanisms •
customer base evolution & cluster analysis • profitability, consumer credit
risk, churn rates, profiles, tenure, customer value, expected value
Data structures, combining customer related entities and customer metrics -
scores • Also known as ‘Marketing Database’ • Flat data structures,
containing hundreds of customer attributes versus time: behavioral data,
demographics, payment & contact information for a series of time frames
enabling modeling of change in customer data • Could be outside DW
and/or Multidimensional
Advanced models (statistical - data mining) on customer profiles &
transactional data producing probabilities for certain events,
homogenous clusters, behavioral patterns • Predictive components
Integration with operational CRM system • Establish systematic data flows
from/to CRM, Call center, corporate site or any other customer touch point •
Development of additional front-end applications for management &
decision making purposes.

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Typical Customer Metrics
Billing & Payment Statistics
Total amount Billed – Amount due
Billing Statistics (Averages,Variability)
Statistical Trends on bill payment
Credit Score (payment behavior)
Profitability or Revenue Rank Score
#accounts (by status), Product & Services, segments
Traffic analysis
Email usage: IN vs OUT
Email usage: distinct email addresses along with
frequency analysis - dependency
Email usage: metrics on volume of data, files, message
size, file types transferred
Certain Services usage patterns (for instance categories
along with frequency analysis
Distribution of sites – pages visited: Distinct Number,
Frequency & average time spent)
Distribution of daily traffic on predefined set of sites or
categories (on-line shops, news, entertainment,
educational, academic, scientific etc)
Time of Day distribution
Day of the week distribution
Quality of Service indicators
Bandwidth, daily, monthly, statistics, trends
Customer Care or Sales Contact History
Frequency of Calls - Requests
Distribution by Service, Reason, Type, Priority
Metadata
Statistically derived Scores, clusters and
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

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Typical Customer Dimensions & Measures
Systematic, normal, occasional user (based on frequency/duration of connections)
Professional, academic, fun (based on sites visited-content preferred)
Service sensitive, price sensitive
Power, normal, entry level user
Demographics, customer type (business-consumer, traffic based)
Average Revenue Per month, expected yearly revenue
VAS usage, mailbox or other services usage
Tenure
Use of e-commerce - On-line transactions
Seasonality indexes
Statistically derived clusters (homogenous groups pf customers)
In a GSM environment
Calls versus SMS usage
Incoming versus Outgoing Calls
Small number of long duration calls versus large number of small duration calls
MSISDN dependency index, based on weighted scores of distinct IN/OUT MSISDNS

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Additional Applications
Advanced OLAP reporting, definition of cubes that allow multidimensional views of customer data
Monitor & analyze customer base evolution, monitor time series of critical KPIs, define customer base health
indexes
CRM program development, monitoring & optimization (closed loop marketing)
Customer satisfaction measurement & monitoring
Design & develop customer Loyalty procedures
Development of micro & macro segmentation schemes
Identify cross selling & up selling opportunities
Define, Monitor & predict Churn
Enhance campaign management procedures, measure effectiveness, calculate ROI
Study bad payment behavior, develop credit scoring models
Build optimized rules and policies for identifying fraudulent cases
Flexible target group definitions, possibility for stratified random sampling for further customer
Research – survey studies.
Marketing activities (promotions, offers, campaigns) assessment capabilities
Fraud detection

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Additional Applications
MARKETING
USER
CUSTOMER BASE
SEGMENTATION
& CAMPAIGN
MANAGEMENT SYSTEM
CRM
DATAMART
CRM / CALL CENTER
SYSTEM / EMAIL SERVER
CUSTOMER &
CAMPAIGN
DATA
CUSTOMER
PROMOTION-
OFFER
CUSTOMER
RESPONSE
DATA
PERFORMANCE EVALUATION & ROI
ANALYSIS
CUSTOMER
RESPONSE
DATA
Closed loop marketing: gather
customer responses to DW
Performance evaluation - ROI
Control – Placebo group design
TARGET GROUP DEF
PROFILE & STATISTICS
ANALYTICS & RESPONSE
TARGET GROUP SPECS

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Campaign management
Analyze data,
define target group
Define Campaign,
special properties
& Objectives
Release
Campaign, manage
Smooth execution
Finalize campaign,
analyze data,
calculate ROI
Define business needs, objectives of the campaign
Basic description of the target group, the time frame, the channels to be used,
the budget to be allocated, human resources needed or special I.T. infrastructure
Evaluation criteria: Definition of the criteria to be applied in order to assess campaign efficiency (such as expected
responses, expected revenue or usage increase)
Decide on use control & placebo procedures in order to measure campaign success (split the target group in to
two random samples)
Decide on campaign customer contact scripts, follow up procedures

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Campaign management
Analyze data,
define target group
Define Campaign,
special properties
& Objectives
Release
Campaign, manage
Smooth execution
Finalize campaign,
analyze data,
calculate ROI
Descriptive analysis of data based on predefined metrics (such as ARPU, tenure, usage patterns &
categorizations, cluster memberships, demographics)
Analyze resulting target group versus other characteristics – metrics (not necessarily used as selection criteria)
to verify the normality of the set of customers.
Customer Eligibility checks: Apart from suitable customer profile (according a specific
target group definition) a given customer must meet specific eligibility criteria before
communicating the campaign (e.g. check of open balance or amount due or fraud indicators
– for post paid services)
Additional eligibility checks for recent customer requests, offers, contacts or other promotional activities (for
exclusion or extra handling)

12. http://www.datamine.gr
Campaign management
Analyze data,
define target group
Define Campaign,
special properties
& Objectives
Release
Campaign, manage
Smooth execution
Finalize campaign,
analyze data,
calculate ROI
Release the campaign, make eligible customer dataset available to call center (CTI) and/or email or other server
depending on communication channels assigned to this campaign
Monitor campaign execution progress on regular basis, collect overall progress data and make adjustments if
necessary (from script enhancement to human recourses allocation)
Monitor follow up actions for customers that have accepted the offer-proposal, ensure smooth completion of the
process (POS, Corporate site, Customer Care).

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Campaign management
Analyze data,
define target group
Define Campaign,
special properties
& Objectives
Release
Campaign, manage
Smooth execution
Finalize campaign,
analyze data,
calculate ROI
The closed loop marketing: Collect all resulting data to the initial data source, correlate and analyze campaign.
Customer contact history within this campaign (whatever the response – if any) becomes part of the overall
customer (contact) history record and available for future analysis, modeling & reporting.
The same dataset is available as history of the specific campaign and of the customer base. The campaign
execution event marks the evolution of the customer base (comparative reporting before and prior the campaign)
for trends or pattern identification
Compile ROI models, compare expected results with actual, analyze versus initial statistical profiles of the target
group, interpret the results

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22 Ethnikis Antistasis Avenue,
15232 Chalandri,
Athens, Greece
George.Krasadakis@datamine.gr
info@datamine.gr
http://www.datamine.gr