There is a shift happening in the world of fundraising research tools. The tools that once felt so incredibly innovative are beginning to feel a bit restrictive. Why are women and minorities consistently overlooked in wealth screenings? And the promise of machine learning has long felt distant and unaffordable.

Beyond the tools, fundraising practices are shifting, too. Diversity, equity, and inclusion are receiving emphasis and are changing the face of our constituencies and maybe even our co-workers, even though it may feel like it’s happening in slow motion.

To place these shifts into context, we need to start at the last major innovations and move forward. My intention isn’t to evaluate specific tools or to cover the field in anything more than very broad strokes. And yet, I still hope to surprise you!

The (brief) history of research tools

The field of prospect research truly began to thrive and grow with some key technological achievements.

1. Relational Databases gave us accessible data
   
   In 1970, E. F. Codd published a research paper while at IBM that described a relational database, which allowed multiple tables to be connected or related to each other by a key field. This meant that tables did not have to contain duplicate data. In 1974 IBM developed a prototype and over the years this has led to continual improvements, such as the customer relationship management (CRM) databases in use today.
   
   No more paper ledgers! Relational databases made the giving and other information we collect on our donors highly accessible.
   
   And this enabled new data mining techniques such as Recency, Frequency, Monetary (RFM), which make it possible to identify the top 20% of donors who give 80% of fundraising revenue.
   
2. The internet exploded
   
   Once the internet became searchable with Google, the world of prospect research became transformed! Sure, there were still paid subscription tools to databases, such as the Who’s Who directories and Dialog, but Google opened up a new level of information to fundraising researchers.
   
   And this opening of the internet in turn was fueled by the digitizing of data. For example, Securities and Exchange Commission (SEC) filings and real estate could now be matched to the donor names in your database. Prospect screening companies were formed and the data sources and the companies’ algorithms improved. In shockingly little time you could discover who made a small gift to you, but had great wealth!
   
3. Social Media gave us personal data
   
   Prospect research was reluctant to accept social media as a valid data source, but folks came around. Once the parallel between LinkedIn and Who’s Who information was made, the debate fizzled. Now we researchers can find as much personal data as donor prospects are willing to make public. And that is a whole lot.
   
   Personality, donor motivations, interests, hobbies, and much more is often revealed!

What is happening now with research tools?

There has been a shift going on in our research tools for years now that has recently been gaining momentum and is completely changing the way I think about our “tools.”

1. Databases are no longer sufficient – we need analytics
   
   This is where you yawn and say, “Really Jen, everyone knows about analytics as a trend!” And it’s true. But to date, the push in research has been to learn Excel, and now there are debates about which analysis software program you should learn, R or Python?
   
   What we need to look out for are nonprofit CRMs that have built-in analytics. It will take time andlot of investment money to present a competitive threat to the behemoth incumbents in the field, but there are new donor CRMs being developed specifically for the nonprofit sector and [spoiler alert!] they have some analytics built right inside.
   
   Take Virtuous CRM. Their CRM product provides RFM data right on the homepage of a constituent record. Companies building software with a focus on the nonprofit sector should know the key stats and activities we want to do with our donor data. But beyond that, there is a growing recognition or remembering of the value of a donor’s history with your organization through giving and other engagement.
   
   Having wealth was never enough, but it feels like we became mesmerized by the wealth screening tools and may have forgotten just how important it is for prospects to have a love for and connection to our organization. The donor CRM is a primary source for this kind of information. Companies like Virtuous CRM understand this.
   
2. BIG data is becoming more accessible–even for little nonprofits
   
   I won’t say it’s happened yet, exactly, but companies are hard at work building models from external BIG data to apply to your organization’s donor CRM. We learned from the wealth screenings that external data, such as SEC filings, can yield tremendous fundraising insights that can be turned into efficient actions.
   
   Machine learning models hold the promise of a new world of external data. Perhaps a world that includes many more people than the traditional wealth screenings.
   
3. Social media is becoming a key donor acquisition strategy
   
   Researchers tend to focus on major gifts, because that’s where the biggest return on investment is. But that may change in some surprising ways. According to NextAfter, donors acquired digitally crossover more easily to multiple-channel giving, and multiple-channel donors give more. And when it comes to being online, social media is where the people are.
   
   Your machine learning company might help you with donor acquisition and retention across all giving levels, as well as data enhancement and major gift prospecting. If the company is leveraging big data for insights, it makes sense that the same company can help you navigate that big data across the gift cycle – especially for your digital first donors.

How can researchers be best positioned for the future of research tools?

There are those who proclaim that you need to learn a program like R or Python to stay relevant, others who insist it’s about developing internal relationship skills to manage people and portfolios, and then there’s me – a prospect research generalist.

If I were to distill a pathway through and among the throng of fundraising research tools, it would be this:

• Know your fundraising. Researchers are fundraisers and it’s key that we understand how relationships with donors are created and maintained. This informs everything!
• Understand the theory behind new tools, how things work, even if you can’t quite execute it in something like Excel or Python. This gives you the perspective to notice how your work process – and the fundraising process – could benefit and achievemore from shiny new tools.
• Make the time to learn about new tools from existing vendors or brand-new companies. This may inspire to you to launch yourself into a specialty like programming, but even if it doesn’t, it improves your chances of getting in on the beta testing level. This translates into big opportunity for lower cost!
• Question everything! How will we use that score to take what action? How is it better than what we use now? There’s a lot of competing companies out there and sales talk is sweet. Your analytical approach to research applies equally to evaluating new research tools. Your organization is counting on it.
  

It’s difficult for us humans to imagine things we’ve never seen or heard of before, but having an open mind is a good start. Donors are not numbers, they are people. And yet, machine learning can provide meaningful insights into the often-elusive indicators such as affinity and engagement.

Will you be able to spot the next best tool? Or will you be the one to say it isn’t possible?

Or will you be the one to share your predictions or tell me I missed something critical or overlooked an important innovation? I’d love to hear from you! Comment here, email, or call.