Helen Brown Group

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Access and Enhance Data Without Leaving Your Spreadsheet

by: Kenny Tavares, Senior Consultant & Lead Data Analyst

 

Kenneth Tavares

Kenny Tavares

Microsoft Excel is undoubtedly one of the most adaptable tools I have used in my prospect research and data analysis work. To paraphrase an old commercial: “It slices, it dices, it makes julienne fries!”

Seriously, its many applications have led me to always assume Excel can accomplish what I need and then find a way to make it happen. I love unlocking the formula or macro that makes arduous tasks effortless. Even as I delve deeper into coding for data transformation and analysis in other more sophisticated tools, my foundational understanding of data structure is rooted in using Excel. I’m sure many of you share this sentiment.

Fortunately, Excel is here to stay, with Microsoft continually enhancing its functionality to match our evolving workflows. However, as creatures of habit, we often overlook these new features; we stick to what we know.

I’d like to highlight the Data Types feature, which enhances data, making it more dynamic, thus making analysis more robust. It’s straightforward to use and can save you valuable time. You might be surprised at what you can do without running to your web browser!

There are three Data Types available: Stocks, Currencies and Geography.  We’ll explore Stocks in detail as a case example:

How It Works

Imagine you’re tracking a prospect’s portfolio that includes stocks in Apple, NVIDIA, and Microsoft. You enter their ticker symbols in a column, select those cells, go to the Data tab, and choose Stocks.

Excel will then turn these symbols into linked data fields. If you see an icon next to the stock name, it means it worked:

Clicking on the floating menu next to the cell will give you more than two dozen data points to choose from:

Let’s say you are interested in stock price simply select that option and the data will then be added in a new column — Voila!

Then, you can just multiply the stock price by the number of shares:

Even better, the next time you open the spreadsheet, the stock prices will update automatically. You can also manually update the data by choosing Refresh from the Data menu.

Other Use Cases

Stocks: Another great use for the Stocks type is business valuation. To assess the value of a privately-owned company, create a custom list of similar publicly-traded companies and use the floating menu to add useful info like employee counts, market cap, and price-to-earnings (P/E) ratio. Instead of copying and pasting from a web browsesr, you can gather all this info using Excel.

Furthermore, you can pair the Stocks information with the STOCKHISTORY formula to generate a list of historical prices for a selected date range without exporting from another website.

Geography: The Geography type is great for enriching a list of addresses. By applying it to a zip code, you can get city/town info like population, county, state, and coordinates.

With the Power Map add-in, you can map out your prospect’s locations. From the county data, you can access more info like the largest city. From state data, you can get Census info like median income and home values. In no time, your simple dataset can be transformed into something more insightful.

Currencies:  Using the Currencies type, you can get updated exchange rates by entering the abbreviations for the two currencies you want to check, for example, Hong Kong Dollar to US Dollar (HKD/USD). Excel will give the rate, which you can then use in your calculations. This makes it easier to evaluate an international prospects’ wealth, especially one with a lot of foreign holdings.

Summary

Excel’s continuous evolution and feature expansion ensures it remains an invaluable tool for prospect researchers and data analysts. Hopefully, the Data Types feature will make your workflow more efficient. By leveraging these innovative tools, you can unlock new possibilities and insights, making your data work for you more effectively. So, take the time to explore these features and see how they can transform your approach to data.

 

 

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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.

***

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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Data insight made easy: using RFM scoring to find great prospects

This week’s article is from the keyboard of my colleague, Kenny Tavares, Senior Researcher and Data Insight Consultant. I asked Kenny to share his knowledge today on one of the most straightforward fundraising analytics projects we offer (which you can also do at home!) – RFM analysis. ~Helen

As fundraisers, when we think about data analysis today, we’re likely to drift into the world of complex modeling practices that predict what donors are most likely to give based on a wide variety of factors. Go to any conference and you’re bound to hear about the wonder of Big Data. But if you work in a smaller organization with fewer resources, you may have walked away feeling like none of this was for you. Let’s face it, just in terms of time spent, these projects seem inaccessible.

