3 quick hacks to be more data-driven

This post originally appeared on Gravyty.

People in fundraising are always asking us if there is a quick fix — a silver bullet. I’m afraid to report: there isn’t, contrary to what some say. Fear not though, with the availability of data and power of experimentation, there are a few things you might try.

January can be a quiet month. So, this may be a good opportunity for your organization to explore data-driven decision making if you never have – or brush up on useful techniques. It also gives you a way to begin (re)introducing data to your colleagues in a small and meaningful way. And better yet, these are all things you can accomplish in a single day.

Those with the luxury of a manageable prospect list find themselves manually reviewing their prospects, identifying their own priorities based on things like lifetime giving and location. But how do you do this if you’re managing an annual fund, a larger subgroup, or a constituency list thousands long?

Here are three things that come to mind:


Get a lay of the land and go beyond treating everyone as a donor, LYBUNT, or SYBUNT. Start creating actionable segments by forming a single score based on recency, frequency, and monetary attributes. Secondly, you can easily share and communicate your findings by creating a chart.

I have some Excel functions you can use that looks at recency and frequency — if you need the monetary, let me know. (At the time, we were only concerned with participation, how much someone gave was irrelevant; and the fiscal year ran July 1 – June 30.) You can find these functions here on my site. It requires three pieces of information: their year of graduation, the number of years of giving, and the date of their last gift. (For non-universities, year of graduation could be substituted with the year when a constituent first engaged with your organization.)

Use this score to visualize the information. This can be a powerful way to share your work with colleagues without getting wonky on them with numbers and formulas.

Here’s an example based on one I previously did for an organization:


Frequency and recency scores are calculated and mapped. At the intersection of reach score is a number, representative of the number of people with that score. I added color to help illustrate to my team what this means. In the bottom left corner are the folks who have never given. The upper right corner are folks who have never missed their annual gift.

The next time someone says, “I’m not worried, those people always give”, now you can have a better idea who those people are — the green area. Maybe your next communication acknowledges someone’s loyalty and shows your appreciation by saying “thank you”.

You can check out Fundraising Analytics by Joshua Birkholz as an additional resource.


You are sitting on a goldmine of information. Take email response data, for example. With a modern email delivery system, you can find:

who has received which content

who opened the email

who clicked which calls-to-action

So what does that mean?

Let’s ignore the open rate for now and focus on the click rate. What does this tell us? It signifies to you that a particular call-to-action or content was relevant to the individual. How can we use that to our advantage? Think of a time you may have shopped online, say with Amazon. They give you recommendations, “People who bought this also bought…” What if we could do the same thing for our donors?

Looking back at a specific email, you see there was an article on a recent event. We have a list of those who clicked on said article. Now, connect that content to a fundraising priority — or as a specific example within a general, unrestricted fund — and you’ve got yourself a targeted audience and message. Make your call-to-action (CTA) about support this priority and link to the online giving page.

Download and open (in Excel) your email response data for the email message you’re focusing on. Be sure to include the email address for each record.

Keep only those who clicked on a particular CTA from the email, i.e. sort by column containing the clicked-on data and remove anything that isn’t the CTA you’re targeting.

Use Excel and the VLOOKUP function to merge your email response data with your giving data.

Keep only those people who have not yet supported your organization, i.e. sort by current year giving and where this is greater than $0.00 remove them.

Craft the new message using the particular subject you determined, make a clear CTA asking for their support, and send this message to this select group of people. (Know that this could be a small list of people and that is okay.)

Look at the donation and email response data 36-hours after the message has been delivered. What did you discover?

IMPORTANT: As a more active follow up, you can use the new click data from this new message to develop a call list. For those who clicked on the CTA and did not make a gift yet, follow up with a friendly phone call. You’ve just used data to inform your call list for the day. It may only be 11 people, but those are 11 people who are engaged with your organization and will more than likely give you the time of day.


Another data point that often goes overlooked is the gift purpose. For those LYBUNTs and SYBUNTs, your donation system probably tracks where they directed their contribution.

Identify a list of those purposes

Relate — directly or indirectly — those gift purposes with existing purposes

Craft messaging around each of them, speaking directly to the donor prospect and acknowledging their past support, its impact on your organization, and how they can continue having that impact.

Gone is the day where we need to talk about the weather and the changing seasons in our introductory paragraph. You can confidently use your data to help deepen your relationship with that donor by getting directly to what matters to them. They gave to an art gallery last spring? Share with them the impact their gift had and how they can continue that impact this year.

You and your organization may benefit by more substantial tools and predictive analytics. But in the meantime, these are three low-tech wins you can pull off. You might even win over skeptical colleagues by demonstrating the power of your own data.

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