Predictive Donor Value Metrics

PREDICTIVE DONOR METRICS
Predictive Donor
Value Metrics
#12NTCpredict
Daniel Atherton, CCAH
Brenna Holmes, CCAH
Mathew Grimm, EDF
John Clese, AVECTRA

Evaluate This Session!
Each entry is a chance to win an NTEN engraved iPad!
or Online at www.nten.org/ntc/eval
INSERT
QR CODE
HERE

Agenda
• How do you use data?
• Why predictive data is important
• Offline data examples
• How can we use offline techniques online?
• Data at Environmental Defense Fund
• A-Score: A way to measure constituents
• Questions

Who are we?

What you’ll learn today
• Why predictive data is important
• Some tips for how to gather predictive data
for your constituents
• Examples of how EDF and AVECTRA gather
and use data

How do you use data?

What is predictive data?
• Predictive data is data that allows you to
predict how a constituent will respond to your
direct marketing

Consider this:
• Jane Q. Nondonor signs up for your list
through your organization’s website
• The next week, your board comes to you and
says, “We want to ask our supporters to
dedicate bricks outside our new office. They
will cost $5,000 each.”
• Do you send your brick-dedicating email to
Jane?

I hope not.

Why predictive data is important
• Is Jane likely to give you $5,000 for a brick only a
week after joining your list?
• Is Jane likely to be very early in the “funnel of
engagement” – looking for more information
about your organization and why she should trust
it?
• Might Jane decide that your organization seems
kind of greedy, to be asking for $5,000 so soon?
• Might she think that you don’t even know
anything about her?

How do we know this?
• Data.

• Very few of your constituents are likely to be
major donors
– 0.51% of EDF’s online file is in their major donor
track
• You are likely to be unsuccessful asking a new
constituent for an amount so much higher
than the average for first-time gifts
– The average first-time gift in the past 12 months
for EDF’s file is $66.90

• New users are much MORE likely to interact
with the first few messages they get from you.
– EDF’s average open rate for its welcome series is
30-100% higher than its usual open rates for non-
donor segments
Why would you waste that on a very low-
probability ask?

• In the offline world, where there is real
opportunity cost in contacting a prospective
donor, predictive data is critical.
• In the online world, there is still a hidden
opportunity cost:
– Your time
– The trust of your constituents
– The possibility that constituents will unsubscribe
or “tune out” future emails

BULLETIN: INACTIVITY IS VERY BAD
EMAIL NERDS REPORT
As SPAM filters become harsher and more
responsive, a user ignoring your email because it
doesn’t speak to her is no longer simply an
opportunity cost. It affects your ability to reach
even the users who WANT to read your emails.

• We may claim that we hate the ways
marketers use our online habits to tailor ads
to us – but then we get mad when those ads
seem irrelevant
• The key to establishing trust with your
prospective donors – and to drive interaction
– is to seem like you know what they want to
be asked

Duh, dude. But how?

Cheer up – it’s not that hard.

You may already use predictive data.
• Do you segment your file into non-donors,
low-dollar donors, and high-dollar donors?
• Do you segment your file by recency of gift?
0-12 month, 13-24, 24+?
• Do you use HPC as the basis for your donation
form ask strings?
• Do you try to get a second gift out of first-time
donors within 30 days of that first gift?

If not, you should be.
• These best practices all stem from predictive
data:
– Donors’ giving patterns tend to stay fairly static;
someone whose first gift was $35 is unlikely to
respond to a high-dollar ask.
– HPC-based ask strings are a time-tested best practice
offline, and testing shows that (for most lists) they
produce the best return online, too.
– Donors are MOST likely to make their second gift
within a few weeks of their first – or even to become a
monthly giver.

Speaking of offline…

Learning from offline examples
• Since it costs money to mail a package, your
net is greatly affected by how successful the
package is and how valuable converted donors
become.

Learning from offline examples
• We use predictive metrics in all sorts of ways
offline:
– In what channel/s is/are John Q. Donor most
responsive?
– To what types of campaigns does John give most
often?
– Will John be more valuable over his donor lifespan
if he joins the file via a premium?

Offline examples
• Creating a “TM Track” for donors who are
particularly responsive on the phones
• Noting when particular lists respond better to
certain topics or campaigns and mailing a
higher quantity
• Finding the most likely paths for donors to
become sustainers, and cultivating that path

So how can this work online?

3 steps to predictive data online
1. Gather as much data as possible.
2. Look for patterns in that data.
3. Selectively target constituents based on
which asks will have maximum value.

