IoF CRM & the Donor Journey - top ten tips for driving fundraising with data

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Ten tips for driving engagement, retention and income using your data from Purple Vision.

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  • Have taken out PV slides, so cover off at this stage re recap on orgs & what they do. Then ask Q – Who here gets excited about data? Who gets excited about supporters? Erm, same thing.
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  • DV slide. Draw donor pyramid on flipchart and show how useless unless you how who’s where, and how to find them.
  • DV – in slides for ease of ref for download post even
  • DV – operating in silos within the team, dept, org…
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  • Shoes, hat. Put yourself in their shoes. What hat are you wearing (B2C vs B2B)
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  • DV – What has caused this? Natural supporters inclined to support, or activity in those areas??
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  • IoF CRM & the Donor Journey - top ten tips for driving fundraising with data

    1. 1. Top ten tips for driving fundraising with your data (aka) how I learned to love data 1
    2. 2. Who we are Steve Thomas Managing Director, Purple Vision www.purple-vision.com @stevethomas393 @purple_vision Dawn Varley Director of Marketing and Fundraising, League Against Cruel Sports www.league.org.uk @nfpdawnv 2
    3. 3. Contents What How So • Strategy • Silos • People • DP • Segmentation • Tools • Integrate • Analyse • Journeys • Evangelism 3
    4. 4. 1. Know what you’ve got • • • • What data? Who decides collection/coding? Who knows it? Org retains info? Data must be driven by strategy 4
    5. 5. The classic donor pyramid Fundraising 101. But *completely useless* if you don’t know what the data says… 5
    6. 6. IoF National Convention 2013 6
    7. 7. 2. Avoid keeping data in silos 7
    8. 8. Data map CMS Social media monitor and broadcast Website Donations SG Donations RG Data import layer Online Advocacy Bulk Email Campaign emails Petitions MPs API Text donation Volunteers Donor database Events |Trust & Statutory | Stakeholders | Members Survey Off-line capture Data house Retail Political contacts Comp laints Finance Opera tions HR Media
    9. 9. 3. Remember data is all about people • Gifts don’t give themselves • The best fundraising is relational, not transactional (Fundraising 101…) • To grow your fundraising you have to know your data • And there are other people too… 9
    10. 10. Data is about people, process and technology (in that order) 10
    11. 11. 4. Keep it clean, and respectful You’ve got data, but is it healthy? Data cleansing is important – budget for it and do it. It can save you money, add value, and keep supporters happy. You can’t talk about data with the dreaded Data Protection subject coming up… Data protection is just about respect for your supporters. How would you like to be treated? 11
    12. 12. The key to success is… 12
    13. 13. 5. Recognise diversity in data One size doesn’t fit all
    14. 14. What is Segmentation? Classification of the population into subgroups that are: • • • • Distinguishable Identifiable Manageable Fit for purpose 14
    15. 15. Why segment? • • • • Make targeting more appropriate to audience Avoid scattergun communications Protect against unsubscribes and lapsing Makes internal expectations realistic 15
    16. 16. Classic segmentation - RFV Recency Frequency Value 16
    17. 17. Creating segments Recency Frequency Value 17
    18. 18. Creating segments 4 3 2 Recency 8 1 9 6 7 Frequency Value 18
    19. 19. Bases for Segmentation • • • • • • Supporter category Reason for support How old are they? How loyal are they? Where in life cycle? Where do we want to take them? • Types of information to collect to enable better segmentation: • • • • • Comms preferences Format/media type Event attendance Frequency of contact Purchases Choose data relevant to your strategy 19
    20. 20. 6. How to manage data You now understand the data, and know how to make the most of it. But what systems help you do that? • Data lives in systems, eg CRM, CMS, Excel etc • Know your systems (‘System Architecture’) • Build to future proof, and this is driven by… Fundraising/Organisational strategy (101!) If I had a £1… 20
