What is the price of bad customer data? Brussels, 15 th September 2009

Agenda Welcome Vincent van Hunnik – Chief Marketing Officer, Human Inference What’s the price of bad customer data? Gary Pill, Information Management Consultant, Accenture The price of bad customer data – some examples Jan Verrept, Account Manager Belgium, Human Inference Data Quality @ Essent - Inside the Data Fortress Mark Humphries, Data Manager, Essent … you want to know more on Accenture – Human Inference proposition references a live demo of Human Inference capabilities Sten Ebenau, Product Manager, Human Inference

Welcome

Vincent van Hunnik – Chief Marketing Officer, Human Inference

What’s the price of bad customer data?

Gary Pill, Information Management Consultant, Accenture

The price of bad customer data – some examples

Jan Verrept, Account Manager Belgium, Human Inference

Data Quality @ Essent - Inside the Data Fortress

Mark Humphries, Data Manager, Essent

… you want to know more on

Accenture – Human Inference proposition

references

a live demo of Human Inference capabilities

Sten Ebenau, Product Manager, Human Inference

Your data is valuable We keep it that way

Your data is valuable

We keep it that way

TELCO & UTILITIES BANKING INSURANCE PUBLISHERS OUR PARTNERS

LOGISTICS PUBLIC LEISURE SERVICES OTHER

The price of bad data quality – some examples Over time I collected real-life Belgian cases A few weeks before this session I have asked some customers, prospects and contacts if they could tell me about some of their experiences of bad customer data which I could share with you (anonymous) I will share some of their examples and observations

Over time I collected real-life Belgian cases

A few weeks before this session I have asked some customers, prospects and contacts if they could tell me about some of their experiences of bad customer data which I could share with you (anonymous)

I will share some of their examples and observations

The price of bad customer data Some examples

Case – Car distributor

The problem Postal office manager calls: “Do you want to send 329 Audi Magazines again to address xyz in B? Answer: “Normally we send only 1 copy per address maybe there is a mistake?” “Or is there a leasing company at that address?” Postal office manager: “No it’s a foundation for homeless people.” Car distributor calls competitors to check if they had cars registered on address xyz in B. Further investigation learned that homeless people received money from a criminal organisation to register a car (obtained in a non-official way) under their name. Since homeless have no home they gave the address of the foundation. Case – Car distributor

The problem

Postal office manager calls: “Do you want to send 329 Audi Magazines again to address xyz in B?

Answer: “Normally we send only 1 copy per address maybe there is a mistake?” “Or is there a leasing company at that address?”

Postal office manager: “No it’s a foundation for homeless people.”

Car distributor calls competitors to check if they had cars registered on address xyz in B.

Further investigation learned that homeless people received money from a criminal organisation to register a car (obtained in a non-official way) under their name. Since homeless have no home they gave the address of the foundation.

The cost 329 Audi Magazines x €5,5 x 5 mailings = €9.047 Extra work = 10 hours x €50/hour = €500 Total = €14.047 lost per mailing The solution Duplicate detection not only on name but more combinations -> one mail piece per address Case – Car distributor

The cost

329 Audi Magazines x €5,5 x 5 mailings = €9.047

Extra work = 10 hours x €50/hour = €500

Total = €14.047 lost per mailing

The solution

Duplicate detection not only on name but more combinations -> one mail piece per address

Case – Utility company

Migration Case - Utility company To program dedup queries = 20 man days € 20.000 2 x outsourced data cleansing € 35.000 staging 1 mio records prospects & customers 80% b2c 20% b2b load

1 st day operational Case - Utility company On time delivery! Operational excellence is great! 1 mio records prospects & customers 80% b2c 20% b2b new application

Case - Utility company 6% of records has changed because of: changes in names – Jean Dupont -> J. Dupont – Martin and/or changes in address – movers and/or changes in products Jean Dupont -> electricity Carine Martin -> gas 6 months operational To program dedup queries = 20 man days € 20.000 2 x outsourced data cleansing € 35.000 Database increased with +12.000 records of which 7.200 duplicates of which 2.800 are considered as new customer after 6 months the superfluous costs related to: marketing 2.800 x € 9 (mailings + welcome gift) € 25.200 billing/dunning 3.800 x € 8,3 (10 minutes) € 31.540 call center 3.800 x € 8,3 (10 minutes) € 31.540 Total € 143.280 1 mio records + 12.000 prospects & customers 80% b2c 20% b2b new application

1 mio records prospects & customers 80% b2c 20% b2b Case - Utility company new application Next project intensified portal traffic and portal services

Phonetic similarity Mateijsen Matheijsen Matheysen Mathijse Mathijsse Mathyse Mathyssen Matijssen Mattheijsen Mattheysen Matthijse Matthijsse Matthijszen Matthyssen Mattijsse Mattyssen Mateysen Matheijssen Matheyssen Mathijsen Mathijssen Mathysen Matijsen Matteijssen Mattheijssen Mattheyssen Matthijssen Matthysse Mattijsen Mattijssen Same sound, different writing

Intelligent matching Transport Dupont Dupont Logistique Distribution Dupont DuPont Expedition Dupont Logistics Dupont Distribution Dupont & Dupont Exp. Exp. & Transp. Dupont Du Pont Logistics & Transport Different sound, different writing, same company

