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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
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 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
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
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
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.”
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
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.