Re-inventing F2F: From data automation to insights · •Comparing performance of different agencies, recruiters, other… •Testing an measuring -> adapting tactics and strategy

IoF F2F Fundraising Conference 2017 20th March 2017

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© 2017 The DonorVoice

http://www.thedonorvoice.com

Re-inventing F2F:

From data automation to insights(a case from Amnesty International

Belgium – Flanders)

Ilja De Coster, MInstF(cert)

The DonorVoice

• Director of Donor

Relationship Management

and Data Strategy.

Amnesty International (BE-FL)

• Fundraising Data Strategist.

Co-architect of an ambitious

fundraising growth plan.

15 years experience in fundraising,

as officer in charities, as well as consultant.

Warning: some call Ilja a data geek.


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Where did those

new F2F

recruitments go?

Till 2014

• 6 months retention, based

on gross sign ups: 50-60%.

• Average start up time since

recruitment: 65-93 days.

• Estimated missed income

due to inefficient data

processing: 8.000-12.000

euros, each month again.

Since September 2015

• 6 months retention, based

on gross sign ups: 75-80%.

• Average start up time since

recruitment: 25-35 days.

• Estimated missed income

due to inefficient data

processing: 0 euros, each

month again.


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Focus on quality

•Quality indicators

•Analytics based Training

•Pay for quality

Focus on

donor

•Asking feedback

•Acting upon feedback

•Complaints handling

Focus on

data

•Processing speed

•Looking a all data

•Live analytics and monitoring


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• Fast start up of welcome communications.

• Fast start up of new direct debits.

• Fast look up donors calling in (early stopping, complains).

• Monitoring what is happening now, for early intervention.

Date of recruitment

Date of entry in CRM


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•Managing the agencies data management.

•Don’t trust agency reporting, even as good partners.

•We report back to agencies their results.Raw data

•All data is ours, no filtering by agencies.

•We want it all: also no shows, changes welcome calls,…

•We want it detailed: recruiters, locations, recruitment context, data about conversation,…

Data points

•What is really happening?

•What parameters influence quality?

•Comparing performance of different agencies, recruiters, other…

•Testing an measuring -> adapting tactics and strategy.

Advanced analytics

Building internal data skills

Ongoing data platform development (Agile)

Open source

software

Code free & self service

tools

Linking different software systems

Fundraisers managing programmers

Back office staff time

Focus on automation

& work efficiency

Investment recruitment volume &

technology


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Open source CRM

focused for non-profits

Code free data

processing and analytics

Self service

business intelligence


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donor 1 18

donor 2 18

donor 3 40

donor 4 28

donor 5 40

donor 6 19

donor 7 21

donor 8 19

donor 9 57

donor 10 60

donor 11 18

donor 12 18

donor 13 47

donor 14 45

donor 15 19

donor 16 20

donor 17 50

donor 18 48

donor 19 50

donor 20 23

Average ≠ median


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Commitment

Feedback

Satisfaction


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Act upon

donor

feedback

Immediate thank you or apology

Adjusting recruiting

tactics

Customised follow up and donor

journey

Coaching individual recruiters


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Current Approach Reality

26% The specific recruitment campaign

17% The individual fundraiser

16%

The amount a donor signups for on the

street

13% The recruitment agency

10% The gender of the recruiter

10%

The difference between amount at signup

and amount after welcome call

9%

The number of days delay between

recruitment date and welcome call date

20%

Donor’s Loyalty to Charity (Commitment

Score™)

19% Donor satisfaction with F2F experience

18%

The age of the donor at the day of

recruitment

7% The specific recruitment campaign

4%

The amount a donor signup for on the

street

4% The recruitment agency

3% The gender of the recruiter

3%

The difference between amount at signup

and amount after welcome call

3%

The number of days delay between

recruitment date and welcome call date

1.

1.

2.

3.

4.

Not just any statistical

model calculated ages ago

on someone else's data.

A statistical model

recalculated everyday, on

our own actual data.


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Commitment scores at recruitment

do predict retention after 9 months

Start amounts at recruitment don’t

predict retention after 9 months


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Predicting within a week

after recruitment, who will

leave in 3-6 months.

• With commitment and

feedback: 85%-90% accuracy.

• Without: 55%-60% accuracy.

Knowing this, what tailor-

made predActions can we

do incorporating insights

from behavioural science?


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Do we need to intervene today?


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• Recruiter training:

– Spending more time to it.

– Ongoing content updates.

– Reflection on data analytics insights.

– Reflection on donor feedback.

• Mystery shopping.

• Complaints follow up.

• A dedicated officer…


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Understanding the factors that drive retention quality.

Manage your F2F program based on quality insights.

Better retention & Paying for quality= Better Return on Investment.


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How much all this will cost?

The question is how much it

costs not to do all this…


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In-depth, live monitoring and analytics really focussing on quality,

creates organisational trust needed to make significant

fundraising investments.

• Ilja De Coster, MInstF(cert)

• Director of Donor Relationship Management

and Data Strategy.

• [email protected]

• http://www.thedonorvoice.com

Documents

Re-inventing F2F: From data automation to insights · •Comparing performance of different agencies, recruiters, other… •Testing an measuring -> adapting tactics and strategy