Predictive Donor Value Metrics

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How to use predictive data to engage your file and convert more donors. #12ntc

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Predictive Donor Value Metrics

  1. Predictive DonorValue Metrics#12NTCpredictDaniel Atherton, CCAHBrenna Holmes, CCAHMathew Grimm, EDFJohn Clese, AVECTRA PREDICTIVE DONOR METRICS Slide 1
  2. Evaluate This Session!Each entry is a chance to win an NTEN engraved iPad! INSERT QR CODE HERE or Online at www.nten.org/ntc/eval PREDICTIVE DONOR METRICS Slide 2
  3. Agenda• How do you use data?• Why predictive data is important• Offline data examples• How can we use offline techniques online?• Data at Environmental Defense Fund• A-Score: A way to measure constituents• Questions PREDICTIVE DONOR METRICS Slide 3
  4. Who are we? PREDICTIVE DONOR METRICS Slide 4
  5. What you’ll learn today• Why predictive data is important• Some tips for how to gather predictive data for your constituents• Examples of how EDF and AVECTRA gather and use data PREDICTIVE DONOR METRICS Slide 5
  6. How do you use data? PREDICTIVE DONOR METRICS Slide 6
  7. What is predictive data?• Predictive data is data that allows you to predict how a constituent will respond to your direct marketing PREDICTIVE DONOR METRICS Slide 7
  8. Consider this:• Jane Q. Nondonor signs up for your list through your organization’s website• The next week, your board comes to you and says, “We want to ask our supporters to dedicate bricks outside our new office. They will cost $5,000 each.”• Do you send your brick-dedicating email to Jane? PREDICTIVE DONOR METRICS Slide 8
  9. I hope not.PREDICTIVE DONOR METRICS Slide 9
  10. Why predictive data is important• Is Jane likely to give you $5,000 for a brick only a week after joining your list?• Is Jane likely to be very early in the “funnel of engagement” – looking for more information about your organization and why she should trust it?• Might Jane decide that your organization seems kind of greedy, to be asking for $5,000 so soon?• Might she think that you don’t even know anything about her? PREDICTIVE DONOR METRICS Slide 10
  11. How do we know this?• Data. PREDICTIVE DONOR METRICS Slide 11
  12. How do we know this?• Very few of your constituents are likely to be major donors – 0.51% of EDF’s online file is in their major donor track• You are likely to be unsuccessful asking a new constituent for an amount so much higher than the average for first-time gifts – The average first-time gift in the past 12 months for EDF’s file is $66.90 PREDICTIVE DONOR METRICS Slide 12
  13. How do we know this?• New users are much MORE likely to interact with the first few messages they get from you. – EDF’s average open rate for its welcome series is 30-100% higher than its usual open rates for non- donor segmentsWhy would you waste that on a very low- probability ask? PREDICTIVE DONOR METRICS Slide 13
  14. Why predictive data is important• In the offline world, where there is real opportunity cost in contacting a prospective donor, predictive data is critical.• In the online world, there is still a hidden opportunity cost: – Your time – The trust of your constituents – The possibility that constituents will unsubscribe or “tune out” future emails PREDICTIVE DONOR METRICS Slide 14
  15. BULLETIN: INACTIVITY IS VERY BAD EMAIL NERDS REPORTAs SPAM filters become harsher and moreresponsive, a user ignoring your email because itdoesn’t speak to her is no longer simply anopportunity cost. It affects your ability to reacheven the users who WANT to read your emails. PREDICTIVE DONOR METRICS Slide 15
  16. Why predictive data is important• We may claim that we hate the ways marketers use our online habits to tailor ads to us – but then we get mad when those ads seem irrelevant• The key to establishing trust with your prospective donors – and to drive interaction – is to seem like you know what they want to be asked PREDICTIVE DONOR METRICS Slide 16
  17. Duh, dude. But how? PREDICTIVE DONOR METRICS Slide 17
  18. Cheer up – it’s not that hard. PREDICTIVE DONOR METRICS Slide 18
  19. You may already use predictive data.• Do you segment your file into non-donors, low-dollar donors, and high-dollar donors?• Do you segment your file by recency of gift? 0-12 month, 13-24, 24+?• Do you use HPC as the basis for your donation form ask strings?• Do you try to get a second gift out of first-time donors within 30 days of that first gift? PREDICTIVE DONOR METRICS Slide 19
  20. If not, you should be.• These best practices all stem from predictive data: – Donors’ giving patterns tend to stay fairly static; someone whose first gift was $35 is unlikely to respond to a high-dollar ask. – HPC-based ask strings are a time-tested best practice offline, and testing shows that (for most lists) they produce the best return online, too. – Donors are MOST likely to make their second gift within a few weeks of their first – or even to become a monthly giver. PREDICTIVE DONOR METRICS Slide 20
  21. Speaking of offline… PREDICTIVE DONOR METRICS Slide 21
  22. Learning from offline examples• Since it costs money to mail a package, your net is greatly affected by how successful the package is and how valuable converted donors become. PREDICTIVE DONOR METRICS Slide 22
  23. Learning from offline examples• We use predictive metrics in all sorts of ways offline: – In what channel/s is/are John Q. Donor most responsive? – To what types of campaigns does John give most often? – Will John be more valuable over his donor lifespan if he joins the file via a premium? PREDICTIVE DONOR METRICS Slide 23
  24. Offline examples• Creating a “TM Track” for donors who are particularly responsive on the phones• Noting when particular lists respond better to certain topics or campaigns and mailing a higher quantity• Finding the most likely paths for donors to become sustainers, and cultivating that path PREDICTIVE DONOR METRICS Slide 24
  25. So how can this work online? PREDICTIVE DONOR METRICS Slide 25
  26. 3 steps to predictive data online1. Gather as much data as possible.2. Look for patterns in that data.3. Selectively target constituents based on which asks will have maximum value. PREDICTIVE DONOR METRICS Slide 26