However, we all need to understand our donors better. The data we keep can unlock critical new insights for our organizations. Data analytics has benefits for all of us, so we all must find ways to sort through the information we have so we can make better decisions. But how to do it?  Let’s start by assessing our top donors using three important metrics: an RFM analysis.

What is RFM?

Created for the purpose of corporate direct marketing, RFM is an analytical method used to determine an organization’s best customers — or in our case, donors. It has been around for more than 40 years, but a popular method for performing RFM analysis is credited to Arthur Middleton Hughes in his 1994 book Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable, Customer-based Marketing Program. In the book, Hughes describes the method of binning three attributes into five ranges, which I will briefly describe in a moment.  These attributes are:

  • Recency (R): In our case, how recently a donor has made a gift
  • Frequency (F): How often a donor makes a gift
  • Monetary value (M): How much a donor has given

For fundraisers, these three areas are important because of what we know about donor affinity:

  • Donors who have given recently are more engaged than lapsed donors
  • Donors who give regularly are more engaged than infrequent donors
  • Donors who make larger gifts on average are more engaged than donors who make smaller ones

Because it’s easier to retain donors than to acquire them, understanding who your best donors are is a no-brainer.  Having a better feel for your donor pool allows you the opportunity to solicit them is a more efficient, cost-effective manner.

At this time, when predictive analysis has become more and more accessible, you might be asking “Why should I perform an RFM analysis?

It’s simple

To perform an RFM analysis, all you need for each donor is a constituent ID, the number of gifts they have made, their lifetime giving and the date of their most recent gift.  Compared to more complex predictive modeling, the threshold for performing this task is much lower, making this project manageable for most organizations. However, if your giving records are inaccurate or incomplete, now would be a great time to address that issue.

Once you have your data, you’ll need to score your records based on the three categories.  For each category, divide your dataset into fifths, giving the best fifth a score of 5, with the subsequent fifths scored from 4 to 1.  For instance, if you sort the last gift date in descending order, your top fifth – the donors who have given most recently, would receive a score of 5. Repeat the process with the total number of gifts (frequency) and lifetime giving (monetary).  In the case of lifetime giving, you may prefer to divide that figure by the number of gifts and use the average gift for your scoring.  Once you’ve scored each variable, combine them to create your final score.  The easiest way to do this would be to adjoin the three numbers to create the final score (ex. 555, 444, 333, etc.).

Now that you’ve scored your records, sort them by their final score and check out the results.  Perhaps you’ll find some surprises.  It’s all good if it improves your understanding of your donor pool.

It’s strategic

RFM analysis operates on the Pareto principle, or the 80/20 rule. For our purpose, it would suggest that 80% of our gifts come from 20% of our donors. Knowing your 20% is critical. Keeping this group happy will obviously have long term benefits for your organization, as long as you don’t give them too much attention. But don’t stop here; there are other donors to consider.

Now, you might be asking “What about the remaining 80%?” It’s a good question, because it doesn’t make sense to cast them aside. Take the time to become familiar with the other segments and develop strategies for these groups as well.  For instance, how can you motivate new donors to give again? How can you encourage loyal donors to increase their giving?  How can you re-engage lapsed donors? Knowing these subsets allows you to better target them based on their behavior. Ultimately, you can craft your message to these groups more efficiently.

It’s predictive

As I mentioned, our three variables, and what they tell us about the donor, make RFM analysis an exercise in predicting which group of people are most likely to give again. That said, it does lack the flair of a more sophisticated predictive model, which would entail the use of many more variables. However, the usefulness of your RFM analysis is ongoing. The more you learn about your top donors, the more adept you’ll become at identifying similar prospects. You may not be as quick as an algorithm, but your institutional knowledge is extremely valuable. Take the time to consider the following about your best donors:

  • How old are they?
  • What is their marital status?
  • Where do they live?
  • Where do they work?
  • In which industry do they work?
  • What are their job titles?
  • How large was their first gift?

