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Seattle U 2010: I Love Data!
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Seattle U 2010: I Love Data!

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  • Philanthropy:The act of donating money, goods, time or effort to support a charitable cause, usually over an extended period of time and in regard to a defined objective.Passion: intense emotion
  • Home and workSelf-reported: checks, e-mail, phone calls, letters, return envelopes, etc.
  • Home and workSelf-reported: checks, e-mail, phone calls, letters, return envelopes, etc.
  • Home and workSelf-reported: checks, e-mail, phone calls, letters, return envelopes, etc.
  • Home and workSelf-reported: checks, e-mail, phone calls, letters, return envelopes, etc.
  • Home and workSelf-reported: checks, e-mail, phone calls, letters, return envelopes, etc.
  • Population characteristicsCompare the one to the groupUsually have to buy this data
  • Home and workSelf-reported: checks, e-mail, phone calls, letters, return envelopes, etc.
  • Individual characteristicsDear Mr. so and so, you may not know that we have a golf tournament coming up and we know you like golf…
  • ConsistentDocumentedEasy to followEveryone uses – no exceptions
  • Transcript

    • 1. I data!
      July 22, 2010
    • 2. Social Media
    • 3. Data
    • 4. What is data?
    • 5. Not just numbers.
    • 6. It’s facts, statistics, and information
    • 7. Builds relationships&Creates insights
    • 8. Tells a story
    • 9. “Effective data analysis is like a way of seeing.”-Stephen Fewwww.perceptualedge.com
    • 10. The most valuable asset of any organization
    • 11. What do we do with it?GatherCleanUse
    • 12. What do we gather?
    • 13. Biographical
      Think personalized
    • 14. Biographical Data
      Where do we gather this data?
    • 19. Statistical
      Think data points and calculations
    • 20. Statistical Data
      Where do we gather this data?
    • 23. Demographic
      Think targeted
    • 24. Demographic Data
      Where do we gather this data?
    • 31. Psychographic
      Think observed
      (and subjective)
    • 32. Psychographic Data
      • Personal preferences, likes and dislikes
      • 33. Hobbies and interests
      • 34. Values
      • 35. Attitudes
      Where do we gather this data?
    • 36. OK…now we’ve gathered our data.
      Let’s clean it!
      or “scrub” it… or perform “data hygiene” on it…
    • 37. Data Hygiene
      Data
      What is it anyway?
    • 38. Principles and practices that serve to maintain accuracy in a computer database
    • 39. Principles and practices that serve to maintain accuracy in a computer database
      The art of keeping our database clean and up-to-date
    • 40. It’s what happens between the mailbox and the garbage can.
    • 41. So what?!?
      Why should we care?
    • 42. Shows respect to your donors
      • Correct information in communication shows you know them
      • 43. Treats donors the way they want to be treated
      • 44. Improves long-term donor value
    • Raise more for less
      • Save costs and reap a higher response rate
      • 45. Make better fundraising selects and file maintenance decisions
      • 46. Improve ability to capture your target audience
    • Be encouraged…
      There are things we can do
    • 47. Documented business rules
      Regular de-dupe routines
      Outside hygiene services
      Hire professional help
    • 48. And now we have gathered and cleaned our data.
      So let’s use it!
    • 49.
    • 50. Key Metrics
    • 51. Micro measures
    • Number Mailed
      How many
      did we mail, call, e-mail?
      aka “count”
    • 59. Total Expenses
      How much
      did the creative, production (print and/or mail), postage cost?
      aka “costs”
    • 60. Number Responses
      How many
      people did what we asked them to do by donating or responding (even without money)?
    • 61. Gross income
      How much
      money did we raise?
      “You can’t write a check on gross…”
    • 62. Average Gift
      Gross income
      divided by
      Number Responses
    • 63. Net income
      Gross income
      minus
      Total Expenses
      “Nothing but net baby…”
    • 64. % Response
      Number Responses
      divided by
      Number Mailed
    • 65. ROIReturn-on-Investment
      Gross Income
      divided by
      Total Expenses
      1:1 – spend $1 get $1
    • 66. Rules of thumb for these measures are bogus!
