Helen Brown Group

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Time for Database Spring Cleaning!

by: Melissa Bank Stepno, VP of Data Insight

 

Sweeper by Pierre-Auguste Renoir (1889)

It’s early June and while some of us might be living in areas of the country where Summer is in full swing, here in New England, we are still in the thick of Spring.  And, by thick, all you need to do is look outside at the layers of pollen covering all surfaces to know that it is really still Spring.

So, let’s do some Spring Cleaning in our database!

Clean data is critical for a nonprofit.  Sometimes, you’ll hear the practice of keeping “clean data” referred to as “data health” or “data hygiene.”  The name doesn’t matter but the practice does.

At its most basic, how can you effectively interact, engage, solicit and steward your constituents if you do not have clean and accurate contact information?  This would typically include data points such as name, mailing address, email address and telephone number.

But, clean data goes far beyond communication strategy.

Broadly speaking, the concept of ‘Garbage In/Garbage Out’ means that if you embark on any type of analysis or reporting effort, your results will only be as strong as the data that you have fed into the analysis.

Dirty data leads to bad results.  Or, at the very least, results that may be inaccurate or misleading.

It is for this reason that clean data is the most foundational – and most important – element of an analytics project as well.

Where do you start? 

The best place to start is to ensure that your organization has clear and consistent data entry policies in place.

It is highly recommended to have a style guide or manual that describes how data should be captured within your database.

Even better if you can use the tools and functionality of your database to “lock down” data fields by requiring standardization in formatting or using dropdowns and picklists.

Let’s look at an example:

Let’s say you are starting an internal prospecting project and you want to run a quick report of all of the people in your database who made a gift last month (May 2024).  Seems easy, right?  You just navigate over to your query function, select the “most recent gift” field in your database, enter the date range “5/1/24” – “5/31/24” and hit Enter.

Presto, you are done!

But are you?

Your results come back empty, and you know that gifts came in last month.

Going back into your query, you realize that you should have entered “05/01/2024” – “05/31/2024.” You edit your query and run it again. This time you get a few results, but they seem smaller than they should be.  Frustration growing, you think: “what now?”

So, you start to do some investigating and realize that your gift processing intern, on a study abroad program from overseas, entered the majority of last month’s gifts using the European date format, meaning that May 1, 2024 is now recorded as “01/05/2024.”  What a nightmare!

Now, a date field might seem like a silly example since we know that every quality database on the market will allow an organization to “lock down” dates into a standard format.  But it still serves a strong illustrative purpose of how consistent data – or maybe the inconsistency of data – matters.

Since a date field is just one simple example, let’s look at how this could snowball when you consider a more complex project.

What if your requirements were: all donors living in NY, who made a gift last month, and have a major giving capacity of $10K or more.

  • Are states entered as: NY, N.Y. or New York?
  • Is major giving capacity a free-text field or have you set up pre-defined dropdowns? Does the capacity field include discrete dollar values or ranges?

It’s actually interesting because in many organizations, the function of data entry is relegated to the most junior staff members (or interns!) as it is considered a rote administrative function.  However, data entry might be the most important function in your office.

Yes, I said it:

Your data entry person or team is THE MOST IMPORTANT in your office!

So, let’s assume you are thinking: “This is great, but I don’t need to worry about this because I know we have a data entry policy in place, and I know our database is configured optimally.”  Even with the best planning, the best intentions, and the most conscientious of people entering data into your database, dirty data still happens.

Here’s a quick list of other considerations:

  • Who has access to enter data into your database?
  • Have they been trained in both the functionality of your database and the policies that govern data entry?
  • Can they enter all datapoints or just specific datapoints?
  • Do you have regular data integrity process or run periodic data audit reports that can help identify dirty data?
  • Do you have a process in place to address data integrity issues?
  • Do you have a cadence in place to update data on a regular basis?

