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Raab Reachforce AMA Data Quality

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Transcript

  • 1. Marketing Lead Data:Five Steps to Higher Revenue July 20, 2011 David M. Raab Raab Associates Inc. 1
  • 2. Data Problem? What Data Problem? • top 3 marketing problems: technology, resources, pr ocess (what about data?) • 2/3 of prospect data is less than 75% accurate (vs. 1/3 of customer data) • data decays at 2% per month 2
  • 3. Why Data Problems Matter • your new marketing automation system will fail (the boss won’t be happy) • your email won’t get delivered (or, even worse, it will) • opportunity costs (wasted acquisitions, lost revenue) • salespeople won’t follow up (55% blame missing data; next-highest reason is 14%) 3
  • 4. Root Causes• poor data capture • less sales involvement• user-provided data • recycled operational data 4
  • 5. Solving the Problem: 5 Step Program1. Set a Baseline2. Organize for Action3. Make Improvements4. Measure Results5. Repeat as Needed 5
  • 6. 1. Set a Baseline • research existing data • select key elements (one word: email) • test for dupes and errors • consider the source • document update processes • identify opportunities for improvement • define value and prioritize 6
  • 7. 2. Organize for Action• governance • cross-department team • executive sponsor • departmental data stewards• select projects • set goals • define metrics • track and report 7
  • 8. 3. Make Improvements initial projects: simple, low cost, measurable • input: data capture, user registration, progressive profiling • import: processing rules, source priority • external data: validation, enhancement, refresh • process: sales and marketing coordination, training, feedback 8
  • 9. 4. Measure Results• measure types • always compare to something • effort (costs, processing volumes) • goal, history, industry benchmark • results (changes, error rates, usage, attitudes) • make results accessible • business value (lower costs, more leads, higher revenues) • dashboard, variance reports 9
  • 10. 5. Repeat as Needed • (it’s always needed) • identify new opportunities • avoid back-sliding (maintain previous improvements) • on-going baseline measurements 10
  • 11. Tools Can Help• data cleansing • name/address standardization, matchi ng, reference data• real time validation• periodic refresh• no silver bullet 11
  • 12. Conclusion • quality pays • integrated systems magnify the value • quality programs set the stage for additional cooperation 12
  • 13. Thank You.David RaabRaab Associates Inc.draab@raabassociates.comwww.raabassociatesinc.omTwitter: @draab 13