Integration of campaign management and data mining at MTN South Africa

Marcelle Georgiev, MTN South AfricaMohan Namboodiri, SAS South Africa

1. Complex Environment2. The role of Predictive modelling3. The results of Predictive modelling – An update on

Prepaid Churn Prediction4. Taking models to market: The case for Automated

Campagining5. Selecting a tool6. Implementation and Methodology7. What to watch out for

Agenda

“MTN wants to continue its established market leadership in innovation by being the leader in relationship marketing in the South African market.”

Bruce Cockburn, General Manager,Centre of Excellence,MTN Marketing

MTN Business Objective

Environment

• “Dynamic”

• Fiercely Competitive

• 2nd Position Means More Pressure For Retention

• 3rd Operator Imminent

Retention Environment

• Managers by Business Sector

• Value banding

• Marketing mix

• Direct mail

• SMS

• e-mail

• Call-centre

Predictive Modelling• Started with contract base, but Prepaid

Cellular is now 80% of the base

• Virtually anonymous

• Churn is a rare but financially significant event in the higher value segments

• Value based direct marketing – outbound calls and SMS communication

• Because of the size and nature of the prepaid base, targetted marketing is essential –requires calling as mailing is impossible

Predictive Modelling of Prepaid Can Be Successful

Non-M odel Cam paigns

M odel Cam paigns

0.00% 2.20% 4.86% 10%W ors t A verage B es t A pprec iably B etter!

Churn Re duction

Measurement Culture

• Targetted marketing means careful monitoring of campaign effectiveness

• Comparisons performed between campaigns and against control groups

• Use of fallow groups

• All of this makes it possible to quantifybenefits of a campaign

Advanced Modelling meets Manual Campaigning

• Positive results have led to putting the model into “production” to guide retention efforts – we score the data monthly

• Most campaign selections were model-based but all subsequent tasks – response monitoring, reporting, creating feedback variables for the database, financial calculations – are MANUAL

• Most other selections are done ad-hoc

• Created a vision and a need: Build more models and spend less time on manual campaign administration – EMA/CMT !!!

CMT - The BIG picture

All OLAP, analysis, segmentation, profiling, tracking and measuring.

Roll-out asprogramme

IterateResponse/results feedback

Measure andtrack

Launch campaign

Sampling and Target groupselection

Plan & designdetailed campaignprocess

Formulateobjectives &measures

NPVAnalysis

Conceptualdesign

KnowledgeDiscovery(Score and add)

Source bestpractice

Mine CRM data

Identify Opportunity Plan overall programme design and roll-out Implement programmes and iterate

WIZARD CUSTOMER CARE

In-house developed system & further

customised for outbound contact support to

Campaigns – Scripts, etc.(Oracle on DEC Alpha

8400 Dec Unix)

Subscriber interaction systems e.g. Call centre,

Service

Centres,Internet,Workflow, etc.(Eppix & Wizard Cus Care & workflow linked to Rockwell

Telephony platform)

CRMData Mart(Subset of

DWH)

MTN Data Warehouse(SAS on DEC Alpha 8400

OPEN/VMS) (Has Oracle - DEC Unix as option)

SAS Data Miner(Churn prediction)

Subscriber response or interaction result (Renew contract, change usage behavior, etc.)Subscriber

1. Rockwell Switch (Oracle RDBMS on Sun Sparc Unix )2. Eppix Cus Care (Informix RDBMS - DEC Alpha Unix)3. Autocomms Workflow (Informix on DEC Alpha Unix)4. Wizard Cus Care (Oracle RDBMS – Dec Alpha Unix)

OLTP PRODUCTION SYSTEMSISIS/GSM Network Billing (PRO-ISAM DEC Alpha

Unix)Eppix GSM SP Billing (Informix DEC Alpha

Unix)Oracle ERP and other

Call lists

Campaign and contact flow administra tionand management

Contact/Interaction execution

Selecting a Tool• Required ease of use

• Required industry standard functionality –capability to automate across call centre, SMS, email and direct mail

• Required integration with Enterprise Miner modelling capability

• Required compatability with existing Customer Data Mart in SAS and flexibility for the future Enterprise Data Warehouse

• Shortlisted 2 Vendors from the Usual Suspects

The envelope please…

ChoseChose

Case Study: A Work in Progress

• Implementation in progress

• Implemetation through a structured methodology

• Cannot advise on Enterprise Miner integration in practice yet

• We can give some very practical advice on success factors in preparation and early phases

Copyright © 2000, SAS Institute Inc. All rights reserved.

Methodology

Pre-implementation:CMT - Readiness Plan

Improve skill sets (before training)Data 101Data Modelling Query DesignAccess physical example

Coaching course For technical team to be able to coach facilitation course

Pre-implementation:CMT - Readiness Plan

Data Modelling courseTechnical Team

Tool Specific trainingAll - part of the implementation plan

TerminologyCampaign specificTool specific

Pre-implementation:Team Walk-Through

The Way Forward

• Automated use of models

• Automated interactions with customers, via:

• eMail• SMS• Voicemail• Outbound calls• Mail shots

• Automated reporting

What to watch out for• Executive buy-in is essential

• Change Management from evaluation to implementation and beyond

• Who attended our change management launch?

• Global View: Marketing Management, Campaign Owners, Retention Analysts, Financial Analysts, Credit, Project Management, Communications Coordinator, IT, SAS

What to watch out for• Never underestimate issues around

business definitions

• Who owns the business definitions ?

• Nailing down basic business definitions.

• What/Who is the Customer ?

• What is contactable or mailable ?

• Documenting the rules

What to watch out for• Never underestimate design issues

around the Customer Data Mart

• Work requirements of campaign owners – reporting, subsetting

• Indexing and query performance

• Space, space, etc.

• Alignment (or future-fitting) with company-wide Data Warehouse initiatives