How can we optimize the campaign-related processes? How Data mining can help? How can we design target groups?

1. Campaign
Optimization
Using Business Intelligence and Data Mining
George Krasadakis
March 2007

2. http://www.datamine.gr
Outline
Key concepts & definitions
A common language regarding campaigns, the main dimensions & metrics involved
The need for campaign optimization
The typical campaign management lifecycle and the need for optimization
Designing the Target Group
Data-driven approaches for target group definition – use of BI and Data mining techniques
Performance Analysis
Analyze campaign response data, model customer responses, compile reports
Application within E-Business environments
Campaign, recommendation, profiling and personalization

3. http://www.datamine.gr
Key concepts & definitions
Campaign
A set of systematic promotional activities (multiple offers, scenarios & channels) against a well
defined target group (advanced business logic for accurate customer selection) within a controlled
environment (infrastructure for response gathering, reporting, analysis and modeling).
Campaign Management
Infrastructure & processes enabling efficient design (Target group definition - customer selection,
eligibility criteria, profile analysis), smooth execution (integration with communication channels) and
effective response analysis (response gathering, analysis, reporting and modelling).
Data Mining & BI (Business Intelligence)
−BI is based on several technologies & scientific areas such as information technology, multidimensional
data exploration technologies (OLAP), data mining, statistical modeling, text mining, visualization
techniques
−BI enables companies to explore, analyze, and model large amounts of complex data
−BI can greatly enhance Campaign Management processes from Design (TG definition), Execution
(efficient communication planning), to response analysis & modelling (exploratory and/ or with data
mining)

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The need for optimization
The ultimate goal
Enable the right treatment on the right customer at the right time through the right channel. This
further enables customer understanding (needs, preferences, usage & buying patterns) enabling
customer response analysis and modeling
The roadmap
Design, implement and automate solid campaign management processes. This will provide flexibility (in
handling customers, products and promotions), reliability (regarding execution, response gathering) and
robust measurement & analysis processes - functions. This will enable a systematic monitoring and
analysis framework to support decisioning in general
The business value
− Winning the performance game (On-time Schedule Indicator, Cost Per Activity)
− Customer insight - usage patterns, profiles and customer base trends may reveal significant
cross-selling or up-selling opportunities
− Assessment of marketing actions, special offers or campaigns can be assessed in detail using
customer responses and changes in usage patterns: The Closed Loop Marketing
− Retain (ensure) or increase Customer Satisfaction levels

5. http://www.datamine.gr
Campaign Management
System
Customer
database
Documents
& templates
Communication Channels
Campaigning: lifecycle
Target Group Definition
The MKT user interacts
with CMS in order to
explore the customer
base and design the
most effective target
group
1
Customer Profile Analysis
CMS retrieves customer
information in order to
provide sufficient
segmentation capabilities to
the MKT user
2
Target Group Release for
contact
List of customers –Target
Group- as defined from the
MKT user, and after
applying the selected,
predefined exclusion logic
3
Customer Communication
The offer assigned to the
campaign is being
communicated to the
customer according to the
predefined script or template
4
Customer Response
Customer responses are being
forwarded into the system for
campaign assessment,
monitoring and optimization
5
Campaign Analytics
Campaign performance
statistics, customer
demographics, campaign
lifecycle information, call center
performance reports and
analytics
6
Campaign performance
Assessment
Sufficient input for better
campaign design, customer
behavior modeling. Insight for
process monitoring, KPIs for
assessment studies
7

6. Target Group Design
Locate, profile and manage customers according to
composite business logic

7. http://www.datamine.gr
Designing the target group
Using Segmentation schemes
effective schemes for categorizing and organizing meaningful groups of customers
Customer Profiling
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
The process
− the target group definition process is an iterative procedure aiming in compilation of a well
structured set of customers with certain degree of homogeneity regarding a set of attributes.
− Involves business knowledge, ideas & creative thinking as well as data-driven concepts, facts
and modelling activities
− Requires effective exploratory analysis and in-depth understanding of the customer base
− Can be optimized using advanced modelling techniques and data mining algorithms