Case – Large bank

prospects & customers b2c and b2b Buy 3 rd party data Case – Large bank Dedup check on First name + Last name + Address + Birth-date 3 rd party birth-date is limited to month and year because of high price When loading the day is set to “01” 3 rd party data 1 mio records Situation: entering customer data on retail level, duplicate check, birth-date is different (customer: “I am not born on the 1 st of June”) New customer is created. Result: around 1.000 duplicates/month created Cost: manual search & modifications over different systems & processes is 35 minutes per record € 25/duplicate duplicate marketing + welcome gifts € 10/duplicate cost/month = € 35 x 1.000 € 35.000 took 4 months or € 140.000 to start decreasing cost Situation: customers move, household names change, prospects move -> Customer data changes in reality, in 3 rd party database and in systems. Or not. load

prospects & customers b2c and b2b Buy 3 rd party data Case – Large bank load 3 rd party data 1 mio records Do not adapt your own processes to 3 rd party data provider Limit the use of 3 rd party data, get more info out of your existing data Measure, implement early warning systems Do not rely on same dedup rules

Case – Large bank

Create single customer view Case – Large bank One database had high quality of customer data When First name = Last name = Birth-date = Address >< then keep the address from the database with the highest quality Result: correspondence, certificates, bills, dunning did not arrive or arrived too late, insurance policies expired, call center overload, etc. For 90% - 95% this was ok For 5% - 10% not ok because an old address was chosen Cost: 100k’s but still calculating “ I could not help paying you late because your mail piece arrived late, because my name-address was not correct and I can prove that.” INSURANCE prospects & customers b2c and b2b BANK prospects & customers b2c and b2b view on golden record

The price of bad customer data Observations

Bad customer data hot spots Observations prospects & customers application intensified portal traffic and portal services 3 rd party data staging

The price of bad customer data – observations Measure data quality before migration The price of bad customer data is high but moreover it increases exponentially over # people, # systems and # processes Single customer view only possible with data quality firewall Do not adapt your own processes to 3 rd party data provider Limit the use of 3rd party data, get more info out of your existing data Cannot solve with queries, scripts, ETL or mathematical matching alone, but do not always rely on same dedup rules Measure, implement early warning systems We pay electronically after we received physically Observations

Measure data quality before migration

The price of bad customer data is high but moreover it increases exponentially over # people, # systems and # processes

Single customer view only possible with data quality firewall

Do not adapt your own processes to 3 rd party data provider

Limit the use of 3rd party data, get more info out of your existing data

Cannot solve with queries, scripts, ETL or mathematical matching alone, but do not always rely on same dedup rules

Measure, implement early warning systems

We pay electronically after we received physically

Presentation Mark Humphries

Accenture – Human Inference proposition

Proposition - Quick Win Assessment - Combining Accentures’ business knowledge and data quality consulting capabilities with the knowledge based customer data profiling and cleansing solutions of Human Inference provides customers with fast and prioritized insight in their data quality opportunities. Within a ten day pilot Accenture and Human Inference analyzes your current level of data quality, identify quick wins and provide further recommendations and prioritizations.

Combining Accentures’ business knowledge and data quality consulting capabilities with the knowledge based customer data profiling and cleansing solutions of Human Inference provides customers with fast and prioritized insight in their data quality opportunities.

Within a ten day pilot Accenture and Human Inference analyzes your current level of data quality, identify quick wins and provide further recommendations and prioritizations.

Quick Win Assessment - Approach - The Quick Win Assessment will focus on delivering a completed Data Quality Process and System analysis based on a three stage approach. Quick Win Assessment Implement Scope & Project plan Study Current DQ process & Data 1. Prepare Pre planning Key Tasks: Mobilize pilot & client team Define pilot scope, setup pilot environment Create high level pilot work plan Profile Sample Data Assess Process & Data Gaps Analyze Profiling Results 2. Analyze Key Tasks: Verify and Validate current data quality process, Evaluate data stewardship and governance Procure & Profile sample data using standard rules Interpret profiling results and generate technical report Outcomes: Document issues of process and data flows and gaps based on scope Perform sample data profiling using standard rules Analyze and document profiling results and reports Evaluate/ Recommend 3. Recommend Quick Wins Key Tasks: Determine quick wins Evaluate impact of best solution /scenario Document profiling report with findings and recommendations Outcomes: Quality data report Quick win summary Implementations options List of improvement project recommendations Final presentation

Key Tasks:

Mobilize pilot & client team

Define pilot scope, setup pilot environment

Create high level pilot work plan

Key Tasks:

Verify and Validate current data quality process, Evaluate data stewardship and governance

Procure & Profile sample data using standard rules

Interpret profiling results and generate technical report

Outcomes:

Document issues of process and data flows and gaps based on scope

Perform sample data profiling using standard rules

Analyze and document profiling results and reports

Key Tasks:

Determine quick wins

Evaluate impact of best solution /scenario

Document profiling report with findings and recommendations

Outcomes:

Quality data report

Quick win summary

Implementations options

List of improvement project recommendations

Final presentation

Special offer First two projects will be done at a 50% discount. Normal pilot price € 12.000,- (VAT excluded) Special offer price: € 6.000,- (VAT excluded) Conditions: With regard to the sample data one database Provision of date according to prescribed format Signed agreement before November 30th 2009 General terms & conditions of Accenture / Human Inference applies.

First two projects will be done at a 50% discount.

Normal pilot price

€ 12.000,- (VAT excluded)

Special offer price:

€ 6.000,- (VAT excluded)

Conditions:

With regard to the sample data

one database

Provision of date according to prescribed format

Signed agreement before November 30th 2009

General terms & conditions of Accenture / Human Inference applies.

Summary Data Quality issues are omnipresent Solving data quality issues requires a solid approach and hard work Learn from the experts Costs are often hidden but can increase dramatically Solutions require a combined approach of people, systems and processes … let’s talk now

Data Quality issues are omnipresent

Solving data quality issues requires a solid approach and hard work

Learn from the experts

Costs are often hidden but can increase dramatically

Solutions require a combined approach of people, systems and processes