In closing, know that no database will ever be perfect.  Data changes rapidly and even with the best intentions, human error happens.  Rather than striving for perfection, strive for thoughtfulness, consistency and continued maintenance.

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For additional reading, guidance and sample policies:

 

 

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Alternate Interpretations of “AI”

          © image via www.vpnsrus.com

It is estimated that more than 1.1 trillion megabytes of data is created every day.  In fact, by the end of 2023, the world will have an estimated 118 zettabytes of data in it. You might be asking: what is a zettabyte? Good question.  I had to look it up too.

By way of a formal definition, 1 zettabyte is equal to 1 billion terabytes, 1 trillion gigabytes or 1,000,000,000,000,000,000,000 bytes (that’s 21 zeros, in case you are counting).

By way of colloquial comparison, 1 zettabyte is approximately equivalent to all of the grains of sand on all of the world’s beaches.

By way of my brain, it is uncompressible how large that really is.

And, sadly, what are we doing with all this data?  Mostly nothing.

By way of formal definition, Artificial Intelligence is “is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is also the field of study in computer science that develops and studies intelligent machines. “AI” may also refer to the machines themselves.”

By way of colloquial comparison, Artificial Intelligence is a “machine’s ability to perform the cognitive functions we usually associate with human minds,” typically in a way that is smarter, faster and/or more complex than the human mind can process.

By way of my brain, Artificial Intelligence can help us make sense out of all those zettabytes of unused data … or perhaps it is just a cause of why we are creating so much data in the first place.

The Proliferation of Artificial Intelligence

Artificial intelligence includes more simplistic applications like descriptive analysis or predictive modeling, both of which have served our industry for decades.

It also includes more sophisticated technologies, like generative AI tools that emerged only a year ago.

No matter the type of Artificial Intelligence you explore, it has the potential to truly transform the world, including our professional corner of it in the philanthropic sector.  Its potential impact cannot be understated.

However, judging by the 1 million Google hits when searching “Artificial Intelligence and nonprofits” and the 7.1 million Google hits when searching “Artificial Intelligence and fundraising,” my concern is that we have gotten to the point where we are overusing the phrase without a clear understanding of what it really means or how we can use it successfully.

Judging from the numbers above, one might wonder: “has Artificial Intelligence just become an overly hyped catch phrase?”  Others might reply: “wow, that’s a controversial comment!”

My opinion? Artificial Intelligence is just one tool of many in our toolbox.  It cannot and should not be applied to every facet of our work lives and it will not be the answer to all of our fundraising problems.  Here are some guiding principles:

Guiding Principles:

Using Artificial Intelligence successfully…

  • will require setting expectations properly.
  • will require thought and planning.
  • is not just about saving time and doing things more quickly, it is also about enhancing our capabilities and allowing us to push further.
  • will require sophistication, even when it is brought down to its simplest form.
  • is not something you can just decide “to do” one day.

So, how do you set yourself and your organization up for success?

Alternative Interpretations of AI:

Let’s explore some alternative interpretations for what the letters “A” and “I” might stand for.  None of these are as “on trend” as the phrase “Artificial Intelligence” but I would argue that all are necessary components that can help us transition from just talking about Artificial Intelligence as jargon to truly leveraging its power to help us operate stronger, faster and more efficiently.

