The document discusses the high costs organizations face due to bad customer data. It provides examples where poor data quality led to unnecessary marketing expenses, duplicate records, late or missed deliveries, and more. The key observations are that data quality issues are widespread, solving them requires a holistic approach, and the costs of bad data are often underestimated and can exponentially increase over time across multiple systems and processes if not addressed. It also promotes a quick win assessment service from Accenture and Human Inference to analyze an organization's data quality and identify priority areas for improvement.

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

4. TELCO & UTILITIES BANKING INSURANCE PUBLISHERS OUR PARTNERS

5. LOGISTICS PUBLIC LEISURE SERVICES OTHER

9. The price of bad customer data Some examples

10. Case – Car distributor

13. Case – Utility company

14. 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

15. 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

16. 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

17. 1 mio records prospects & customers 80% b2c 20% b2b Case - Utility company new application Next project intensified portal traffic and portal services

18. 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

19. 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

20. Case – Large bank

21. 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

22. 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

23. Case – Large bank

24. 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

25. The price of bad customer data Observations

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

28. Presentation Mark Humphries

29. Accenture – Human Inference proposition