    21. 21. 21
    22. 22. 7. Integrate
    23. 23. 7. Integrate 23
    24. 24. Email & social media integration • Add-ins – eg. Outlook • CRM integration “bridges” • Benefits • • • • Track friends and followers Major donors Advocates and viral “buzz” Measure level of influence 24
    25. 25. IoF National Convention 2013 25
    26. 26. 26
    27. 27. Systems integration 27
    28. 28. Data map CMS Social media monitor and broadcast Website Donations SG Donations RG Data import layer Online Advocacy Bulk Email Campaign emails Petitions MPs API Text donation Volunteers Donor database Events |Trust & Statutory | Stakeholders | Members Survey Off-line capture Data house Retail Political contacts Comp laints Finance Opera tions HR Media
    29. 29. Data warehouse Off-line capture Website Data house Online Fundraising CMS Forms, HR, Volunteers News, Forums Supporter Portals, Donor Journeys Events & P2P E-commerce Opera tions Bulk Email Bulk email Segmentation Newsletter Design Retail Media Directory Data Import Layer Finance API Comp laints Text donation Donor Database Volunteers Political contacts HR Events |Trust & Statutory | Stakeholders | Members Social monitor and Broadcast Online Advocacy Campaign emails Petitions MPs API Data Tools Data Warehouse Data Analytics & Reporting 29
    30. 30. 8. So – what does this mean? So, you have your data, you know what it means, and you have it in the right place… Now you need to make the data work for you by: • Profiling your data • Learning from your data • Using it to inform your strategy eg looky-like acquistion, targeted messages, correct channels 30
    31. 31. Look alike logic Universe Non-profit supporters Your Database Your Sector 31
    32. 32. Profile variables • • • • • • Income Housing Tenure Spending Power Education Occupation Social Grade • • • • • • Age Children Household Size Property Type Urbanicity Retail Accessibility 32
    33. 33. Profile Model – closeness of fit Segment 4 (71<Tenure) AND (54<Age) AND (60<Urbanicity<=65) Segment 16 (85<Tenure) AND (54<Age) AND (65<Urbanicity<=83) Segment 7 (71<Tenure<=85) AND (54<Age) AND (65<Urbanicity<=83) Segment 10 (71<Tenure) AND (Age<=54) AND (72<Property) AND (60<Urbanicity<=83) Segment 8 (40<Tenure<=71) AND (56<Age) AND (62<Urbanicity<=83) Segment 3 (71<Tenure) AND (Age<=54) AND (Property<=72) AND (60<Urbanicity<=83) Segment 15 (32<Tenure<=71) AND (45<Spend) AND (Age<=56) AND (60<Urbanicity<=88) Segment 9 (40<Tenure<=71) AND (Education<=46) AND (56<Age) AND (83<Urbanicity) Segment 11 (71<Tenure) AND (63<Age) AND (83<Urbanicity) Segment 20 (11<Income) AND (Tenure<=40) AND (56<Age) AND (Children<=50) Segment 18 (71<Tenure) AND (82<Spend) AND (Urbanicity<=60) Segment 14 (32<Tenure<=71) AND (Spend<=45) AND (Age<=56) AND (60<Urbanicity<=88) Segment 19 (40<Tenure<=71) AND (46<Education) AND (56<Age) AND (83<Urbanicity) Segment 6 (40<Tenure<=71) AND (56<Age) AND (Urbanicity<=62) Segment 22 (Tenure<=32) AND (25<Spend) AND (Age<=56) AND (60<Urbanicity<=88) Segment 17 (Tenure<=40) AND (Education<=29) AND (56<Age) AND (50<Children) Segment 5 (Income<=11) AND (Tenure<=40) AND (56<Age) AND (Children<=50) Segment 2 (71<Tenure) AND (Age<=63) AND (83<Urbanicity) Segment 0 (Tenure<=71) AND (Age<=56) AND (Urbanicity<=60) AND (Retail<=43) Segment 1 (71<Tenure) AND (Spend<=82) AND (Urbanicity<=60) Segment 24 (Tenure<=40) AND (29<Education) AND (56<Age) AND (50<Children) Segment 13 (Tenure<=32) AND (Spend<=25) AND (Age<=56) AND (60<Urbanicity<=88) Segment 23 (Tenure<=71) AND (38<Age<=56) AND (88<Urbanicity) Segment 12 (Tenure<=71) AND (Education<=36) AND (Age<=38) AND (88<Urbanicity<=90) Segment 28 (Tenure<=71) AND (36<Education) AND (Age<=38) AND (88<Urbanicity<=90) Segment 27 (Tenure<=71) AND (38<Spend) AND (Age<=56) AND (Urbanicity<=60) AND (43<Retail) Segment 26 (Tenure<=71) AND (39<Occupation) AND (Age<=38) AND (90<Urbanicity) Segment 21 (Tenure<=71) AND (Spend<=38) AND (Age<=56) AND (Urbanicity<=60) AND (43<Retail) Segment 25 (Tenure<=71) AND (Occupation<=39) AND (Age<=38) AND (90<Urbanicity) 33