Step 1: Gather data

Step 1: Gather data
There are three ways to gather data on your
constituents:
1. Automatically through your blast mailer/CRM
• Basic stats like time on file, opens and clicks, donation
history
2. Through an append or file modeling service
• Biographical stats like age and gender; data on social
media use or how users spend their time online
3. The best way: ask for it!

Using surveys to find stuff out

• Ask questions about users as soon as they sign
up for your list
• Ask questions about users when they’re taking
an action or donating
• Send your file surveys a couple times per year
– then ask for a donation when they’re done

• Post-survey donation asks are one of the most
successful, least intrusive ways to convert new
donors and to engage a “tired” file
– People like being asked what they think
– If they think you’re listening to them, people will
think more highly of your organization – and what
you would do with their donation
– Surveys are just like ZIP code, address, cell phone
number – the more info someone is willing to give
you, the better a donor s/he is likely to become

Step 2: Look for patterns

Step 2: Look for patterns
• Become a journalist in your dogged pursuit of
fundraising truth:
– Who
– What
– When
– Where
– Why
– How

Who?
• Who is engaging with your emails?
– Are a small core of dedicated activists driving 90%
of the actions and/or donations?
– Does your file skew old or young? Male or female?

What?
• What is your file interested in?
– Do they prefer to hear about/take action
on/donate to one of your issues over another?
– Do they prefer to sign petitions? Do they prefer to
donate? Do they prefer to share personal stories?

When?
• When does your file engage with you?
– Do they donate more in the morning or the
evening?
– Are they more active if you send an email on a
Monday or a Friday? Are they active on
weekends?
– Do they donate at particular times of year?

Where?
• Where is your file?
– Where does your file live? Are they concentrated
in particular states or cities?
– Where does your file access your content? Do
they use your website? Do they engage mostly
through your emails? How about Facebook and
Twitter?

Why?
• Why do they give to you?
– Do they respond to:
• Institutional asks (“We are rated four stars by Charity
Navigator”)?
• Emotional appeals (“These children need your help”)?
• Efficiency (“94 cents of each dollar go straight to people
in need”)?
• Emergency (“WE NEED THIS RIGHT NOW HELP”)?
• Anger (“Here’s something dumb this idiot said about
us”)?

How?
• Do a few donors give large amounts, or do lots
of donors give small amounts?
• Do your donors respond to renewals, or to
appeals?
• Do they give online after they’ve received a
mail piece or a TM call? Or vice versa?

Step 3: Profit

Target your asks based on the data
• If your file (or donors) are mostly older
women, focus on what’s important to them
• If your file prefers to take action rather than
donate, use more after-action donate asks
• If your file donates more on Mondays…send
more emails on Mondays

Target your asks based on the data
• If you’re a national organization but 50% of
your file lives in California, consider locally-
targeted content
• If part of your file responds more
institutionally and part responds more
emotionally, split up your segments to target
them
• If part of your file responds more to renewals
than to appeals, send them more renewal asks

Environmental Defense Fund

AVECTRA
• A-Score

Why score your constituents?
• Scoring enables you to measure relevant information
about who a given constituent is, how they interact with
your organization and identify key behavioral attributes
• A weighted score relevant to your organization’s mission
and activities helps support smarter, more targeted and
timely engagement activities in a reliable, systematic way
• Use scoring to unveil early indicators of other donors
who are beginning to mirror key characteristics of your
top performers and use this data to intervene more
effectively in the relationship

Why score your constituents?
• Scoring can help identify donors who are
disengaging from your organization by
aggregating and scoring behavioral trends
unnoticeable to the naked eye
• Scoring can replace the herculean task of
multiple queries, reports and analysis to spot
trends within in your donor base

Discovery
• Who are my top constituents?
• What are the activities that people and organizations
do that are meaningful and valuable to you?
• Similarly, which demographic characteristics are
meaningful to you and indicate the importance of a
member or donor?
• Include observed and tracked behavior, activities and
demographics, as well as your anecdotal information,
whether these are in your database or not.

A-Score™ Scales
• A-Score™ is a
composite of
other scales, each
of which
measures
engagement in a
specific category
Social
Participation
Fundraising
Advocacy
A-Score
Events

View Scoring Trend Over Time

Questions?

Predictive Donor Value Metrics

Recommended

Recommended

More Related Content

What's hot

What's hot (15)

Similar to Predictive Donor Value Metrics

Similar to Predictive Donor Value Metrics (20)

Recently uploaded

Recently uploaded (20)

Predictive Donor Value Metrics