8. http://www.datamine.gr
Designing the target group
The Physical Customer Structure
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 efficiently scored in the top level
Physical Customer
Usage History Usage metadata
Customer Care
& Contact History
Application, ordering &
payment History
Time Related Patterns
Statistical &
Data Mining Modeling
Analytics,
segmentation & profiling
Benefits
− A complete picture of the customer, in all dimensions (profitability, risk, loyalty, satisfaction etc)
− Elimination of contradictive communication attempts (bonus due to product A ‘performance’
while in collections procedure due to product B payment habits)

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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
Designing the target group: input
Target Group Design
Involves 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 / average duration
-ARPU / average revenue
-Specific Traffic metrics (services
usage – destination analysis,
incoming vs outgoing etc)
-Churn Behavior
-Campaign Responses
-Customer Satisfaction
metrics
Metadata
− 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

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Designing the target group: CBE
Customer & Products
Attributes enabling the
dynamic target group
definition
1
Dimensions & Measures
Enabling custom views of your
customer base
2
Customer Sample
Random sample of
Customers for verification
reasons
3
Customer Profiling
Analysis of the resulting
customer set versus any
combination of attributes
4

11. Performance Analysis
Browse, report and model customer responses

12. http://www.datamine.gr
Campaign response analysis
A Measurement Environment
A set of metrics, KPIs and predefined reports, enabling an instant picture of each specific campaign.
Reports also include suitable comparisons with ‘global constants’ such as group averages, baselines and
predefined limits thus enabling comparative performance analysis of a campaign.
Customer Contact History
Customer campaign memberships and response history (memberships, contacts, feedback, offers &
promotions attempted) should be maintained and further processed in order to generate related
customer metadata. This ‘customer communication history’ should also be available to other systems as
well, thus extending the knowledge regarding customers, their needs and preferences.
Detailed Campaign History
Campaign History & Reporting provide rich history of the full lifecycle of each specific campaign.
Information on campaign execution events can be used as markers against the evolution of the customer
base (reporting before and prior the campaign) for trends, indirect results or pattern identification.
Formal evaluation
ROI models, comparisons of expected results against actual, analysis versus initial statistical profiles of
the target group, all packed in standardized, well define reports

13. http://www.datamine.gr
Campaign response analysis
Campaign Analysis Cube
Analyze campaign response data in any meaningful way. Start with exploratory analysis, browsing the
results in order to see the shape of the response set. A powerful, high-performance environment for
browsing customer response data. Basic dimensions:
1. Customer segment: enables the projection of the target group of your campaign (and any subset
as well) against the available segmentation schemes
2. Customer Profile type: similarly the customer set can be analyzed in terms of well-known &
understood customer profiles
3. Channel: the channels available/ selected for the specific campaign. Enables analysis of
performance (for instance response rate against channel used and in combination with other
dimensions)
4. Offer: the actual promotion, offering to the customer
5. Contact Time: the time zone (day and time – according to schemes in use)
6. Timing: the time positioning of the communication event in terms of customer critical dates (e.g.
forthcoming contract expiration or renewal process)
7. Script: the actual communication ‘dialogue’ – how the offering has been proposed to the
customer
8. Agent profile: Characteristics of the agent involved (demographics, experience, seniority,
specialization)

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Campaign response analysis
Campaigns – working list
Quick or composite campaign search
functionality and the resulting
campaigns list. To be used as
navigation tool for exploring and
managing campaigns
1
Campaign Viewer
A set of different views against the
selected campaign (from sophisticated
analytics to execution oriented reports)
provide instant & accurate information on
the aspect of interest
2
Cohort Analysis
Specialized computations &
Charts provide direct insight
to campaign performance
factors. Quick tabulation
along with export utilities in
a standardized output
ensures optimum results
with minimum effort
3

15. http://www.datamine.gr
Campaign response analysis
Customer base mapping according to generated profiles
100
75
50
25
0
RevenueRank
Tenure Rank
0 25 50 75 100
Customer Profiles projected against by revenue & tenure
Response A
Response B
Response C
Response D
Response E
Response categories
Categorized customer
responses
Customer projection
Projected on a two
dimensional space
(revenue-tenure)
ranks, and colored by
response category for
the selected profile

