datenanalyse für ngo/npo
DESCRIPTION
Folien zum Referat von Duane Raymond an der Kampaweb Soiree zum Thema: Analyse von Unterstützer-Daten.TRANSCRIPT
Organised and hosted by Kampaweb.ch
@fairsay #ngodata [email protected]
Strategic Data Analysis for NGOs Getting more out of your advocacy and fundraising
By Duane Raymond [email protected] @fairsay Hashtag: #ngodata Zurich, Switzerland 9 Feb 2012
Who are you? Data for what?
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@fairsay #ngodata [email protected]
…and usually leads to more questions.
Data answers our questions…
Data is more important than ever
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…helps us make informed decisions…
Trevor Rickard
We need a strategy FIRST
1. Analysis for who? Public? Senior managers? Peers? 2. What are your organisational objectives, goals and
priorities? 3. How do you know you are progressing / achieving them? 4. What model(s) (tactics) will you be using? 5. What do you need to learn from an analysis? 6. What indicators will help you learn what is needed? 7. What data is needed for the indicators? 8. Where / how do you get that data? 9. What is ‘good’ or ‘bad’?
Throughout this process we ignore the data.
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@fairsay #ngodata [email protected]
Analysis may require a few sources
• Tracking: techniques for knowing where people start their experience with you and how far along the process they get
• Split-testing: techniques for determining what factors get the best results with a given audience
• Surveying/Polling: Asking for responses to questions • Analysis: understanding what is happening online,
what is insightful and what could be improved • Reporting: selecting findings that relate to the
ambitions, goals and objectives of a given stakeholder
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Case: typical campaigning analysis
1. For who: managers and peers
2. Campaign impact + retain and recruit supporters
3. Impact: Win? Progress? Mobilisation? Retain: Repeat active. Recruit: new supporters.
4. Model: call-to-action
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@fairsay #ngodata [email protected]
Model: call-to-action
Usually email Usually
web form via email +
other social media
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@fairsay #ngodata [email protected] http://fairsay.com/hypevsreality2010
0% 10% 20% 30% 40% 50% 60%
via Mobile Site via Widgets
via Facebook App via Facebook Links
via Twitter Links via YouTube Links
via Flickr Links via Habbo
via Stardoll via Online Ads
via Search Email + Direct
Source of 1GOAL eAction Supporters % of Total
11% 4%
12% 2% 1% 2% 0% 2% 6% 2%
10% 49%
Email still best for calls-to-action
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Case: typical campaigning analysis
5. Learn: Are we on-track? Where are our gaps?
6. Impact indicators: target movement Other indicators: participation ratio, activity levels, recruitment levels/ratio, etc.
7. Impact data needed: target movement Other data needed: - what was promoted, how and to who - who responded to what was promoted
8. Data source: campaigners (impact) + email & action tool
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Qualitative & quantitative
• Qualitative: impact, design, usability
• Quantitative: rates, counts
When doing data analysis • most is quantitative • the qualitative
– adds context – helps explain the findings
I will focus on quantitative findings today
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Notice what I haven’t mentioned?
• Google Analytics / web stats • Emailing open / click rates
I focus first on the end-to-end findings – not the middle steps
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Simple findings: where are we now
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Simple findings: activity patterns
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Comparisons: ratios and volume
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High and low points
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Ratios to ‘level’ data
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Repeat activity levels
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Related indicators
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Segments: Journey indicators
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Lifespan: time to lapse
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Sector benchmarks
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@fairsay #ngodata [email protected]
Analysis: Key Indicators
Key Indicators Best Practice Level Participation rate (of email received) 25% (35% with chaser) Attraction Rate (of actions) 33% Opt-in rate (of new) 55% Recruitment rate (of actions) 17% Cost / recruit (variable costs) 3-5 CHF Avg. donation value 21 CHF Conversion to donors 0.5%
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@fairsay #ngodata [email protected]
Analysis: Key Formulas
Key Indicator Formulas Participation Rate # Emailed who acted / # Emails received Attraction Rate # New / # Unique Actions Opt-in Rate # New opt-ins / # new Recruitment Rate Attraction rate x Opt-in rate Cost / recruit Variable costs / # New opt-ins Avg. donation value Donation Value / # Donors Donor conversion # Donors / # Unique Actions
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How: blood, sweat and tears
Process 1. Extract 2. Standardise 3. Clean 4. Import 5. Explore 6. Relate 7. Query 8. Visualise
Volume Picks Tools • Small (MB):
spread sheet
• Medium-large (GB): relational database
• Massive (TB): hadoop, etc.
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How: relate the data
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Emailing Recipients
Emailing
Recipient
Open Date
Click Date
Bounce Date
Unsubscribe Date
Action Participants
Action
Participant
Action Date
Action Source
Action Referrer
Email-Action Link
Emailing
Action
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How: query the data
SQL SELECT COUNT(Participants) FROM Actions WHERE Action = ‘Apple Labour Rights’ ..or use visual query tool (e.g. MS Access, Navicat)
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@fairsay #ngodata [email protected]
Split-testing is best place to start
Do split testing and analysis with every emailing – and act on the lessons learned
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@fairsay #ngodata [email protected]
Split-testing: indicators Email
Web
Other # Sent
% Received
% Opened
% Clicked
% Landed
% Completed
% Help promote
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Great campaigning matters
Having great advocacy / fundraising campaigns makes more difference than anything you learn from data
analysis.
Solid research and strategy ensures data analysis will help you make the
most of the campaign.
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So what next?
1. Plan great campaigns 2. Have activity that is recorded 3. Have systems for emailing and
actions data (e.g. CRM) 4. Analyse how you are performing 5. Change what you are doing &
re-analyse @fairsay #ngodata [email protected] 32
@fairsay #ngodata [email protected]
Questions? Comments?
Join eCampaigning Forum 2012: 21-22 March, Oxford, UK http://fairsay.com/ecf12 Learn more at • 2009 eCampaigning Review: http://fairsay.com/ecr09 • Join the eCampaigning Community: fairsay.com/ecflist • FairSay Blog: http://fairsay.com/blog • Kampaweb: http://kampaweb.ch/news Contact me: Duane Raymond: [email protected]
Skype/ Twitter: fairsay
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