Membership nonprofits have always faced unique challenges along the data transformation path — but launching and completing successful data transformation has never been harder than it is today.
Economic hesitation has driven down giving. Investments in new digital experiences have created new, siloed data streams. Technical talent is hard to find (and keep) at all levels. And leadership, digital, and data analytics teams struggle to solve the difficult coordination questions of a post-office world. Some of our newest nonprofit partners are on their second or even third try. They’re feeling burned out.
Civis Analytics has spent the last decade working closely with more than 100 nonprofits from across the nation, helping the ACLU, Sierra Club, Doctors Without Borders, and dozens more develop world-class data and analytics programs to become more data-driven.
Part of what we’ve learned over the last ten years is that a one-size-fits-all, corporate-consulting approach to data transformation often falls short. Nonprofits are vastly different entities than the typical for-profit commercial organization, and their approach to transformation should be different as well.
Our goal with this article is to provide some of the hard lessons we’ve learned along the way. We’ll focus on the first step of the journey: Getting off the ground, making the hard choices, and getting a transformation going.
One disclaimer: Civis is an analytics company, not a broad-based ‘data company,’ so we’re going to focus on how nonprofits can organize their data and make use of it for critical decisions. We don’t have an opinion on this CRM or that CRM — only on how nonprofits can use their growing data sets to run better, more efficient programs.
Let’s get started.
All membership nonprofits eventually arrive at a data transformation crossroads. They want to invest (or have historically invested) in building their analytics programs, and their data goals from these activities are nearly universal:
Okay, let’s assume you’ve achieved buy-in at a leadership level to become more data-driven to achieve the above goals. But getting there is difficult. Before we provide recommendations on what to do, we want to talk about where we’ve seen nonprofit data transformations go off the rails. Apart from the raw difficulty of the process, we’ve seen a few recurring themes:
Let’s start with a blanket statement bordering on the obvious: The people on data teams and the people in leadership roles are often very different — different backgrounds, goals, and ideas on what’s important. In organizations where more severe communication breakdowns occur, you may hear something like the following:
CEO: “I have all these data people. I have no idea what they’re doing all day.”
The data team: “Leadership’s problem is that they can’t articulate what they want. And they’re scared to admit it.”
Harsh, but it’s real. And important to say out loud.
Many data transformations fail because of bad tech. But they also fail because of human judgment, loss of trust, and consistently poor communication between leadership and technical teams.
But why should you care?
| Reasons leadership gets frustrated at data teams | Reasons data teams get frustrated at leadership |
|---|---|
| Data teams miss deadlines by days or even weeks. | Leadership asks for specific deliverables, but then interrupts with ad-hoc requests. |
| Analyses have no supporting narrative or explanation…it’s just a bunch of charts. | Leadership doesn’t bother to pre-read written communication to understand the narrative. |
| Data teams won’t provide a detailed plan with clear ROI for budget meetings. Why should I pay for this? On what terms? | Leadership won’t commit to vital investments without mounds of paperwork. |
Trust is earned, and you’ll likely have to put in extra work to earn it.
Whether you’re on the tech side or the business side of things, it’s an important life skill to learn how to effectively communicate and advocate for your goals with other departments. Hearing the other side’s point of view and understanding what they need to be successful makes collaboration — and collaboration toward a shared mission — much less painful.
You’ll also be far better positioned to successfully move your project forward.
You’ve likely heard the sales pitch: “Buy this CRM or tool, and you’ll unlock a world of insights.” It’s an alluring proposition, but the analytics journey inevitably ends up looking something like this:
Here’s a reality all nonprofits must learn on the path to data transformation: Your CRM alone won’t solve your data problems.
“I just spent two years building a CRM, and yes, my data is in one place,” one nonprofit exec recently told us. “But how do I ask critical questions against our database and publish insights across my organization? I probably should have asked that question two years ago.”
A CRM is a necessary data system for recording attributes about a nonprofit organization’s membership and conversations with those members, and facilitating conversations and actions individually in a scalable manner. In the nonprofit world, this typically means a system like Salesforce, Blackbaud, EveryAction, or others: There are many.
But that’s where CRMs stop. We’re going to focus below on how to build a more reliable nonprofit data stack, but many buyers often fall into the trap of believing that a CRM is going to produce insights and interact easily with other outside CRMs that you still want to keep (text messaging services, third-party fundraising sites, etc.).
Make no mistake, a CRM is a valuable tool — a central, single source of truth for pipeline and data reporting. But expect months of implementation and adoption before you can functionally use it. Otherwise, it’s simply a bucket to put stuff in.
Moreover, a CRM doesn’t solve for common challenges like stale legacy data, technical debt, and poor data hygiene, making it all but impossible to determine the accuracy of your supporter lists — and calling into question the validity of what these lists tell you.
Worst of all, even when you finally put your CRM to use, you can’t rely on it for:
Most nonprofits, when they reach a certain size, evolve to a point in their data journey where the CRM needs augmentation to produce additional value to the organization. That post-CRM next step is the data warehouse — a central repository of information that can be analyzed, segmented, transformed, and modeled to make more informed decisions.
With a data warehouse, your data ecosystem can evolve; you use the data of your CRM but you’ll also be able create engagement models, furthering the understanding of your member base to anticipate its behavior and know how best to engage them as individuals.