      Either so broad they mean nothing
      or
      Cannot be specific enough to be helpful
    • 67. “If you don’t act on it, you just look at it, you are just enjoying your data. What do you want to do about it?”-Lihn DyeBarnes-Jewish Hospital
    • 68. Macro measures
      • Impact and Campaign Reports
      • 69. RFM Analysis
      • 70. Key Indicators
      • 71. Long-term value
    • What questions do I ask every time I pick up a report?
      Do I understand what’s going on?Do I need to do something?Do I know what to do?
    • 72. Know the purpose of your report!
      • Find a specific value: Tables
      • 73. Find the largest value: Bar Chart
      • 74. Compare values: Bar and line charts
    • 75. What about pie charts?
    • 76. Always interpret your report in context
      FYE 2003 FYE 2004 FYE 2005 FYE 2006
      TOTAL PROGRAM
      Number Giving This Year 90,031 85,009 88,539 87,846
      Number of Gifts 401,979 359,047 364,737 357,094
      Amount of Gifts 12,761,613 12,088,157 12,371,525 13,500,495
      Avg. Number/Donor 4.4 4.2 4.1 4.0
      Avg. Amount/Gift 31.74 33.66 33.91 37.80
      Avg. Cumulative Amt/Donor 141.74 142.19 139.72 153.68
    • 77. $1,000,000
    • 78. “Think how much value you have if all that data suddenly springs to life.”-Pat HanrahanCTO Tableau Software
    • 79. What’s the future hold?
      • Custom graphics
      • 80. Variable copy
      • 81. Complex calculations
      • 82. Social media integration
      • 83. Contextual ads based off social media profiles
      • 84. Data visualization
    • Strategicallygather, clean and useyour dataand you will…
    • 85. Build strong relationships&Create actionable insights
    • 86. Maximize revenue&Minimize expense
    • 87. Tell the storyof your non-profit!
    • 88. and that’s why…
      I data!
    • 89. Questions?
    • 90. I data! resources
      NTEN: Nonprofit Technology
      http://www.nten.org
      Idealware
      http://www.idealware.org
      NPower: Seattle
      http://www.npowerseattle.org/
      Wild Apricot
      http://blog.wildapricot.com
      Beth Kanter (Beth’s blog)
      http://beth.typepad.com/beths_blog/
      Tableau
      http://www.tableausoftware.com/
      Oneicity
      http://www.oneicity.com/blog
      Don’t forget one of my favorite books: “How to Lie with Statistics”
      by Darrell Huff, 1954
    • 91. Oneicity—Income Solutions for NonprofitsI data!
      Kris Hoots
      Partner
      Oneicity
      Website: www.oneicity.com
      Blog: www.oneicity.com/blog
      Facebook: www.facebook.com/oneicity
      Twitter: www.twitter.com/oneicity
      LinkedIn: www.linkedin.com/in/krishoots
    • 92. Photo credits
      Used as per Creative Commons Attribution 3.0 U.S. license
      Slide 02: http://www.flickr.com/photos/26782864@N00/4782854680/
      Slide 03: http://www.flickr.com/photos/fdevillamil/2305061260/
      Slide 05: http://www.flickr.com/photos/jfgornet/4181901804/
      Slide 06: http://www.flickr.com/photos/toky/2486199601/
      Slide 07: (purchased photo)
      Slide 07: http://www.flickr.com/photos/31672944@N07/3346060703/
      Slide 08: http://www.flickr.com/photos/umjanedoan/496707576/
      Slide 22: http://www.flickr.com/photos/jhoc/2590732283/
      Slide 25: http://www.flickr.com/photos/spbutterworth/3196892594/
      Slide 26: http://www.flickr.com/photos/stibbons/392072011/Slide 33: http://www.flickr.com/photos/redjar/136165399/
      Slide 49: http://www.flickr.com/photos/wheatfields/2587147000/
      Slide 51: http://www.flickr.com/photos/refractedmoments/223052548/