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.
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
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
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
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
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
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
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
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
Determine quick wins
Evaluate impact of best solution /scenario
Document profiling report with findings and recommendations