  •  Accurate Information – the concept of needing clean data for analysis is just about as basic as you could get, but it’s foundational for a reason.  As with any data or analytics application, the term “garbage in/garbage out” applies to Artificial Intelligence as well.  While no data set will ever be perfect, the more accurate and complete a data set, the more accurate Artificial Intelligence applications will be.
  • Apply Incrementally – not every aspect of your organization’s program will need Artificial Intelligence to become optimized.  And, in fact, given that using Artificial Intelligence takes the investment of time and resources, consider how you might start small and where it might have the biggest impact.  As an analogy, if Artificial Intelligence is the rocket ship of using data and technology strategically, the rocket ship is piloted by someone who learned to fly on a single engine plane – what is your single engine plane?
  • Amplifying Interests – how can you find your single engine plane?  Consider where and how Artificial Intelligence will be able to best assist you in amplifying your interests.  You can focus on either the core mission of the non-profit organization that you work for or the specific program area/type of responsibilities that you are focused on supporting.  It does not matter.  Either way, you want to choose a project or an application that is meaningful and needle-moving, not something that just seems “cool” or interesting.
  • Accountably Informed – there’s a lot of chatter about the ethical and legal considerations of Artificial Intelligence – and for good reason.  From data bias that could impact the results of an analysis project to privacy legislation that governs the types of data we can use, the concerns are real.  Each of us has a duty to hold ourselves accountable to being informed about how to properly collect, store and utilize data within our organizations.
  • Actionable Implementation– Over the past two decades, I have witnessed dozens and dozens of organizations do nothing to change their business practices in response to an artificial intelligence exercise.  There is no common denominator for the size or sector of the organizations, or the type of project they conducted.  The reasons for not implementing are broad and varied, ranging from lack of bandwidth, to it not being prioritized, to lack of support from leadership, to cross-departmental challenges waiting for someone else to do something in order to “get started.”  Frankly, most frequently it is just complacency since it’s easier to not change and to keep the status quo. Here’s the problem: you can’t expect artificial intelligence to have an impact if you aren’t taking action to implement change.  If you are not committed, willing or able to do so, do not proceed.
  • Augmented Interpretations– Sometimes Artificial Intelligence will be wrong.  In fact, all statistical processes have flaws and some margin of error.  Celebrate each win.  But, do not expect perfection.  Be prepared to have some failures.  Healthy skepticism and critical questioning are perfectly natural.  This is a balancing act.  Always consider the long game, which includes the overall efficacy without letting individual imperfections push you away.
  • Actual Intellect – Artificial Intelligence is a given.  Will Artificial Intelligence change the way we do our work? Absolutely.  Some of us are old enough to remember when the Internet was rumored to replace our profession and it obviously didn’t.  We are still here!  Our actual human intellect is still needed.  And, in fact, I would argue that artificial intelligence will never replace real intelligence, because artificial intelligence is … well, artificial!  As good and as strong as it gets, there will always be nuance, context, consequence and strategic interpretation that will need our actual intellect.

With that, I’ll summarize by saying that I hope you found this blog post to have Applicable Insights for your interest in this topic (sorry folks – I couldn’t help but sneak one last “A” and “I” in) and that it has encouraged you to proceed with Artificial Intelligence endeavors more thoughtfully.

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Reflecting on Narrative and Data

HBG Data Insight Lead Steve Grimes

In this month’s article on data science, Steve Grimes provides important insight, showing us how he reminds himself and us about how important thoughtfulness and care are in the final step of any data science project – the way we present the results to end users who (we hope) eagerly await the ability to see what the data tells us. Will it alter our current trajectory? Will it provide us with actionable insight? Will we easily be able to see answers that we didn’t have before? The way that data are presented by data science professionals most importantly can help explain why things are the way they are, and help point us in the direction of “okay, so what do we want to do about it?” ~Helen

While I’ve been a data professional for a decent amount of time, every now and then I sometimes struggle with crafting a story about the results of my analyses. This usually happens on larger projects with complicated workflows causing the data points to feel like they are a world unto their own – a world in which I have very little ability in shaping the story about that data. To put it another way, I sometimes get lost in the sauce in those work-streams and simply forget my role in making the data meaningful.