  27. Step 1: Gather data PREDICTIVE DONOR METRICS Slide 27
  28. Step 1: Gather dataThere are three ways to gather data on yourconstituents: 1. Automatically through your blast mailer/CRM • Basic stats like time on file, opens and clicks, donation history 2. Through an append or file modeling service • Biographical stats like age and gender; data on social media use or how users spend their time online 3. The best way: ask for it! PREDICTIVE DONOR METRICS Slide 28
  29. Using surveys to find stuff out PREDICTIVE DONOR METRICS Slide 29
  30. Using surveys to find stuff out• Ask questions about users as soon as they sign up for your list• Ask questions about users when they’re taking an action or donating• Send your file surveys a couple times per year – then ask for a donation when they’re done PREDICTIVE DONOR METRICS Slide 30
  31. Using surveys to find stuff out• Post-survey donation asks are one of the most successful, least intrusive ways to convert new donors and to engage a “tired” file – People like being asked what they think – If they think you’re listening to them, people will think more highly of your organization – and what you would do with their donation – Surveys are just like ZIP code, address, cell phone number – the more info someone is willing to give you, the better a donor s/he is likely to become PREDICTIVE DONOR METRICS Slide 31
  32. Step 2: Look for patterns PREDICTIVE DONOR METRICS Slide 32
  33. Step 2: Look for patterns• Become a journalist in your dogged pursuit of fundraising truth: – Who – What – When – Where – Why – How PREDICTIVE DONOR METRICS Slide 33
  34. Who?• Who is engaging with your emails? – Are a small core of dedicated activists driving 90% of the actions and/or donations? – Does your file skew old or young? Male or female? PREDICTIVE DONOR METRICS Slide 34
  35. What?• What is your file interested in? – Do they prefer to hear about/take action on/donate to one of your issues over another? – Do they prefer to sign petitions? Do they prefer to donate? Do they prefer to share personal stories? PREDICTIVE DONOR METRICS Slide 35
  36. When?• When does your file engage with you? – Do they donate more in the morning or the evening? – Are they more active if you send an email on a Monday or a Friday? Are they active on weekends? – Do they donate at particular times of year? PREDICTIVE DONOR METRICS Slide 36
  37. Where?• Where is your file? – Where does your file live? Are they concentrated in particular states or cities? – Where does your file access your content? Do they use your website? Do they engage mostly through your emails? How about Facebook and Twitter? PREDICTIVE DONOR METRICS Slide 37
  38. Why?• Why do they give to you? – Do they respond to: • Institutional asks (“We are rated four stars by Charity Navigator”)? • Emotional appeals (“These children need your help”)? • Efficiency (“94 cents of each dollar go straight to people in need”)? • Emergency (“WE NEED THIS RIGHT NOW HELP”)? • Anger (“Here’s something dumb this idiot said about us”)? PREDICTIVE DONOR METRICS Slide 38
  39. How?• Do a few donors give large amounts, or do lots of donors give small amounts?• Do your donors respond to renewals, or to appeals?• Do they give online after they’ve received a mail piece or a TM call? Or vice versa? PREDICTIVE DONOR METRICS Slide 39
  40. Step 3: Profit PREDICTIVE DONOR METRICS Slide 40
  41. Target your asks based on the data• If your file (or donors) are mostly older women, focus on what’s important to them• If your file prefers to take action rather than donate, use more after-action donate asks• If your file donates more on Mondays…send more emails on Mondays PREDICTIVE DONOR METRICS Slide 41
  42. Target your asks based on the data• If you’re a national organization but 50% of your file lives in California, consider locally- targeted content• If part of your file responds more institutionally and part responds more emotionally, split up your segments to target them• If part of your file responds more to renewals than to appeals, send them more renewal asks PREDICTIVE DONOR METRICS Slide 42
  43. Environmental Defense Fund PREDICTIVE DONOR METRICS Slide 43
  44. AVECTRA• A-Score SCORE PREDICTIVE DONOR METRICS Slide 44
  45. Why score your constituents?• Scoring enables you to measure relevant information about who a given constituent is, how they interact with your organization and identify key behavioral attributes• A weighted score relevant to your organization’s mission and activities helps support smarter, more targeted and timely engagement activities in a reliable, systematic way• Use scoring to unveil early indicators of other donors who are beginning to mirror key characteristics of your top performers and use this data to intervene more effectively in the relationship PREDICTIVE DONOR METRICS Slide 45
  46. Why score your constituents?• Scoring can help identify donors who are disengaging from your organization by aggregating and scoring behavioral trends unnoticeable to the naked eye• Scoring can replace the herculean task of multiple queries, reports and analysis to spot trends within in your donor base PREDICTIVE DONOR METRICS Slide 46
  47. Discovery• Who are my top constituents?• What are the activities that people and organizations do that are meaningful and valuable to you?• Similarly, which demographic characteristics are meaningful to you and indicate the importance of a member or donor?• Include observed and tracked behavior, activities and demographics, as well as your anecdotal information, whether these are in your database or not. PREDICTIVE DONOR METRICS Slide 47
  48. A-Score™ Scales• A-Score™ is a composite of Advocacy other scales, each of which Social Participation measures engagement in a A-Score specific category Events Fundraising PREDICTIVE DONOR METRICS Slide 48
  49. View Scoring Trend Over Time PREDICTIVE DONOR METRICS Slide 49
  50. PREDICTIVE DONOR METRICS Slide 50
  51. PREDICTIVE DONOR METRICS Slide 51
  52. Questions?PREDICTIVE DONOR METRICS Slide 52

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