16. http://www.datamine.gr
Applying Data Mining
Data Mining
refers to statistical and machine learning algorithms, applied in large amounts of data, aiming in
identifying hidden relations and patterns. Popular data mining models include decision trees,
clustering & association rules.
− Association rules can identify correlations between pages/content not directly or obviously
connected. May lead to previously unknown – not obvious- associations between sets of users with
specific interests thus enabling more efficient treatment of customer
− Clustering is a set of statistical algorithms aiming in grouping together items (customers) that present
at least a certain degree of homogeneity relevant to specific measures. In contrast, the ‘distance’
between groups is maximized, thus forming a physical ‘segmentation scheme’ for further processing or
event direct use.
− Classification refers to a family of algorithms that ‘learn’ to assign items to pre-defined (existing)
groups.
− Sequential Analysis is a methodology for unveiling patterns of co-occurrence

17. http://www.datamine.gr
Campaign response modeling
Sample rules as derived from Decision trees:
CreditLimit >= 15150,007 and ProfessionClass = 'Medical staff' > (positive=91%, negative=9%)
CreditLimit >= 15150,007 and ProfessionClass not = 'Medical staff'
and Residence not = 'ΘΕΣΣΑΛΟΝΙΚΗ - ΠΡΟΑΣΤΙΑ' > (positive=82%, negative=18%)

18. Web Analytics
Campaigns, recommendation and personalization for
the e-business

19. http://www.datamine.gr
Personalization: Definitions, Needs & Business Value
Personalization
− consists of mechanisms used to adapt a web-site in terms of information / content served or
services/ functionality enabled, based on user navigational patterns, their profiles and their
preferences.
− improves customer experience, resulting in more efficient actions through an ‘intelligent web
site’ able to adapt according to user’s profile. May dramatically improve customer (user)
satisfaction & Loyalty, usage boost, cross-selling & up-selling opportunities
Personalization within typical e-commerce environments can take the following forms:
− Recommendation. Determine suitable material (content, links, listings etc) for the specific user
and the specific session. The ‘suitability’ of the material is computed from data mining algorithms
which process large volumes of data and identify ‘hidden’ relationships.
− Localization. User’s physical geography (as registered), or retrieved (connection based) can be
used and ‘appropriate’ content is displayed
− Targeted Advertising. ads that are expected to interest the user most (based on data mining –
profiling & segmentation models)
− Email Campaigns. Personalized messages to highly targeted users (according to their
profiles/interests & segmentation schemes)

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Personalization: An overview
Website
I.T.
Infrastructure
CMS DOC
Billing
User Interaction
Session data that describe
typical user interaction with the
portal/ web site. Includes
requests, user registration and
preference data, navigational
information
1
2 3
User Request/ data
submission
registration and
preference data,
navigational information
Web Analytics Infrastructure Data mining
models
ETL
Data gathering,
Cleansing, preparation &
standardization,
data mining specific
transformations
Analytics Database
Customer profiles,
content structure &
Metadata, processed traffic
information
Recommendations
Engine
Reporting Engine
Personalized
Output
Personalized content
(links, documents),
controlled functionality
4
5
Systematic Raw Data Feed
Raw data describing key portal entities,
traffic data, content. Gathered
systematically from the ETL components for
further processing, analysis and modeling
Portal Personalization transaction
Portal submits visitor's identification data.
RE retrieves metadata, compiles a
Recommendation’s List and forwards it to
the portal
Personalized Data
Recommendations List
as served from RE

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Personalization: Data Requirements
User data includes information that can be used to define profiles of the physical user (individual
and/or company) such as:
− Demographics: gender, age, socioeconomic data, profession, education level, company
attributes etc
− Interests & preferences: communication settings, interests against specific content categories or
functionality offered (as submitted by the user through registration process)
− User experience: experience in the domains of interest, roles etc
Usage data consists of the set of data that describe in detail every single user-portal interaction.
A usually complex, large volume data set including log file information, session specific data,
content structure.
Environmental data refers to information describing the technological infrastructure enabling
each user to access services and content offered (hardware, software, operating system)
‘Portal data’ refers to information providing structural representation, content definitions, relations,
actions, processes (registration, applications, service activation, inquiries etc)

22. datamine ltd
Decision Support Systems
22 Ethnikis Antistasis ave
15232 Chalandri
Athens, Greece
T: (+30) 210 6899960
F: (+30) 210 6899968
info@datamine.gr
http://www.datamine.gr
George Krasadakis
Head of engineering
g.krasadakis@datamine.gr
Contact information