To counteract this, I usually turn to a good book on analytics for inspiration and guidance when I’ve lost my way within the analytic process. One that I return to from time to time is Visual Data Storytelling with Tableau by Lindy Ryan. While it is a few years old (published in 2018) and limited in unpacking the full scope of Tableau as it stands today, I’ve found that it provides some excellent advice on the conceptual mechanics of the story-making process within data analytics. I think the following excerpt is a good example:

 Data visualization and data storytelling are not the same thing; however, they are two sides of the same coin. A true data story utilizes data visualizations as a literary endeavor would use illustrations—proof points to support the narrative. However, there’s a little bit of a role reversal here: whereas data visualizations provide the “what” in the story, the narrative itself answers the “why.” As such, the two work together in tandem to translate raw data into something meaningful for its audience. So, to be a proper data storyteller you need to know how to do both: curate effective data visualizations and frame a storyboard around them. This starts with learning how to visualize data, and more importantly, how to do so in the best way for communication rather than purely analytical purposes.”

Clearly, there are few takeaways from this passage, but what resonates the most with me is that narrative decisions are not just made in data visualization workflows but within the entire analytic process.

For example, there is a story to be told in the collection of your data; a story in how the data has been structured; the analytic techniques; and the way the results are presented. Each choice made within the analytic process comes with its own narrative and will be inextricably tied to how the data are presented. To lose sight of that is to forget that analytics is ultimately a process of making meaning, and if narratives are the currency of meaning, as human beings, we are the sole brokers to that exchange.

So excerpts like these serve as a good reminder to me that, if I am struggling to create a story about my results, I have not done the work of acknowledging the narratives that would have come along in every stage of the analytic process and the meaning I am creating within each step. I imagine that I will lose sight of this again, but hopefully sharing this with you will serve as a good reminder for me and something for you to consider and reflect on.

To that end, I’m reminded of another quote on our relationship with narrative and storytelling from theorist Roland Barthes:

 The narratives of the world are numberless. Narrative is first and foremost a prodigious variety of genres, themselves distributed amongst different substances – as though any material were fit to receive man’s stories. Able to be carried by articulated language, spoken or written, fixed or moving images, gestures, and the ordered mixture of all these substances; narrative is present in myth, legend, fable, tale, novella, epic, history, tragedy, drama, comedy, mime, paintings…., stained glass windows, cinema, comics, news item, conversation.

Moreover, under this almost infinite diversity of forms, narrative is present in every age, in every place, in every society; it begins with the very history of mankind and there nowhere is nor has been a people without narrative. All classes, all human groups, have their narratives, enjoyment of which is very often shared by men with different, even opposing, cultural backgrounds. Caring nothing for the division between good and bad literature, narrative is international, transhistorical, transcultural: it is simply there, like life itself.”

 

 

 

 

 

 

 

 

 

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Prospect Research Analytics in Three Acts

Steve Grimes

This month for our fundraising analytics feature, Data Insight Team Leader (and aspiring playwright!) Steve Grimes brings three real-life fundraising conundrums to life and illustrates how data insight can bring clarity and direction to common situations our nonprofits face. ~Helen

The stories you are about to read are fictional. The analytical techniques, however, are all true. 

 

Act 1: Survival Analysis

[Amari, prospect research manager, knocks on data analyst Hollis’ office door]

Hollis: Come in!

Amari: Hollis, you got a moment?

Hollis: Sure, what’s up?

Amari: I’m worried about our donor retention rate. I’ve had to reduce the gift officers’ portfolios by 20% this quarter because of non-response from their outreach efforts.

Hollis: Oof! Ok, any thoughts on how to fix that, I kinda like my job.

Amari: Sort of. I’m thinking of setting up an alert system to warn us when a donor is in danger of being non-responsive, but I need a straightforward indicator that I can communicate to the gift officers to keep an eye on. I don’t want to overwhelm them with too much information.

Hollis: So, like, a number we can use as a threshold so when a prospect falls under it we can flag them for being in danger of non-response?

Amari: Yup, pretty much!!

Hollis: Well, Amari, I think I have something for you. [Hollis points to their computer] Welcome to the Wonderful World of Survival Analysis!

Amari: Sounds intriguing!

Hollis: Well, it’s actually pretty morbid because survival analysis is usually used in medical research, to…well, understand how effective a particular medical treatment is..

Amari: That does sound morbid.

