What is the price of bad customer data?Brussels, 15th 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 examplesJan Verrept, Account Manager Belgium, Human Inference

• Data Quality @ Essent - Inside the Data FortressMark Humphries, Data Manager, Essent

• … you want to know more on Accenture – Human Inference propositionreferencesa live demo of Human Inference capabilities

Sten Ebenau, Product Manager, Human Inference

Your data is valuableWe 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

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 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

Case – Utility company

staging

Migration

Case - Utility company

To program dedup queries = 20 man days € 20.000

2 x outsourced data cleansing € 35.000

1 mio recordsprospects & customers

80% b2c20% b2b

load

1st day operational

Case - Utility company

1 mio recordsprospects & customers

80% b2c20% b2b

new application

On time delivery!

Operational excellence is great!

Case - Utility company

1 mio records + 12.000prospects & customers

80% b2c20% b2b

new application

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) € 31.540Total € 143.280

1 mio recordsprospects & customers

80% b2c20% b2b

Case - Utility company

new application

Next projectintensified portal traffic

and portal services

Phonetic similarity

MateijsenMateijsen

MatheijsenMatheijsen

MatheysenMatheysen

MathijseMathijse

MathijsseMathijsse

MathyseMathyse

MathyssenMathyssen

MatijssenMatijssen

MattheijsenMattheijsen

MattheysenMattheysen

MatthijseMatthijse

MatthijsseMatthijsse

MatthijszenMatthijszen

MatthyssenMatthyssen

MattijsseMattijsse

MattyssenMattyssen

MateysenMateysen

MatheijssenMatheijssen

MatheyssenMatheyssen

MathijsenMathijsen

MathijssenMathijssen

MathysenMathysen

MatijsenMatijsen

MatteijssenMatteijssen

MattheijssenMattheijssen

MattheyssenMattheyssen

MatthijssenMatthijssen

MatthysseMatthysse

MattijsenMattijsen

MattijssenMattijssen

Same sound, different writingSame sound, different writing

Intelligent matching

Transport DupontTransport Dupont

Dupont LogistiqueDupont Logistique

Distribution DupontDistribution Dupont

DuPont ExpeditionDuPont Expedition

Dupont LogisticsDupont Logistics

Dupont DistributionDupont Distribution

Dupont & Dupont Exp.Dupont & Dupont Exp.

Exp. & Transp. DupontExp. & Transp. Dupont

Du Pont Logistics & Du Pont Logistics & TransportTransport

Different sound, different writing, same companyDifferent sound, different writing, same company

Case – Large bank

prospects & customersb2c and b2b

Buy 3rd party data

Case – Large bank

Dedup check on First name + Last name + Address + Birth-date

3rd party birth-date is limited to month and year because of high price

When loading the day is set to “01”

3rd partydata

1 mio recordsload

Situation: entering customer data on retail level, duplicate check, birth-date is different

(customer: “I am not born on the 1st 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 3rd party database and in systems. Or not.

prospects & customersb2c and b2b

Buy 3rd party data

Case – Large bank

load3rd party

data1 mio records

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

Limit the use of 3rd 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

INSURANCEprospects & customers

b2c and b2b

BANKprospects & customers

b2c and b2b

view on golden record

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.”

The price of bad customer data

Observations

Bad customer data hot spots

Observations

prospects & customers

application

intensified portal trafficand portal services

3rd partydata

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 3rd 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

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.

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 - Approach -

Quick Win Assessment

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

Implement

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.

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