Strategic role of business intelligence
Blackbaud had been the dominant software provider to US nonprofits for three decades. But the ground was shifting. Cheaper, more modern CRM alternatives, Salesforce.org chief among them, were pulling customers out of the ecosystem. And when a nonprofit left the CRM, they typically left everything.
The small, startup-like division I joined had identified a response more nuanced than building a better CRM: if we could become indispensable on the data side, we could retain customer relationships even when they defected on the software. Nonprofits were increasingly fragmented across multiple platforms, spending significant time and consulting budget trying to piece together a coherent picture of their donors and fundraising performance.
We set out to solve that with a software-agnostic BI platform, one that could unify donation data from any source and give fundraising directors, marketing analysts, and development officers a single place to understand their revenue, donor retention, direct marketing results, and long-term giving behavior.
There was no playbook. We were a product team embedded inside a services division of a CRM company, building something the organization had never attempted, under pressure to be profitable in year one. Our internal peers doubted the market would accept a slim first release. We launched it anyway.
VantagePoint: donor intelligence at a glance
VantagePoint was a tight collaboration between a small, close-knit team: our chief data architect designed the universal data model that made multi-source unification possible, our BI developer and backend team built the infrastructure, and I served as the connective tissue, translating customer needs into dashboard designs, data requirements, and visualization logic, then working closely with the architect to ensure the metrics we needed could be produced within the constraints of the universal model.
My role shifted constantly depending on what the product needed. On any given week I might be running customer discovery, sketching dashboard layouts, writing detailed requirements, validating calculations in Tableau, or presenting to a sales prospect. The goal was always to give fundraising leaders an immediate read on organizational health without requiring them to be analysts.
The first version of VantagePoint shipped in under six months with five dashboards covering the core use cases for direct marketing managers and fundraising directors. Early adoption gave us the user feedback, market validation, and internal credibility we needed to build the roadmap forward.
The entry point for leadership: annual revenue and donor counts tracked against goal, quarterly breakdown, donor status split (new, reactivated, retained), 5-year trend, and a year-over-year scorecard, all in a single view. Clicking any quarter drills into the Revenue Summary below.
Full-year fundraising performance: revenue, cost, and net revenue tracked monthly with this-year vs. last-year comparisons, donor retention rate over time, and retained/reactivated/new donor counts by month, the dashboard that drives direct marketing strategy.
Ten campaign types compared simultaneously across retention, giving level, frequency, recency, change in giving, revenue per donor, and 5-year acquisition trend, all in a single scannable layout. Hovering any data point surfaces full detail.
Rolling up our sleeves when the situation changed
Building a new product inside an established organization means navigating conditions you didn’t create. When our team’s working process became an obstacle, a rigid delivery approach that prevented us from learning through doing and responding to what we discovered, I raised the issue with my manager and documented my concerns. It took persistence, and it was ultimately the courage of a colleague who escalated further that led to change. Once leadership cleared the path, our team redesigned the process together.
We implemented scrum with our business analyst stepping into the scrum master role alongside me. We built a sprint cadence with bi-weekly reviews and gave ourselves the space to iterate. The result was a 50% improvement in team productivity and 65% reduction in time from idea to shippable increment, not because scrum is magic, but because the team finally had room to work the way we knew how.
That capacity for adaptation was tested again in our second year when leadership cut the budget and our outsourced staff mid-build on three new analysis modules. Rather than slow down, I changed how I worked. With limited developer hours available, I built every prototype myself first, working through each module’s full functionality in Tableau, validating calculations, resolving design questions before any developer time was spent. When I handed work over, the specs were exact because I had already lived through every decision they would need to make.
I flexed into whatever role the team needed most to get the product shipped.
Prototyping our way to the pipeline
The budget cut hit while several significant enterprise deals were still in progress, sales cycles of 6–9 months. Two prospects had requirements our product didn’t yet fully address, and walking away from them wasn’t an option for a team operating under a first-year profitability mandate.
One large nonprofit ran a sophisticated monthly giving program and needed to see that we truly understood the complexity of sustainer analysis, not just month-over-month revenue, but the ability to normalize donors to a common time scale regardless of when they were acquired, so that retention rates and long-term value could be analyzed together by acquisition channel, payment type, and donation amount. This kind of cohort analysis requires non-trivial data transformation, and even our expert Tableau developer found the calculations difficult to build correctly so that the dashboard would update properly when filters were applied. We worked through it together, and the result was a working analytical workspace (not a mockup) that let the prospect slice and dice their historical donor data in ways their own team couldn’t replicate.
A second prospect needed a peer-to-peer giving module still in development. I used internal benchmarking data we already had to build working Tableau dashboards showing exactly how we’d analyze their events results. The pipeline wasn’t built yet, but the analysis was real and the dashboards were live.
At least one of those deals closed. For a small internal team, a six-figure enterprise contract wasn’t just revenue, it was the proof of concept that justified our existence and opened the door to larger relationships as customers grew.
Taking the product to the customer community
“The New Sherlock Holmes: Using BI to Shed Light on Your Direct Response Opportunities”
Co-presented a practitioner session for nonprofit fundraising professionals on translating raw data into donor intelligence, with worked examples on sustainer retention, cross-channel campaign performance, and donor acquisition optimization.
Speaking at bbcon meant stepping outside the product team and into the shoes of the fundraising professionals we were building for. Co-presenting with a colleague, we walked a room of practitioners through a 7-step framework for putting BI to work in direct response fundraising, grounding every step in examples drawn from real nonprofit challenges.
A three-year roadmap built on market evidence
Alongside building and shipping, I developed a strategic roadmap grounded in market research. Our internal analysis of enterprise CRM RFPs showed that self-serve reporting and BI appeared in roughly 70% of requests, with consistent themes around ease of use and freedom from vendor dependency. I worked with the team to define four customer personas and a tiered product model (Starter, Essentials, Pro) to serve each. The three-year roadmap organized our work around six strategic themes:
Donor health at a glance: giving distribution by contribution level, recency segmentation, and change in giving since first gift, the view that informs upgrade and retention strategy.
Every gift plotted by date and channel, color-coded by solicitation type. Clicking any channel highlights its gifts across the timeline, designed for identifying multi-channel giving patterns.
Campaign-level analysis with response rates, average gift, cost per dollar raised, top performing sources, and daily and cumulative performance by initiative.
All dashboards built on anonymized composite demo data.