Hollis: In our case, it will be wonderful! So survival analysis visualizes a curve that shows how long a population of individuals ‘survive’ after treatment. From there we can use it to identify how long it takes for our donors to be in danger of moving into non-response. ‘Treatment’, our starting point, would be the first time they give to our organization, and we would be looking for the point when the majority of our donors stop donating regularly.

Amari: Ah, I think I get it. If we know what our retention rates are, identify at what point our donors stop giving regularly or in this case ‘survive’ within our organization, then those two metrics combined could be used as a threshold indicator to alert the gift officers for special action to retain the donors and hopefully improve our retention rate?

Hollis: Indubitably!

Amari: Indubitably?

Hollis: It was my word of the day.

Amari: Well, what do you need to get started?

Hollis: Data that describes how long each of our donors have been with us and their giving history.

Amari: Lovely! Ok, next problem, what are you having for lunch, I’m spoiled for options.

Hollis: I heard marketing had catering for a meeting, want to see if there is anything left?

Amari: You’re just solving all the problems today.

Hollis: I wear many hats, Amari!

 

Act 2: Decision Trees

[Hollis joins a Zoom meeting with Amari and Lin, Chief Gift Officer]

Lin: So then I says to Mabel, I says…oh hey, Hollis! Thanks for joining us!

Hollis: Not a problem!

Amari: Hollis, now that our retention rate is up, we want to make sure that the active prospect pool is populated with the best folks, but I feel like our Recency, Frequency, Monetary Values (RFM scores) aren’t measuring our best.

Hollis: What do you mean?

Lin: Our current RFM structure really only takes into account a person’s giving, but we have other engagement points that I don’t feel like we are tapping into.

Amari: So, like, email engagement, event attendance, and our web portal metrics?

Lin: The thing is, I’m not sure which of these engagement points we should incorporate into the RFM analysis. I’m worried we aren’t capturing individuals who are not giving a high or frequent amount, but who really like us, and I want to account for that.

Hollis: Ah, Lin, I think I have a solution! Welcome to the Wonderful World of Decision Trees!

Lin: I’m listening.

Amari: Is it as morbid as survival analysis?

Lin: Survival analysis?

Hollis: That was in the last scene, and no. But decision trees are just as useful to solve this problem.

Lin: Go on.

Hollis: Well, putting aside how we define ‘best’ and ‘engagement’ for the moment, and assuming that we want to capture as many people as possible who like us – not just the frequent donors – decision trees can help us in identifying which engagement points are the most appropriate to add into a RFM model for our purposes.

Lin:  But it’s key that we have engagement points in the RFM model that takes into account as many people in our database, not just a small population of individuals.

Hollis: And that’s why I think decision trees are perfect. If you’ve ever seen a family tree, the output of decision trees sort of looks like that. What we would do is put all our recorded engagement points into a decision tree model, and the subsequent output of that analysis provides a roadmap of sorts of what engagement points are the most important, and how many people each engagement point affects.

Lin: Oh, so it is sort of a visualized way to see which engagement points are the most impactful to our prospects and how many prospects fall into that engagement point?

Hollis: Pretty much! There is some work that we would have to do to conceptualize some of these steps, but it is totally doable, and in the end we can create a more robust RFM analysis that properly ranks more of our prospect pool.

Amari: Great! When can you get started?

Hollis:  As soon as I’m able to access all data points that describe how our prospects engage with our organization. From there, we would need some conversations on your definition of what the “best” prospect is for your team. It would take some iterations to get it right, but it is relatively straightforward.

Amari: Alright, I’ll get started on getting you that access.

Lin: Lifesaver, the both of you!

Hollis: Not a problem! Speaking of..[points at candy on Lin’s desk] can I have a couple of those Lifesavers next time I come into the office?

