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Predictive Donor Value Metrics#12NTCpredict

Daniel Atherton, CCAHBrenna Holmes, CCAHMathew Grimm, EDFJohn Clese, AVECTRA

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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

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Who are we?

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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

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How do you use data?

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What is predictive data?

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

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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?

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I hope not.

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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?

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How do we know this?

• Data.

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How do we know this?

• 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

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How do we know this?

• 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?

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Why predictive data is important

• 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

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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.

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Why predictive data is important

• 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

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Duh, dude. But how?

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Cheer up – it’s not that hard.

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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?

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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.

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Speaking of offline…

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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.

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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?

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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

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So how can this work online?

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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.

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Step 1: Gather data

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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!

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Using surveys to find stuff out

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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

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Using surveys to find stuff out

• 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

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Step 2: Look for patterns

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Step 2: Look for patterns

• Become a journalist in your dogged pursuit of fundraising truth:– Who– What– When– Where– Why– How

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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?

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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?

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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?

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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?

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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”)?

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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?

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Step 3: Profit

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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

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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

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Environmental Defense Fund

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AVECTRA

• A-Score

SCORE

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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

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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

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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.

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A-Score™ Scales

• A-Score™ is a composite of other scales, each of which measures engagement in a specific category

SocialParticipation

Fundraising

Advocacy

A-Score

Events

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View Scoring Trend Over Time

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Questions?