Campaign optimization using Business Intelligence and Data Mining

Campaign Optimization

Using Business Intelligence and Data Mining

George Krasadakis

March 2007

http://www.datamine.gr

OutlineKey concepts & definitionsA 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 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 & definitionsCampaignA 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 ManagementInfrastructure & 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 techniquesBI enables companies to explore, analyze, and model large amounts of complex dataBI 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 optimizationThe 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 User Customers

Campaigning: lifecycle

Target Group DefinitionThe MKT user interacts with CMS in order to explore the customer base and design the most effective target group

1

Customer Profile AnalysisCMS retrieves customer information in order to provide sufficient segmentation capabilities to the MKT user

2

Target Group Release for contactList of customers –Target Group- as defined from the MKT user, and after applying the selected, predefined exclusion logic

3

Customer CommunicationThe offer assigned to the campaign is being communicated to the customer according to the predefined script or template

4

Customer ResponseCustomer responses are being forwarded into the system for campaign assessment, monitoring and optimization

5

Campaign AnalyticsCampaign performance statistics, customer demographics, campaign lifecycle information, call center performance reports and analytics

6

Campaign performance AssessmentSufficient 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 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)


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 Satisfactionmetrics

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 analysisA 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 response analysisCampaign 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 customer5. 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

customer8. Agent profile: Characteristics of the agent involved (demographics, experience, seniority,

specialization)


Campaign response analysis

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


Campaign response analysisCustomer base mapping according to generated profiles

100

75

50

25

0

Rev

enue

Ran

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 categoriesCategorized customer responses

Customer projectionProjected on a two dimensional space (revenue-tenure) ranks, and colored by response category for the selected profile


Applying Data MiningData 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 modelingSample 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 ValuePersonalization

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

Web

site

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 Engine Reporting 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 RequirementsUser 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 ltdDecision Support Systems

22 Ethnikis Antistasis ave15232 Chalandri

Athens, Greece

T: (+30) 210 6899960F: (+30) 210 6899968

[email protected] http://www.datamine.gr

George KrasadakisHead of [email protected]

Contact information

mailto:[email protected]

http://www.datamine.gr/

mailto:[email protected]

http://www.datamine.gr/

Date post:	13-Apr-2017
Category:	Business
Upload:	george-krasadakis
View:	87 times
Download:	1 times