campaign optimization / bi/ data mining

Campaign Optimization

Using Business Intelligence and Data Mining

George Krasadakis

March 2007

http://www.datamine.gr

Outline

Key concepts & definitionsA common language regarding campaigns, the main dimensions & metrics involved

The need for campaign optimizationThe typical campaign management lifecycle and the need for optimization

Designing the Target GroupData-driven approaches for target group definition – use of BI and Data mining techniques

Performance AnalysisAnalyze campaign response data, model customer responses, compile reports

Application within E-Business environmentsCampaign, recommendation, profiling and personalization


Key concepts & definitions

CampaignA 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)


The need for optimization

The ultimate goalEnable 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 roadmapDesign, 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


Campaign Management

System

Customer

database

Documents

& templates

Communication Channels

Just select and type

text. Use control

handle to adjust line

spacing.

Call Center

Email Server

Marketing UserCustomers

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

Target Group DesignLocate, profile and manage customers according to

composite business logic


Designing the target group

Using Segmentation schemeseffective 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


Designing the target group

The Physical Customer StructurePhysical 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 HistoryTime 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)


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

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


Designing the target group: CBE

Customer & ProductsAttributes enabling the

dynamic target group

definition

1

Dimensions & MeasuresEnabling custom views of your

customer base

2

Customer SampleRandom sample of Customers

for verification reasons

3

Customer ProfilingAnalysis of the resulting

customer set versus any

combination of attributes

4

Performance AnalysisBrowse, report and model customer responses


Campaign response analysis

A Measurement EnvironmentA 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 HistoryCustomer 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 HistoryCampaign 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 evaluationROI models, comparisons of expected results against actual, analysis versus initial statistical profiles of

the target group, all packed in standardized, well define reports



Campaign Analysis CubeAnalyze 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)



Campaigns – working listQuick or composite campaign search

functionality and the resulting

campaigns list. To be used as

navigation tool for exploring and

managing campaigns

1

Campaign ViewerA 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 AnalysisSpecialized 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



Customer base mapping according to generated profiles

100

75

50

25

0

Re

ve

nu

e R

an

k

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


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


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%)

Web AnalyticsCampaigns, recommendation and personalization for

the e-business


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)


Personalization: An overview

Portal UserBusiness Users

We

b s

ite

I.T.

Infrastructure

CMS DOC

Billing

User InteractionSession 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

submissionregistration 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

EngineReporting Engine

Personalized

OutputPersonalized content

(links, documents),

controlled functionality

4

5

Systematic Raw Data FeedRaw data describing key portal entities, traffic

data, content. Gathered systematically from

the ETL components for further processing,

analysis and modeling

Portal Personalization transactionPortal submits visitor's identification data. RE

retrieves metadata, compiles a

Recommendation’s List and forwards it to the

portal

Personalized DataRecommendations List as

served from RE

Business Users


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)

datamine ltd

Decision Support Systems

22 Ethnikis Antistasis ave

15232 Chalandri

Athens, Greece

T: (+30) 210 6899960

F: (+30) 210 6899968

[email protected]


George Krasadakis

Head of [email protected]

Contact information

http://www.datamine.gr/


mailto:[email protected]


mailto:[email protected]