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Data Quality Survey Trends & Perceptions

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Experian QAS reviews August 2010 data quality survey. This webinar discusses research findings, key trends and tips for improving data quality in your organization. …

Experian QAS reviews August 2010 data quality survey. This webinar discusses research findings, key trends and tips for improving data quality in your organization.

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  • 1. Data Quality Survey Trends & Perceptions Wednesday, September 22, 2010 Teleconference: Dial-in: 1-866-237-3252 Passcode: 900499 © Experian Limited 2008. All rights reserved. Experian and the marks used herein are service marks or registered trademarks of Experian Limited. Other product and company names mentioned herein may be the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the prior written permission of Experian Limited. Confidential and proprietary.
  • 2. Welcome!Introductions and Overview of Today’s Session Experian QAS reviews August 2010 data quality survey  Research findings  Key trends and perceptions  Tips for improving data quality in your organization Today’s speaker:  Courtney Fulton  Marketing Programs Manager, Experian QAS © Experian Limited 2008. All rights reserved. Confidential and proprietary. 2
  • 3. Why did we do this survey? To answer questions like . . .  How do organizations prioritize data quality among other strategic initiatives?  Are there common database issues that all organizations face? Do these common issues vary by industry?  How do most organizations measure data quality?  Are businesses assessing the impact of data quality on marketing initiatives, and therefore, marketing budget?  Who typically holds responsibility for improving and ensuring data? © Experian Limited 2008. All rights reserved. Confidential and proprietary. 3
  • 4. What was the research methodology? August 2010 300 respondents from the United States Produced by pureprofile, an online marketing research firm 3 industries surveyed:  Banking  Insurance  Retail Company size from 50 employees to 1,000+ employees Titles included CEOs, CIOs, directors and managers connected with data management © Experian Limited 2008. All rights reserved. Confidential and proprietary. 4
  • 5. Data quality is top of mind for finance andinsurance industries 84% of all respondents said that they plan to invest or should plan to invest in data quality initiatives over the next 12 months  Banks are almost unanimous on this point – 90% of bank respondents chose this option  Insurers are planning a high level of investment in data quality initiatives as well, at 87% © Experian Limited 2008. All rights reserved. Confidential and proprietary. 5
  • 6. Documented strategy essential for banks Banks are more likely than insurers to document contact data management strategies  83% of bank respondents have or are currently working on this type of strategy  67% of insurance organizations have or are currently working on a documented contact data management strategy 90 80 Percentage 70 60 50 Banks Insurers Retailers © Experian Limited 2008. All rights reserved. Confidential and proprietary. 6
  • 7. Why is contact data quality important? Overall, survey respondents identified 4 key reasons for maintaining the quality of contact data: 1. Enhance customer satisfaction 2. Increase efficiency 3. Save costs 4. Capitalize on market opportunities through customer profiling © Experian Limited 2008. All rights reserved. Confidential and proprietary. 7
  • 8. Why is contact data quality important for banks andinsurers? Looking at responses from banks and insurers, the reasons for maintaining contact data quality were prioritized differently Overall 1. Enhance customer satisfaction 2. Increase efficiency 3. Save costs Banks/ Insurers 1. Save costs 2. Enhance customer satisfaction 3. Increase efficiency © Experian Limited 2008. All rights reserved. Confidential and proprietary. 8
  • 9. What are the marketing impacts of bad contactdata? Organizations waste large amounts of budget on incorrect and inaccurate contact data Overall, 63% of respondents said that 5% - 30% of their marketing budget is wasted as a result of bad data  62% of insurers fell into this category  67% of banks fell into this category © Experian Limited 2008. All rights reserved. Confidential and proprietary. 9