 

Act 3: Cluster Analysis

[During their weekly one-on-one, Amari and Hollis discuss the possible expansion of the organization]

Amari: You know, I’m feeling pretty good about bringing more people on board, but this is the first time in years that the organization has expanded like this, so I’m feeling a bit uneasy about the direction Development will go in the future.

Hollis: Lay it on me, what’s bothering you?

Amari: Well, I’m particularly worried about how to segment portfolios that speak to the strengths of the additional gift officers we are going to bring on the team. I would love a way to think about their portfolios that would speak to their abilities as stewards of the organization.

Hollis: Well Amari…

Amari: Here we go again…

Hollis: …I think I have a solution.

Amari: Welcome to the….?

Hollis: …Wonderful World of Cluster Analysis!

Amari: Hollis, how do you know so many worlds?

Hollis: I like wonderful things, Amari. All jokes aside, I do think cluster analysis is exactly what you need for this particular workstream.

Amari: I feel like you’re gonna explain this to me.

Hollis: Yup, you know the drill! So cluster analysis is used to identify natural groups within your data.

Amari: “Natural” groups?

Hollis:  Well, calling them “unbiased groups” is a little misleading, but for all intents and purposes that is what a cluster analysis algorithm creates. In the end, what we are doing in a cluster analysis is segmenting the data based on groups that are most similar.

Amari: So we would group prospects based on how similar they are across categories?

Hollis:  Well, “we” wouldn’t be doing it, the algorithm would. The power of cluster analyses is that it is unsupervised, we are essentially going where the data tells us to go.

Amari: Ahhh, I see where the “unbiased” part comes in.

Hollis: Yup, so we could find clusters of prospects that live less than 10 miles from our building, those who have given less than fifteen hundred dollars in the past year, or those who like the color blue. It really all depends on what we are tracking in our database and how we structure the datasets.

Amari: [eyes widen] And because we are letting the algorithm decide the types of groups, it might show ways of looking at prospects that we are not paying attention to?

Hollis: Spot on! This way we can identify ways of grouping prospects that are “natural” to our organization.

Amari: And knowing that beforehand might help who we recruit!

Hollis: Right, so we could do a cluster analysis on our giving data, find some groups of prospects that we feel are impactful to us, run a wealth screening on each of those groups to identify the return of investment to cultivate those prospects, and then use what we found in that analysis as a guide when doing interviews to select the right candidates for our demographic of prospects.

Amari: I think that solves some other problems we weren’t even thinking about.

Hollis: Probably, it’s nice when it works out that way.

Amari: Should I just give you unfettered access to all of our data.

Hollis: Of course. The more access we have to the org’s data, the more we can think about different ways of solving problems like this.

Amari: Can it help me with what to eat for lunch?

Hollis: I can probably figure something out, won’t know until I see what data we have. There is probably a positive correlation between eating a kale salad and how productive you are.

Amari: ….are you in the mood for salad, Hollis?

Hollis: Yeah, kinda.

Amari: Alright, my treat! Although, I’m sort of in the mood for a burger.

Hollis: There is probably a negative correlation with burgers and your productivity.

Amari: Yeah, you’re probably right, better stick to kale to be safe.

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Innovation: Measuring fundraiser portfolio strength

This week I’m very excited to share with you a podcast I recorded earlier this week with Mark Dumich and Ling Sun of the University of Notre Dame. Their department is doing innovative work to help strengthen and create parity amongst their fundraisers’ portfolios.

In our podcast, Mark and Ling shared with me the questions they were trying to answer, their step by step actions, and some unexpected (but welcome!) outcomes. Mark explains their plan:

 The Portfolio Strengths Model was developed at the University of Notre Dame to better understand both the strength of a prospect and the quality of a portfolio.  As fundraisers expressed concern about the giving potential of their prospects, we decided to score various attributes of those individuals to get a better assessment of their value. It moved the conversation away from how a development officer feels, and back to objective data points that we can all agree on.