I could go on, but you get the point. Exploring the data on your top givers will improve your ability to recognize and efficiently target promising new donors.

Conclusion

Your ability to analyze the information you have available is a critical component to retaining donors and discovering prospects. Regardless of resources, we all need a way to recognize what the data tells us. Applying RFM analysis is a great place to start; it might even whet your appetite for other data strategies. Once you start using analytics to discover insight in your data, you’ll find answers to questions you might not even have thought to ask!

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Exciting Announcement: HBG Data Insight!

I hope you had a safe and relaxing Fourth of July weekend! Our town cancelled its annual fireworks show this year, but various neighbors randomly filled the gap in their own way (ahem) so we still got to peep some here and there. Same with you?

The celebration here at HBG continues this week: We’ve been working on something very special over the past year, and today I have some exciting news to share – cue more fireworks!

As we all retool and strategize to be successful in this new normal, the project we’ve been developing could not have been ready at a better time. We are delighted to announce a new initiative here at The Helen Brown Group specifically designed to help nonprofits identify and get the information they need on their best donors.

We’ve formed a new unit called HBG Data Insight (DI).

While the unit is new, you’ll definitely recognize the DI team as long-time HBG talent. Tara McMullen-King is the new unit’s Lead Consultant, and senior staffers Kenny Tavares and David Hargadon are the DI Consultants filtering and transforming data into insight.

Why HBG Data Insight? Why now?

Over the years we’ve been frustrated seeing nonprofit colleagues unintentionally left stranded by talented specialists who provide analytics services…but then move on. The nonprofit pays a lot of money for the work and they may get a fantastic group of new prospects, but when the analytics consultant goes away to their next job, then what?

Circumstances change quickly – especially these days – and algorithms have a very limited shelf life.

And all of the AI and machine learning and data science and screenings in the world won’t tell you which living, breathing, human being amongst the list of hundreds is genuinely interested in your cause and has a personal connection to your nonprofit. Or which ones have family wealth. Or have made multiple philanthropic gifts through a DAF. Data insight and screenings alone just can’t do that.

We decided it was time for a practical, integrated, affordable, solution.

So over the past year, we partnered with a group of our dedicated consulting clients to beta-test a unique service: data insight married with prospect development:

Data insight plus research.

Data insight plus due diligence.

Data insight plus prospect management.

We found that most wanted all four woven together: insight, research, due diligence, and prospect management to take advantage of new opportunities and ideas as they arose.

We’ve helped our clients use their own data to answer these questions (and quite a few more):

  • Who are our most likely donors right now?
  • How should we prioritize our prospect pools?
  • How can we segment our wealth screening results to find top prospects faster?
  • Do we have the right people in the right portfolios?
  • Are we allocating our frontline fundraisers in the right way?
  • Can we better predict what our fundraising goals should be next year?
  • If we can’t do events now, what is our next-best option of engagement?
  • Where are our best planned giving prospects?

Our insight services range from descriptive analysis projects and RFMV analyses, up to and including predictive modeling. For some we have integrated wealth screening information that they had; for others we helped them get the most out of a new screening. We’ve even gathered a proprietary set of additional data to help us surface more prospects.

When you work in an integrated way, insight questions like the ones above can rotate and be added to continuously as new questions arise.

Marrying ongoing data insight with the rest of prospect development and the frontline team makes sure that you’re introducing the right – vetted – prospects into the pipeline continuously.

Don’t just take our word for it, here’s information from a survey of nonprofits done by IBM.

  • 78% of nonprofit leaders with analytic capabilities reported higher effectiveness in performing their missions.
  • Nonprofits using analytics had more efficient operations and increased staff productivity.
  • However, the survey found that 48% of non-profits indicated that technology was a primary barrier to advancing analytics, and 41% indicated that talent was their primary barrier.
  • Nearly three quarters said that budget was one of top three barriers to using analytics.

Honestly, that last bullet point just floors me. It doesn’t have to be that way.

