In direct mail fundraising, getting your targeting right is everything. Send to the wrong people, and you waste budget, lower your ROI, and risk damaging donor relationships with irrelevant asks.
Yet most nonprofits still rely on traditional segmentation to target their fundraising campaigns - grouping donors into broad categories based on simple rules like recency, frequency, and value/gift size. Simply put, traditional segmentation is a blunt tool that inevitably lumps high-potential donors together with those unlikely to give, creating inefficiencies, reducing response rates and missing opportunities to engage other donors altogether.
AI changes that.
By analysing the data and signals donors send through every interaction, AI gives fundraisers the power to know who to engage, when to reach out, and how to personalise the ask.
The result? Every dollar works harder, every donor feels valued, and every campaign is set up for maximum impact from day one.
In this blog, we’ll look at why traditional segmentation and calendar-based approaches fall short — and what an audience-first strategy powered by AI means for your annual giving and direct mail results.
The Limits of Traditional Segmentation
For decades, direct mail targeting has been built on traditional segmentation. You take your database, apply broad rules such as recency, frequency, and monetary value (RFM), and group donors together based on these simple yet arbitrary criteria.
As a result, fundraisers often treat very different people with diverse interests, motivations, and readiness to give in exactly the same way.
Imagine selecting all donors who gave $50 – $200 in the last 18 months for your next appeal…
Some supporters will be primed and ready to give again. Others may have donated once and have no intention of supporting further. Some only want to fund specific programs or areas of your work, while others dislike direct mail entirely and only respond to email. A few even have the capacity to give at much higher levels ifi stewarded well.
However, traditional segmentation overlooks these nuances and treats them all the same, sending the same ask or message at the same time and in the same way.
This lack of precision and personalization creates two major problems for fundraisers:
- First, it drives up costs - wasting money on printing and postage for low-propensity donors who were unlikely to respond in the first place.
- Second, it undermines the donor experience - irrelevant or poorly timed asks make supporters feel unseen and unappreciated, increasing the risk of disengagement.
Put simply, traditional segmentation is too broad and blunt of a tool. It often includes donors with no interest or capacity to give, driving up mailing costs, wasting budget, lowering ROI, and causing donor fatigue due to over mailing. Even the best creative fails if the campaign reaches the WRONG people.
Why the Calendar-Based, ‘Send to All’ Approach Doesn't Work Anymore Too
Not only does traditional segmentation treat broad and diverse supporters the same way, essentially reducing them to segments rather than individuals, it also locks fundraisers into rigid, calendar-based campaigns.
When it comes to individual giving and direct marketing programs, most organizations still follow a campaign-driven fundraising model. The defining feature of this model is that campaigns are planned months in advance, locked to an internal calendar, and rolled out according to the nonprofit’s schedule - not when the donor feels inspired or ready to give.
This model isn’t inherently bad, but it’s increasingly out of step with today’s environment and evolving donor expectations.
With direct mail costs rising, every piece sent needs to count and wasted outreach just cuts deeper into ROI. At the same time, donor retention rates are worsening, as supporters receive more appeals that feel irrelevant, mistimed, or impersonal. In short: sticking rigidly to the calendar costs more, delivers less, and risks turning donors off at a time when retaining them has never been harder.
But what if fundraising didn’t have to work this way?
What if, instead of the calendar dictating your campaigns, your donors did?
Imagine shifting from static, one-size-fits-all segmentation to a responsive approach where outreach happens when donors are most likely to engage and not just when the nonprofit’s agenda and calendar say it’s time to give.
But moving away from a campaign-driven model is only possible with the right tools, like AI.
AI-powered data insights help fundraisers replace the old “spray and pray” approach of fundraising with precise targeting that finds donors most likely to respond at that time - whether that means reactivating lapsed supporters, upgrading loyal ones, or finding the right moment to reach out.
This is how fundraisers can finally deliver true personalization at scale - treating donors as individuals, but doing it across entire campaigns without adding to your workload.
Understanding Audience-First Fundraising: From Campaign-Centric to Donor-Centric at Scale
Audience-first fundraising flips the old approach to direct response campaign targeting on its head.
What audience-first fundraising with AI really does is evolve and replace the limitations of traditional segmentation and send-all campaigns by moving the starting point from the campaign to the donor.
Traditional segmentation starts with your organisation’s plan (“We’re sending an appeal in September”) and then uses blunt criteria to choose who gets it. Audience-first starts with the data signals and behaviours of individual donors and builds precision audiences around those insights, ensuring every campaign is built for the people most likely to respond.
