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Top ten tips for driving fundraising with your data

(aka) how I learned to love data

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Who we are

Steve ThomasManaging Director, Purple Visionwww.purple-vision.com@stevethomas393 @purple_vision

Dawn VarleyDirector of Marketing and Fundraising, League Against Cruel Sportswww.league.org.uk @nfpdawnv

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Contents

What• Strategy• Silos• People

How• DP• Segmentation• Tools• Integrate

So• Analyse• Journeys• Evangelism

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1. Know what you’ve got

• What data?• Who decides collection/coding?• Who knows it?• Org retains info?

Data must be driven by strategy

The classic donor pyramid

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Fundraising 101.

But *completely useless* if you don’t know what the data says…

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2. Avoid keeping data in silos

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Data map

Donations RG

CMS

Donor databaseEvents |Trust & Statutory | Stakeholders | Members

Data import

layer

API

MediaPolitical contacts

Online Advocacy

Campaign emailsPetitions

MPs

Off-line capture

Data house

Bulk Email

Donations SG

Retail

Complaints

Operations

HRSurvey

Volun-teers

Fin-ance

Text donation

Social mediamonitor and broadcast

Website

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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…

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

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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?

The key to success is…

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5. Recognise diversity in data

One size doesn’t fit all

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What is Segmentation?

Classification of the population into subgroups that are:

• Distinguishable• Identifiable• Manageable• Fit for purpose

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Why segment?

• Make targeting more appropriate to audience• Avoid scattergun communications• Protect against unsubscribes and lapsing• Makes internal expectations realistic

Classic segmentation - RFV

Frequency

Recency

Value

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Creating segments

Frequency

Recency

Value

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Creating segments

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4

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67

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Frequency

Recency

Value

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Bases for Segmentation

• Types of information to collect to enable better segmentation:• Comms preferences• Format/media type• Event attendance• Frequency of contact• Purchases

• Supporter category• Reason for support• How old are they?• How loyal are they?• Where in life cycle?• Where do we want to

take them?

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Choose data relevant to your strategy

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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…

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7. Integrate

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7. Integrate

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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

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Systems integration

Data map

Donations RG

CMS

Donor databaseEvents |Trust & Statutory | Stakeholders | Members

Data import

layer

API

MediaPolitical contacts

Online Advocacy

Campaign emailsPetitions

MPs

Off-line capture

Data house

Bulk Email

Donations SG

Retail

Complaints

Operations

HR

Survey

Volun-teers

Fin-ance

Text donation

Social mediamonitor and broadcast

Website

Website

Donor DatabaseEvents |Trust &

Statutory | Stakeholders | Members

Data Import

Layer

API

Media Directory

Political contacts

Online AdvocacyCampaign emails

PetitionsMPs

Off-line capture

Data house

Bulk EmailBulk emailSegmentationNewsletter Design

Fin-ance

CMS Forms, HR, Volunteers

News, Forums

Online Fundraising

Supporter Portals, Donor Journeys

Events & P2PE-commerce

Data Warehouse

API Data Tools

Complaints

Volun-teers HRRetail

Operations

Social monitor and Broadcast

Text donation

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Data Analytics & Reporting

Data warehouse

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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

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Look alike logic

Universe

Your Database

Your Sector

Non-profit supporters

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Profile variables

• Income• Housing Tenure• Spending Power• Education• Occupation• Social Grade

• Age• Children• Household Size• Property Type• Urbanicity• Retail Accessibility

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Profile Model – closeness of fitSegment 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)

Profile Model – closeness of fitAssembli Model Customers Base Penetration Z-Score Index       

Counts % Counts % %       0 100 200  

Segments    

  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          

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Segment geography

Furthest fit

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Closest fit

BUT use past data analysis/organisational knowledge to inform strategy too!

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9. So – Supporter Journeys

First Gift

Became committed

giver

Joined membership

Became committed

giver

Volunteered

Volunteered

Legacy Pledge

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First engagement = purchase

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First engagement = committed gift

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Loyalty ladders

Segment 1

Segment 2Segment 3

Segment 4Segment 5

Segment 6

Segment 7

66714183

2295

2525

53112790

7119

9457

Segment 0

ZerosPotentials

first biters

activists

keen but stuck

7 on sabbatical

7 on holiday

super close

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Segment shifting

7 0.12 0.47 1.10 1.85 3.25 9.20 11.08 88.74

6 79.48 4.62

5 0.01 87.23 7.16 4.62

4 96.75 3.57 2.27

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

0 1 2 3 4 5 6 7

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

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Insight – snakes and ladders

Segment 1

Segment 2

Segment 3

Segment 4

Segment 5Segment 6

Segment 7

66492213

3111

39763301

35118845

7250

Segment 0

Zeros

Potentialsfirst biters

activists

keen but stuck

7 on sabbatical

7 on holiday

super close

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10. Be a data evangelistNow 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

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Recap…

What• Strategy• Silos• People

How• DP• Segmentation• Tools• Integrate

So• Analyse• Journeys• Evangelism

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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 external4. Keep relationships clean & respectful. How

you apply data protection & cleansing is key.5. Know when and how to segment

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Top Ten Tips

6. Be aware how your data is managed7. Discover how to bring it all together8. So learn from your data – report, analyse,

question – and use it to inform decisions9. 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.

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ResourcesInstitute 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

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Questions?

steve.thomas@purple-vision.comdawnvarley@league.org.uk

Find us on LinkedIn

Follow us on twitter @nfpdawnv @stevethomas393