Datenanalyse für NGO/NPO

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?

@fairsay #ngodata [email protected] 2


Data used to be scarce…

Rémih 3


Now is it overwhelming

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…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.



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



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


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


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


Simple findings: where are we now


Simple findings: activity patterns


Comparisons: ratios and volume


High and low points


Ratios to ‘level’ data

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Repeat activity levels


Related indicators

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Segments: Journey indicators


Lifespan: time to lapse


Sector benchmarks



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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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.


How: relate the data


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)



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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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.


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


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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Documents

Datenanalyse für NGO/NPO