What does partnering with HBG Data Insight look like?

As I mentioned, our goal is to help clients affordably make data-informed decisions. To do that, we primarily use an integrated, dedicated, prospect development approach, which is something we’ve specialized in since 2005.

We provide expert consultants for a contracted number of hours each month for at least three months. That commitment allows us to keep our price-point lower and bring clients real value. It also allows us – together – to customize what happens during those hours to exactly what you need.

Don’t need the whole burrito of data insight plus prospect research? No problem.

If you already have internal prospect research capacity, that’s great. Our data insight team can work with you on a project basis, too, and we’d be happy to partner with your researcher (or research team) to provide results they can build on.

Check out our website to see more detailed information on the services we’re offering. If you have an insight project in mind that isn’t listed there, we’d be happy to talk it through with you.

HBG Data Insight: Practical. Integrated. Affordable.

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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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Bookmark These 3 Great Analytics Software Resource Lists

London Eye by Scott Liddell

London Eye, by Scott Liddell, www.scottliddell.com

There are tons of free and fee-based analytics tools. Some are designed for complete novices and others are meant for only the most experienced data scientists.

Wherever on that spectrum you and your team feel most comfortable, there are software tools to help you get the answers you need from the information layers in your database.

New analytics and data visualization tools are being developed every day. Where can you find out which program(s) you should use? [Read more…]

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Fundraising Analytics ABCs – Visualizing Data

What are the best representations of information that you’ve ever seen? For most people, they’re the ones that are the simplest to understand. The ones that display information clearly and move people to action. Or inaction:

Even in a different language, if we understand the context, the message is still clear.

That’s what good data visualization does. It takes information and lays it out in the simplest of terms to get across what needs to be communicated.

KISS

There’s no getting around the fact that data analytics is intimidating for most of us, and that it produces a lot of really interesting factoids that can move people to action. But when you’re trying to get across complex information, there are still ways to keep messaging simple. In fact, the more complex the information, the more imperative it is to KISS (Keep It Simple, Stupid). Or, in the words of Joey Cherdarchuk, director of operations at DarkHorse Analytics, Data Looks Better Naked.

THE MASTER AT WORK

If you haven’t seen Hans Rosling’s fascinating TED Talks using his free software GapMinder or watched his fascinating holographic data visualization on YouTube, you really should take the time to be impressed. And inspired.

Even if you’re not a data analytics person, watching Dr. Rosling will help you think about presenting information better, whether you’re a frontline fundraiser, researcher, social media guru, consultant or …just about anyone. Okay, go watch those two videos and then come back and we’ll talk more about available resources for visualizing data.

RESOURCES

Now that you’re inspired to do something different, let’s talk about what’s available for manipulating and showing off the great conclusions (or continuing questions) that your data is begging you to share.

Enter the graph, the moving chart, the GIF, the mini movie, the podcast, and the interactive illustration, to name a few, all made possible by recent entries into the market from companies including (in alpha order) Advizor Solutions, a flexible visualization tool that is especially good for looking at annual giving results and gift officer portfolio performance; Factary, whose product Atom allows you to visualize relationships between people, organizations and entities; Rapid Insight, a modeling and illustration package; and Tableau Software, an interactive visualization tool that allows for on-the-fly graphic shifting and drill-down-into-the-data capability.

What’s free?

NodeXL, which adheres to Excel like an elbow-length glove to allow you to manipulate and visualize relationships and social media interactions. Hans Rosling’s Big Data manipulator, GapMinder, which he uses in the aforementioned videos, is also completely free to download. And there are lots of others.

Where are some examples of visualizations?

Here is a terrific collection of resources from Andy Kirk, a UK-based freelance data visualization design consultant. And Mike Bostock of The New York Times has pulled together a great compilation of compelling visualization tools.