    34. 34. Profile Model – closeness of fit Assembli Model Customers Counts Base % Counts Penetration % Z-Score Index % 0 100 200 Segme nts Segment 4 1582 11.7 10311 3.0 15.3 9 396 ██████████ >200 Segment 16 1206 8.9 10017 2.9 12.0 7 311 ██████████ >200 Segment 7 980 7.2 10008 2.9 9.8 6 253 ██████████ >200 Segment 10 958 7.1 10183 2.9 9.4 6 243 ██████████ >200 Segment 8 1418 10.5 16860 4.8 8.4 6 217 ██████████ >200 Segment 3 950 7.0 15953 4.6 6.0 3 154 █████ Segment 15 661 4.9 12749 3.7 5.2 2 134 ███ Segment 9 540 4.0 10787 3.1 5.0 2 129 ███ Segment 11 534 4.0 10760 3.1 5.0 2 128 ███ Segment 20 565 4.2 14191 4.1 4.0 0 103 Segment 18 377 2.8 10391 3.0 3.6 0 94 █ Segment 14 497 3.7 15365 4.4 3.2 -1 84 ██ Segment 19 385 2.8 12085 3.5 3.2 -1 82 ██ Segment 6 404 3.0 13376 3.8 3.0 -2 78 ██ Segment 22 267 2.0 10391 3.0 2.6 -2 66 ███ Segment 17 232 1.7 10003 2.9 2.3 -3 60 ████ Segment 5 228 1.7 10115 2.9 2.3 -3 58 ████ Segment 2 352 2.6 17560 5.0 2.0 -5 52 █████ Segment 0 215 1.6 12063 3.5 1.8 -5 46 █████ Segment 1 158 1.2 10053 2.9 1.6 -5 41 ██████ Segment 24 159 1.2 10856 3.1 1.5 -6 38 ██████ Segment 13 152 1.1 10429 3.0 1.5 -6 38 ██████ Segment 23 253 1.9 17591 5.0 1.4 -8 37 ██████ Segment 12 120 0.9 10061 2.9 1.2 -7 31 ███████ Segment 28 82 0.6 10053 2.9 0.8 -10 21 ████████ Segment 27 74 0.5 10014 2.9 0.7 -11 19 ████████ Segment 26 74 0.5 10316 3.0 0.7 -11 19 ████████ Segment 21 48 0.4 12971 3.7 0.4 -19 10 █████████ Segment 25 47 0.3 13458 3.9 0.3 -21 9 █████████ - - 0 0 - Total 13518 348,970 3.87 34
    35. 35. Segment geography Closest fit Furthest fit BUT use past data analysis/organisational knowledge to inform strategy too! 35
    36. 36. 9. So – Supporter Journeys Volunteered Became committed giver Joined membership First Gift Volunteered Legacy Pledge Became committed giver 36
    37. 37. First engagement = purchase 37
    38. 38. First engagement = committed gift 38
    39. 39. Loyalty ladders 7119 super close 2790 5311 7 on holiday Segment 7 7 on sabbatical 2525 Segment 6 keen but stuck 2295 Segment 5 4183 activists Segment 4 6671 first biters Segment 3 Potentials Segment 2 9457 Zeros Segment 1 Segment 0 39
    40. 40. Segment shifting Probabilities of being present in each segment next month depending on presence this month 7 0.12 0.47 1.10 1.85 3.25 9.20 11.08 88.74 79.48 4.62 87.23 7.16 4.62 96.75 3.57 2.27 4 5 6 6 5 0.01 4 3 0.27 0.18 0.89 92.88 2 0.42 2.22 93.74 3.97 1 0.01 97.12 4.25 1.24 0 99.18 0.01 0.05 2 3 0 1 7 40
    41. 41. Insight – snakes and ladders 8845 3511 super close 3301 7 on holiday Segment 7 3976 on sabbatical 7 Segment 6 3111 keen but stuck Segment 5 2213 activists Segment 4 6649 first biters Segment 3 Potentials 7250 Segment 2 Zeros Segment 1 Segment 0 41
    42. 42. 10. Be a data evangelist Now you know the power of data, and how it can transform your fundraising. You have been initiated into the club, and you must be a data … - advocate - believer - defender 42
    43. 43. Recap… What How So • Strategy • Silos • People • DP • Segmentation • Tools • Integrate • Analyse • Journeys • Evangelism 43
    44. 44. Top Ten Tips 1. Get to know your data. What do you have, what do you need? 2. Avoid data silos. What brings it together? 3. Data is people. Do what you can to build relationships, internal and external 4. Keep relationships clean & respectful. How you apply data protection & cleansing is key. 5. Know when and how to segment 44
    45. 45. Top Ten Tips 6. Be aware how your data is managed 7. Discover how to bring it all together 8. So learn from your data – report, analyse, question – and use it to inform decisions 9. So apply data insights to growing your supporters’ relationships (and their giving) 10. So now live it! Go back to the office and be a data evangelist. 45
    46. 46. Resources Institute of Fundraising Groups: • Insight SIG http://insightsig.org/ • Technology SIG http://www.ioftech.org.uk/ LinkedIn for networking and Groups, inc • Purple Patch • UK Fundraising • Institute of Fundraising….and more! Events • Purple Vision Breakfast Briefings • IoF Insight & IoF Tech conferences 46
    47. 47. Questions? steve.thomas@purple-vision.com dawnvarley@league.org.uk Find us on LinkedIn Follow us on twitter @nfpdawnv @stevethomas393 47

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