Of course, much like a CRM, a data warehouse won’t solve all of your problems. You’ll need to consider how an optimal tech stack aligns with the competencies of your team. Yes, people are still an integral consideration, as your software capabilities can augment their expertise, but can’t replace it.
Editor’s note: If you’re looking for some guidance on how to build a technology stack and data team that suits your organization, here are some resources we hope you find valuable:
Need more help? Our nonprofit teams are here to offer guidance if you’re looking for additional support on what can best suit your organization’s goals.
Building a modern analytics tech stack, as well as a team who can make that stack drive value for your nonprofit, can be a daunting proposition, depending on where you’re at on the data-driven journey.
But to reiterate, this evolution is a process — one that you shouldn’t expect to happen overnight. The good news is that once you have a stack that works for you (a user-friendly CRM, compatible outreach tools, an effective analytics workbench that sits on top of a data warehouse), the work becomes easier. You’re better positioned to start breaking down data silos and create a holistic, 360-degree view of your organization, your supporters, and your outreach programs.
Your dream tech stack, managed by people who value the data your organization collects in the process, turns an overwhelming and siloed experience into a goldmine, meaning you’ll be able to answer million-dollar questions like:
Any type of digital transformation is daunting — data transformation even more so. The timespans, budgets, approvals, technical scoping, and implementation can feel insurmountable.
Because of all the hurdles, it is tempting to lean into inaction by forming a committee to discuss the impact of a data transformation process. Meanwhile, you aren’t gaining any clarity around what your data can and can’t tell you, what a scalable solution will look like, or how data can support your organizational goals.
Start your information gathering:
Here are few additional data warehouse development considerations to follow:
Too many nonprofits wait too long to plan what comes after your data is centralized. Sure, you need a data warehouse to create a complete picture of your donors — and to keep all your data in one place — but such a project requires many layers and stages of input, approval, and planning. Otherwise you run the risk of succumbing to what we call the Field of Dreams approach:
The Field of Dreams model is a remnant of the long-ago days when organizations still commonly implemented large, on-premise hardware/software solutions. But it makes little sense in today’s world. Cloud technology — and the competitive technology labor market — inverted the curve, meaning software is cheap, people are expensive, and time is your most valuable commodity.
Today’s nonprofits simply can’t afford to spend six months building a data warehouse with no endgame in mind. By the time the project wraps, all your hypotheses will be irrelevant and totally out of date. Failing to properly plan what you’ll do with your data warehouse before you build it means investing in just another tool whose value your organization will squander.
While many digital transformations fail, others go exceedingly well. Nonprofits boost their fundraising through predictable giving asks. They build communication programs based on upfront testing and robust measurement of their website content performance. They distribute resources to the beneficiaries that need them most, boosting efficiency and lowering waste.
In other words, successful digital transformations show results, and establish a culture of winning between leaders and data teams. And winning creates inertia.
Over the years, Civis has worked with dozens of nonprofits at varying levels of data sophistication. We’ve learned that a “crawl-walk-run” approach to data transformation works best, and that an efficient, effective data science practice is founded on five key pillars:
Now it’s time to assess your organization’s data — ask yourself these three questions:
Having data and having usable data are two entirely different propositions. Usable data is cleaned and centralized: A single source of truth to inform the organization’s decision-making processes. Analytics capacity is built on making assumptions explicit, confirming or disproving those assumptions, and iterating — and a shared source of truth fosters these conversations.
But too often, data is stuck in silos and fragmented across the organization — a common issue for nonprofit teams working with multiple source systems (email, CRM, digital, SMS, event, offline, program databases, etc.) across multiple physical locations.
Organizations must also install the right people and the right technology. Data scientists make their biggest impact when they create and deploy solutions that other people in your organization can use to inform their own work; this impact is dependent on people with the right skills to conduct analysis and communicate results, as well as technology that supports maintaining and surfacing data, collaboration, and automation.
Most critically, establishing a data-driven decision making culture means embracing scientific experimentation and iteration. You may need to try on a few approaches before settling into what works best for your organization: Start with a clear, self-contained research question or objective, and make sure it is tied to a decision — and that it’s actionable. Consider these key elements to nurture data literacy and drive data adoption:
Data transformation does not happen overnight. But you can set the wheels in motion by identifying the most pressing problems facing your organization. These will serve as your research questions and a lens through which you’ll analyze your data. Remember:
To understand our approach in action, consider the example of the Natural Resources Defense Council (NRDC), a national membership-driven organization dedicated to fighting climate change. NRDC was struggling to connect with its millions of supporters, and data silos were to blame: With data scattered across various systems and locations, and no single source of truth to inform its decision-making processes, NRDC had insufficient insight into which causes its supporters cared about and which messages moved them to action.
“By partnering with Civis, we’ve developed a deeper understanding of our donors,” says NRDC’s Director of Insights and Data Strategy Kate McKenney. “We’ve been able to find the best prospects and cultivate long-term donor relationships.”
Membership nonprofits investing in data transformation should ask themselves the following questions:
No matter how you answer these questions, Civis Analytics has a proven track record of helping organizations just like yours accomplish their data transformation goals. We’ve not only worked with dozens of membership nonprofits, but many of our team members came up through the nonprofit ranks. And like your organization, Civis is dedicated to doing social good: No other data platform provider is as synergistically aligned with your mission or your business.