Finally, we’d like to leave you with a quote by David Schmitt, head of Performance Strategy and Planning Analytics in the finance division at the Intercontinental Hotels Group (IHG). Schmitt’s team is responsible for communicating information about IHG’s financial performance in a way that is entertaining, compelling and clear. You wouldn’t think that corporate financials would lend themselves to music videos, but that’s one of the tools Schmitt’s group has used to visualized data. For him, “data isn’t the point; numbers aren’t the point—it’s about the idea.”

It looks like our Excel pie chart days are probably over, thank goodness. We’d love to hear about any tools, resources or sites that you’ve found to help you visualize data, too! Please share them here in Comments, so that all of us can check them out.

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Fundraising Analytics ABCs – Donor Modeling

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 these terms. In each, she will describe the method and give examples of how they can be used. To continue our series, we describe the questions that Donor Modeling can answer.

Let’s begin with a case study:

A museum is in the planning stage to launch a major fundraising campaign. Their last campaign was over 5 years ago, and while they had a number of significant gifts, the coming campaign will require many more major gifts in order to be successful. After developing a table of gifts for the campaign, it quickly becomes apparent that there are huge gaps that need to be filled with prospects at every level. Significant prospect identification needs to happen.

To score the museum’s database and identify the top prospects, the museum decides to use a technique called predictive modeling, also referred to commonly as donor modeling.

What is donor modeling?

Donor modeling uses statistics tools to score a group of records using a variety of methods, including regression analysis, clustering, decision trees, neural networks and support vector machines (SVMs) amongst lots of others. Let’s take a look at just one of them, regression analysis.

Regression analysis uses calculus to find the slope of a line, which helps us visualize trends in the data. For example, we could see (based on a number of factors) which groups of people in the museum’s database have the most capacity to give as well as affinity, or connection to, the museum.

Here’s a standard matrix that is often built for major gifts programs. After downloading records and using regression analysis to score the group studied, prospects would be shown along the slope of the red line based on their relative affinity with the museum and their capacity to make a major gift.

Affinity, or “how much they love the museum” might be measured by the number of times someone attended events, or donated in consecutive years, or bought tickets to special exhibits, amongst other things. Capacity, or “how much they can give” might be found through primary or secondary research, such as a visit, prospect research or an electronic screening.

A graphic describing the relative level of a group of prospects’ affinity using a number of hearts (ranked on a scale of 1 to 3) and the relative level of their  gift capacity (ranked 1 to 3) by dollar signs might look something like this:

In this example, the top-right box represents those with greatest capacity and affinity for the organization, and the bottom-left box shows those with the least.

If you were the chief development officer at the museum, whom would you want to approach first? Your answer is likely to be those in the top right group. Unfortunately most of the time that group is also the smallest population among the scored groups, and are usually the donors you know fairly well.

Whom to select next, then? Often, two of the largest groups, represented by the larger boxes, are the $$$ ♥♥ and the $$♥♥♥ groups.  And of those, it might be hard to decide which to choose.

So, your next donor modeling study might be to look at the museum’s past track record with each of these two groups. What is your level of success in cultivating each group? What motivates them to become major gift donors?

Donor modeling helps answer those questions. The characteristics of top level donors are compared to various segments of the pool, and their scores help bubble up the best future prospects.

What else can you use donor modeling for?

Although it’s most often used to identify major gifts prospects, donor modeling can also rank groups like these:

  • Annual giving prospects who are most likely to renew
  • People who are likely to be good board/volunteer candidates
  • Planned giving prospects
  • People who would be great prospects for a specific project or campaign (like a library fund, or for endowment)
  • People who would be most likely to accept a request for a visit
  • Top level annual giving prospects
  • Prospects best suited for a particular gift officer or volunteer

Donor modeling can even help determine the best ways to acquire new members for a member recruitment campaign. It’s a powerful tool to help your organization identify new donors, whether you’re in a campaign, thinking about a campaign, or just looking for new donor prospects.

What do you need to know?

Our series on the ABCs of fundraising analytics continues next Thursday, September 19 with a look at data visualization.

Do you have questions about donor modeling or would you like to see it at work at your organization? Contact us at info [at] helenbrowngroup [dot] com for more information.

 

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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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