The audience-first fundraising approach starts with the donor — their behaviour, preferences, and where they are in their journey with your organisation. AI analyzes your donor data to find patterns and identify signals that reveal who is most likely to respond to a particular type of ask.
From there, you can build hyper-targeted audiences of like-minded supporters — people with shared giving behaviours, interests, or engagement patterns — and deliver personalised campaigns that speak directly to their motivations and the ways they like to give.
This approach means:
- Better donor experiences – Donors receive asks that feel relevant and timely.
- Less waste – You only mail or contact those with a high likelihood of responding.
- More impact at scale – You can treat donors as individuals across thousands of records, something impossible with manual segmentation.
As a result, every direct response campaign is built for maximum relevance and return, and your targeting is powered by the needs and behaviours of your donors, not just the timing of your appeals or campaign calendar.
AI Powers Makes Audience-First Fundraising Possible, at Scale
Audience-first fundraising means putting donors and their needs, not your organization’s fundraising calendar, at the centre of every campaign. The challenge with this approach is scale. To do this well, fundraisers need to be able to analyze the signals donors are sending through their giving and engagement data, interpret them, and translate them into something meaningful and actionable. That’s where AI comes in. By processing vast amounts of data with speed and efficiency, AI turns scattered donor data and activity into clear insights - helping you build precision audiences and deliver personalised outreach at scale.
In traditional segmentation in contrast, fundraisers only use the surface-level three data points (recency, frequency, and gift amount). It’s like looking only at the tip of an iceberg while a vast body of data and insight remains hidden beneath the surface (including event engagement, channel preferences, recurring giving patterns, payment methods, response timing, postcodes and more).
Dataro’s AI models surface all of that rich data and engagement signals previously hidden from fundraisers.
By analysing ALL of the available data in context across systems, channels, and touchpoints, AI uncovers the patterns that reveal donor motivations and intent. It distils thousands of signals into practical insights, like who is most likely to give to your next appeal, which channel they’ll respond on, or whether they’re ready for a higher ask. And it's these insights make it possible to build precision audiences and deliver personalised campaigns at scale - campaigns that feel timely, relevant, and built around each donor’s journey.
Understanding HOW AI Works in the Context of Predictive Modeling
In fundraising, when we talk about AI (artificial intelligence) we really mean machine learning, complex algorithms capable of analysing vast amounts of donor data, uncovering patterns, and predicting future behaviour.
When we talk about predictive AI in fundraising, what we really mean is machine learning (ML). The simplest way to think about ML is pattern analysis. It’s the process of analysing patterns in data using advanced algorithms to predict a future outcome or behaviour.
Here’s how that process works in practice:
- Feed the data in — everything you already hold in your CRM and related systems: giving history, engagement, response behaviour, and more.
- Pinpoint the outcome you want to predict — for example, whether someone will respond to a direct mail appeal or give a gift over $500.
- Find the patterns — the model analyses all past examples of that action and learns which combinations of data points are most predictive.
- Apply those learnings — once the patterns are clear, the model can identify other supporters who are showing similar signals in their engagement data and are therefore the best fit for that kind of ask.
In short, machine learning takes all the noise and complexity of your donor data and turns it into actionable predictions. And instead of only relying on the tip of the iceberg to target your direct mail or other fundraising campaigns, AI helps fundraisers see the whole picture of a donor's journey and engagement. Instead of guessing, AI-predictive insights help fundraisers know exactly which donors are most likely to take the action they care about, whether that’s making a donation to the upcoming appeal, upgrading their gift, reactivating after lapsing, or looking like a good prospect to steward for a midlevel or major gift.
Understanding HOW AI Works in the Context of Campaign Audience Building
Here’s how it changes the game for direct mail and other campaigns:
- Interprets donor signals — analysing thousands of data points, not just RFM.
- Identifies predictive patterns — like the 146 factors Dataro’s models use to predict campaign response.
- Builds a 360° predictive profile for every supporter — showing which programs, channels, and asks are most likely to resonate with each individual.
- Assigns live propensity scores — continuously updated predictions that show which donors are most likely to give, and when.
- Enables hyper-targeted audiences — supporters who are not just likely to give, but likely to give now, via the right channel.
The output is a living, predictive profile of every supporter in your database. That means you don’t just see who gave in the past - you see who is most likely to give to your next appeal, how much you should ask for, which channel to ask on, and even whether they’re likely to give more than $500. Beyond appeals, Dataro’s propensity models also provide scores across other fundraising asks, helping you decide where each donor fits best in their journey, so every interaction feels personal, timely, and relevant.
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