What is the price of bad customer data?
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What is the price of bad customer data?

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    What is the price of bad customer data? What is the price of bad customer data? Presentation Transcript

    • 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
        • 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
    •  
    •  
    • 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
    • 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
    • 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.
    • 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
    • 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