It’s very easy to evaluate a fundraiser on the number of dollars he or she brings in to the organization, and it’s important, too. Unfortunately, that metric has some significant shortcomings when you consider that not all portfolios have the same potential. So how can we more accurately measure a fundraiser’s performance? When we viewed production relative to the strength of a portfolio, it highlighted a lot of incredible work that had been achieved at all levels of philanthropy within our organization.”

We’re especially fortunate that, not only did they speak with me for 30 minutes, they also generously provided a step-by-step worksheet of their project, so you can do it, too!

The podcast is below, and the worksheet is in our free member area. Warm thanks to Mark and Ling for sharing this with us!

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Are you ready for analytics?

If you’re a regular reader, you know that each month we feature special guests writing about their favorite topics. This month we welcome HBG Senior Researcher and member of the HBG Analytics team, Tara McMullen to share her thoughts about one of her favorite subjects!
Vintage typewriter

Sometimes it’s hard to get started with a new program or type of technology because we don’t know what its power is. We don’t know what it can DO, so we stick with the old familiar way of doing things. But these days, doing things the same old way can leave your progress lagging and your program looking a little old-fashioned.

Sometimes the basic principles are good, they just need a little updating.

Maybe you are thinking about undertaking a campaign and aren’t sure if you have the critical mass or the right prospects to meet your goal.

Or maybe you are looking to create a prospect management system, and want a way to sort prospects into various stages in the pipeline.

Or maybe you are trying to find new potential volunteers for your board. [Read more…]

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Using Analytics to Pull Away From the Pack

raceHow are top-performing organizations pulling away from their peers? In many cases, it’s through an understanding and clever usage of analytics. This week we welcome HBG Senior Researcher and analytics student and practitioner, Heather Willis, to The Intelligent Edge. Heather shares some of the latest studies with tips on the most important things organizations should do to take advantage of the data available to them.

So: are you a Pacesetter or a Dabbler? What do I mean by that? As you probably already know, we are in the midst of significant change in how we deal with and use the massive amount of data that is being created and collected each day. [Read more…]

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Fundraising Analytics ABCs – Data Mining

Chances are good if you are in the fundraising field that you have heard the term “fundraising analytics.” You’ve probably also heard the terms “data mining,” “donor modeling,” “reporting” and “prospect identification,” too. Do these terms mean the same thing? What are the differences among them?

I asked Marianne Pelletier, who leads the HBG Analytics team, to help me put together a series of short articles designed to make sense of all of this. In each, we will describe the method and give examples of how they can be used. To begin our series, we discuss data mining.

Let’s begin with a case study:

The fundraising team at a university is having a problem with donor retention. Every year the university must acquire a significant number of new donors to offset the nearly 50% of donors they lose from the previous year. They need to find an answer to the question “why are we losing so many donors, and what can we do to keep them from leaving?”

They decide to use a technique called data mining to find out.

What is data mining?

Sometimes we don’t know what we don’t know. In those situations, it may be best to explore a little to see if we can find answers (and perhaps even see the questions we need to ask). Think of it as discovering what’s in a new mall by walking the length of it: data mining is like shopping in the data mall. What will we find when we look in each store?

Data mining sifts data back and forth until it finds natural breaking points, puts together associated characteristics and then lays out what it finds. For example:

“The donors who give the highest gift amounts are married male alumni. They’re in their 50s. They live along the east or west coast. They have a job and children that we know about.”

Data mining delves deeper to find relationships between many characteristics

The good news is that data mining also names the characteristics for the second best segment, and the third best, and so on, so a university could use it to find out various solutions to their attrition problem. The characteristics they see might look like these:

“The donors who stop donating tend to leave between their 5th and 6th years of consistently donating to the annual fund” So we can work harder to retain them before year 5!

“The donors who stop donating tend to be married alumni males in their thirties” What special incentives can we offer to that group since we know that married men in their 50s tend to be our largest donors later on?