  • 10. How prevalent are contact data errors? 65% of banks and 60% of insurance organizations say that 6% or more of their database contains missing or inaccurate contact data Top contact data errors are consistent across industries:  Incomplete or missing data  Outdated information  Incorrect data © Experian Limited 2008. All rights reserved. Confidential and proprietary. 10
  • 11. Where do contact data errors originate? Both bank and insurance respondents agreed upon which department typically creates contact data errors: Customer Service The second most likely area of origination was different:  Bank respondents said that Marketing contributes a large number of data errors  Insurance respondents identified Sales as a main contributor of contact data errors Both industries find that multiple departments are responsible for these types of errors © Experian Limited 2008. All rights reserved. Confidential and proprietary. 11
  • 12. Who is responsible for clean contact data? Overwhelming response – the IT department is most often responsible for cleansing contact data Surprisingly, less than 25% of respondents said that data quality responsibility was shared by multiple departments  Remember – respondents said that multiple departments contribute data errors, and 28% of respondents said that ALL departments contribute data errors  A variety of departments use contact data  Why not share responsibility among several stakeholders? © Experian Limited 2008. All rights reserved. Confidential and proprietary. 12
  • 13. Are there common barriers to maintaining accuratecontact data? In a word – yes  Budget  Staff errors  Awareness of changes to data Budget Senior Management Support Large Volume of Changes Knowledge or Awareness of Changes Staff Errors Time and Internal Resource None © Experian Limited 2008. All rights reserved. Confidential and proprietary. 13
  • 14. How do organizations measure data quality? Respondents had three main ways of measuring data quality:  Analysis of response rates 60  Software tools 40 Percentage  Manual processes 20 Only a very small percentage of 0 survey respondents have no Manual Processes current system for measuring data Outsource to agency Analysis of response rates quality Software Tools We dont currently measure accuracy © Experian Limited 2008. All rights reserved. Confidential and proprietary. 14
  • 15. How do organizations maintain and improve contactdata? We Have No Solutions in Place We Have Clean Data Prior to Each Communication Clean Da Data Cleansing Tools at All Customer Data Clea Touchpoints Software Tools Software Outsource to Call Center Outsourc Outsourcing of Clean Data Outsourc Staff are Measured on Data Quality Staff are Training of Staff Training 0 20 40 60 0 20 40 60 Percentage PercentageBANKS We Have No Solutions in Place INSURERS Clean Data Prior to Each Communication Data Cleansing Tools at All Customer Touchpoints Software Tools Outsource to Call Center Outsourcing of Clean Data Staff are Measured on Data Quality Training of Staff 0 20 40 60 Percentage © Experian Limited 2008. All rights reserved. Confidential and proprietary. 15
  • 16. What types of data quality tools are being used? Banks 60% Insurers 40% 20% 0% Point-of- Batch Address De- Email Capture Verification Duplification Verification Address Verification © Experian Limited 2008. All rights reserved. Confidential and proprietary. 16
  • 17. Overall trends Data quality is a priority for businesses Customer satisfaction key reason for data quality Large amount of resources wasted on inaccurate data Customer Service, Sales and Marketing cited as error-prone departments One department is responsible for data quality Budget still ranked as key barrier to clean data Companies still use manual processes to clean data © Experian Limited 2008. All rights reserved. Confidential and proprietary. 17
  • 18. Tips to clean data1. Understand your database2. Clean existing data3. Remove duplicate records4. Verify data during all capture processes5. Enhance and update data © Experian Limited 2008. All rights reserved. Confidential and proprietary. 18
  • 19. QASProducts & services Real-time verification Clean & enhance Address Clean  QAS Pro (PC Based)  QAS Batch (PC Based)  QAS Pro On Demand  QAS Bulk Processing (Software as a Service) (Web Based)  QAS Pro Web (Web Based)  Phone & Email Batch  QAS Pro API (Service) (Integration Toolkit) Enhance Phone and Email  QAS Unify  QAS Phone (Service) (PC Based)  QAS Email (Service)  NCOALink® (Service) © Experian Limited 2008. All rights reserved. Confidential and proprietary. 19
  • 20. Please visit www.qas.com for more information. © Experian Limited 2008. All rights reserved. Confidential and proprietary. 20