Top ten tips for driving fundraising with your data. (aka) how I learned to love data

Transcription

1 Top ten tips for driving fundraising with your data (aka) how I learned to love data 1

2 Who we are Steve Thomas Managing Director, Purple Dawn Varley Director of Marketing and Fundraising, League Against Cruel Sports 2

3 Contents What Strategy Silos People How DP Segmentation Tools Integrate So Analyse Journeys Evangelism 3

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 The classic donor pyramid Fundraising 101. But *completely useless* if you don t know what the data says 5

6 IoF National Convention

7 2. Avoid keeping data in silos 7

8 Data map CMS Website Donations SG Donations RG Social media monitor and broadcast Online Advocacy Data import layer Bulk Campaign s Petitions MPs Text donation API Volunteers Comp laints Donor database Events Trust & Statutory Finance Opera tions Survey Stakeholders Members HR Off-line capture Data house Retail Political contacts Media

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 Data is about people, process and technology (in that order) 10

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 The key to success is 12

13 5. Recognise diversity in data One size doesn t fit all

14 What is Segmentation? Classification of the population into subgroups that are: Distinguishable Identifiable Manageable Fit for purpose 14

15 Why segment? Make targeting more appropriate to audience Avoid scattergun communications Protect against unsubscribes and lapsing Makes internal expectations realistic 15

16 Classic segmentation - RFV Recency Frequency Value 16

17 Creating segments Recency Frequency Value 17

18 Creating segments 4 Recency Frequency Value 18

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 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

22 7. Integrate

23 7. Integrate 23

24 & 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

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27 Systems integration 27

28 Data map CMS Website Donations SG Donations RG Social media monitor and broadcast Online Advocacy Data import layer Bulk Campaign s Petitions MPs Text donation API Volunteers Comp laints Survey Donor database Events Trust & Statutory Stakeholders Members Finance Opera tions HR Off-line capture Data house Retail Political contacts Media

29 Data warehouse CMS Forms, HR, Volunteers News, Forums Website Bulk Bulk Segmentation Newsletter Design Comp laints Opera tions Retail Online Fundraising Supporter Portals, Donor Journeys Events & P2P E-commerce Donor Database Events Trust & Statutory Stakeholders Members Data Import Layer API Off-line capture Data house Finance Volunteers HR Text donation Political contacts Media Directory Online Advocacy Campaign s Petitions MPs API Data Warehouse Data Tools Social monitor and Broadcast Data Analytics & Reporting 29

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 Look alike logic Non-profit supporters Universe Your Database Your Sector 31

32 Profile variables Income Housing Tenure Spending Power Education Occupation Social Grade Age Children Household Size Property Type Urbanicity Retail Accessibility 32

33 Profile Model closeness of fit Segment 4 Segment 16 Segment 7 Segment 10 Segment 8 Segment 3 Segment 15 Segment 9 Segment 11 Segment 20 Segment 18 Segment 14 Segment 19 Segment 6 Segment 22 Segment 17 Segment 5 Segment 2 Segment 0 Segment 1 Segment 24 Segment 13 Segment 23 Segment 12 Segment 28 Segment 27 Segment 26 Segment 21 Segment 25 (71<Tenure) AND (54<Age) AND (60<Urbanicity<=65) (85<Tenure) AND (54<Age) AND (65<Urbanicity<=83) (71<Tenure<=85) AND (54<Age) AND (65<Urbanicity<=83) (71<Tenure) AND (Age<=54) AND (72<Property) AND (60<Urbanicity<=83) (40<Tenure<=71) AND (56<Age) AND (62<Urbanicity<=83) (71<Tenure) AND (Age<=54) AND (Property<=72) AND (60<Urbanicity<=83) (32<Tenure<=71) AND (45<Spend) AND (Age<=56) AND (60<Urbanicity<=88) (40<Tenure<=71) AND (Education<=46) AND (56<Age) AND (83<Urbanicity) (71<Tenure) AND (63<Age) AND (83<Urbanicity) (11<Income) AND (Tenure<=40) AND (56<Age) AND (Children<=50) (71<Tenure) AND (82<Spend) AND (Urbanicity<=60) (32<Tenure<=71) AND (Spend<=45) AND (Age<=56) AND (60<Urbanicity<=88) (40<Tenure<=71) AND (46<Education) AND (56<Age) AND (83<Urbanicity) (40<Tenure<=71) AND (56<Age) AND (Urbanicity<=62) (Tenure<=32) AND (25<Spend) AND (Age<=56) AND (60<Urbanicity<=88) (Tenure<=40) AND (Education<=29) AND (56<Age) AND (50<Children) (Income<=11) AND (Tenure<=40) AND (56<Age) AND (Children<=50) (71<Tenure) AND (Age<=63) AND (83<Urbanicity) (Tenure<=71) AND (Age<=56) AND (Urbanicity<=60) AND (Retail<=43) (71<Tenure) AND (Spend<=82) AND (Urbanicity<=60) (Tenure<=40) AND (29<Education) AND (56<Age) AND (50<Children) (Tenure<=32) AND (Spend<=25) AND (Age<=56) AND (60<Urbanicity<=88) (Tenure<=71) AND (38<Age<=56) AND (88<Urbanicity) (Tenure<=71) AND (Education<=36) AND (Age<=38) AND (88<Urbanicity<=90) (Tenure<=71) AND (36<Education) AND (Age<=38) AND (88<Urbanicity<=90) (Tenure<=71) AND (38<Spend) AND (Age<=56) AND (Urbanicity<=60) AND (43<Retail) (Tenure<=71) AND (39<Occupation) AND (Age<=38) AND (90<Urbanicity) (Tenure<=71) AND (Spend<=38) AND (Age<=56) AND (Urbanicity<=60) AND (43<Retail) (Tenure<=71) AND (Occupation<=39) AND (Age<=38) AND (90<Urbanicity) 33

34 Profile Model closeness of fit Segme nts Assembli Model Customers Base Penetration Z-Score Index Counts % Counts % % Segment >200 Segment >200 Segment >200 Segment >200 Segment >200 Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Segment Total ,

35 Segment geography Closest fit Furthest fit BUT use past data analysis/organisational knowledge to inform strategy too! 35

36 9. So Supporter Journeys First Gift Became committed giver Volunteered Joined membership Legacy Pledge Volunteered Became committed giver 36

37 First engagement = purchase 37

38 First engagement = committed gift 38

39 Loyalty ladders super close on holiday Segment on sabbatical Segment keen but stuck Segment activists Segment first biters Segment Potentials Segment 2 Zeros Segment 1 Segment 0 39

40 Segment shifting Probabilities of being present in each segment next month depending on presence this month

41 Insight snakes and ladders super close on holiday 3976 Segment 7 7 on sabbatical Segment keen but stuck Segment activists Segment first biters Segment Potentials Segment 2 Zeros Segment 1 Segment 0 41

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 Recap What Strategy Silos People How DP Segmentation Tools Integrate So Analyse Journeys Evangelism 43

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 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 Resources Institute of Fundraising Groups: Insight SIG Technology SIG 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 Questions? Find us on LinkedIn Follow us 47