Data mining can also be used for:

  • Determining the best solicitation methods for donor acquisition, renewal, or upgrade
  • Measuring the characteristics of event attendees who later become donors
  • Understanding the clusters of members/grateful patients/families/alumni/docents who respond better to e-mail, direct mail, phone calls, or social media
  • Finding the best pattern for the cultivation/giving ladder
  • Adding or dropping solicitation methods, or venues
  • Assessing timing, including how long it takes to successfully solicit gifts at different levels

What else can data mining do?

Have you ever walked into a store that has section after section of fun things for purchase? A department each of clever t-shirts, gifts that your best friend would love, beautiful hand-crafts, and more – things that are so perfect that you’ve picked up an armful of things and you need to find a basket to dump them all in.

Data mining is like that. It can also be for:

  • clustering like-minded, or like-attributed prospects for a cultivation dinner
  • breaking long-held “truths” about your donor base, such as “our best athletics prospects are male football alumni.” What if that’s not actually true?
  • looking at what statistics calls “interactions” – the combination of characteristics that make good prospects, members, volunteers, trustees, etc. For example: Married prospects and/or prospects living in rural areas show a mild relationship to loyal giving. However, prospects who are married AND live in rural areas show a strong relationship to loyal giving.
  • determining which group responds best to email and which to social media.

What do you want to know?

Our series continues next Thursday, September 12 when we will discuss Donor Modeling.

Do you have questions about data mining or would you like to see how it can work for your organization? Email us for more information at info [at] helenbrowngroup [dot] com.

 

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5 Great Ways to Find New Donors

You need new donors in major gifts, annual giving, planned giving, principal giving…okay, I understand: you need new donors in ALL areas of your fundraising operation. No worries. Here are just a few (of the many possible) remedies to help you identify and involve new donors.

 

Remedy #1: Have you taken care of donor attrition?

For many organizations, attrition numbers are scary-high right now. Do you know what percentage of your donors leave every year? It’s a lot easier (and cheaper) to keep a donor than it is to acquire a new one, so work at understanding how many are drifting away and why they leave. Then devise strategies to keep them.

 

Remedy #2: Do you know who your best prospects are?

It doesn’t matter if you work at an organization with less than 500 donors or one with a million. You need to get to know your donors better so you can find others like them. Data analytics – even basic queries – can provide characteristics of your best prospects to help you identify more people just like them. Slicing and dicing your data – even sparse data – will give you great answers. If you don’t have capacity to do it in-house, it’s very easy to find talented analytics experts to help you.

 

Remedy #3: Do you know what it is about your organization that donors love?

You may be surprised to learn that it’s not always a priority you’re pushing, but some other X factor that gets them jazzed. Ask them! Surveys are a great way to find out donor interests and opportunities you could capitalize on. (And don’t give me the old “but we’re not an alumni-based organization!” argument!) Alumni organization or not, don’t you have gorgeous t-shirts to give away as an incentive? Or what about a “Free ice cream cone in the splash park for donor survey responders appreciation day”? What do you have that prospects would value? Be creative and piggyback activities!

 

Remedy #4: Are current donors giving you what they’re giving other nonprofits?

An electronic screening can help you answer this question, and will help you elevate both annual fund and major gift numbers – probably significantly. Many of the vendors, in addition to providing asset information, also match the individuals in your database to donor honor roll lists of nonprofits across the US and United Kingdom. Someone who is regularly making gifts across town that are 10x what they give your organization needs to be asked for more.

 

Remedy #5: Maybe they don’t love you yet, but what about the ones who ‘Like’ you?

It’s a good bet that your nonprofit has some kind of social media presence at this point (and if not, it’s time to get a move-on). What have you done to convert those people who just Like you into future donors who love you? What can you offer them – of value – in exchange for their contact information? A study or white paper? Access to an invitation-only lecture? A free hour in the swimming pool? A ‘behind the scenes’ tour with the performers?

These are just a few of the many ways strategic prospect research can help you identify prospects. Thanks for reading – What ideas do you have for how you identify new donors?

 

 

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