Hi, everyone. Thank you so much for being a part of our presentation today, Measuring Affiliation through RFM Analysis. I'm Jess, AVP of Data Analytics at CCS Fundraising, and I'm joined by my colleague, Jacob, Director of Data Analytics, and Ashley, Senior Director from Hagerstown Community College. In this presentation, we're going to show you how to calculate a recency, frequency, and monetary score, otherwise known as RFM scores, to generate your strongest prospects. We're going to accomplish this by using an easy-to-use formula. We will cover what RFM scores are, how to apply them and use them, not just for your philanthropic engagement with your institution, but also how to apply similar methods to things like event attendance and membership. After we go through the methodology of how to calculate these scores, we will show you how to layer on wealth capacity to get not only those that have the strongest affiliation to you, but those that have the ability to make a meaningful gift. We are thrilled to have Ashley partnering with us today to show RFM in action and how the scores helped her to pinpoint her strongest donors and share some of her successes. To use resources as efficiently as possible in a time when we all have very limited bandwidth, RFM scores help us to narrow down to those top prospects. To find your best prospects, we use a simple Venn diagram. Your best prospects have both the strongest affiliation, which is achieved from the RFM scores, and they have the capacity to make a meaningful gift, which is determined from wealth screening. To start to tell a story of RFM, we want to look at some key giving variables to determine the score. R is recency, or the last recent gift date to your organization, average frequency, or how many gifts they've given to you in your lifetime. And monetary is total lifetime given to your institution. Before diving into RFM some more, let's discuss some pros and cons. Starting with the pros, it's a great starting off point for you to really zero in on your best prospects to look for opportunity as well as optimizing your portfolios. It's a quantitative way to examine those who are the best prospects based on gift history, and it's a really easy to use tool and really easy to implement. Moving on to the cons. Although it's a great starting off point, it does not provide the full picture. It does not take into account certain behaviors such as volunteering and event attendance. And although someone might have a low RFM score, they might be worth engaging because of interest in attending events, for example. We all know that fundraising is an art. To bring the science to it, we use RFM scores to let the data guide us to the best major gift prospects. The highest scoring prospects would be those that have given recently, frequently, and made large gifts. However, what RFM helps us to do is also focus in on those who might not be on your radar because they have not made a major gift, but they have, for instance, given frequently and recently. Those that have top RFM scores that are based on frequency and recency are really good candidates for legacy or plan giving efforts. Prior to building your RFM score, we suggest defining a time frame. For example, would you like to look at the last 10 years or your lifetime giving to your institution? Once you've defined your time frame, you'll want to export data from your CRM into Excel. We recommend at a minimum that you have the following columns, ID, first name, last name, gift date, total number of gifts, and total giving. When you export the data, remember to remove those that are deceased and non-donors since we're only giving RFM scores to living donors. Then you'll assign scores for each of the R, the F, and the M, and we will use 1 to 100, for example, with 100 being the highest for the R, the F, and the M. Then you'll take these three scores and you'll get that RFM score by summing up the total where the highest will be 300. Once this is done, we really recommend that you double check your work just to make sure everything was done accurately. Now that I share the methodology and rationale behind RFM scores, Jacob's going to show us some formulas of how to calculate RFM scores easily. Thanks, Jess. I now want to talk about how you can use a simple formula in Excel to generate RFM scores for your donors. So when you're getting started in this process, it's important to keep in mind the fields in question. You want the last gift date to generate the recency scores, the number of gifts to generate the frequency scores, and the total giving to generate the monetary score. In addition to the last gift date, number of gifts, and total giving, you also want the donor ID for each of your donors associated with these last gift dates, number of gifts, and so on. Because as you generate the scores, you want a reliable way of getting those scores back onto the donor records, and the ID is the ideal way of doing that. So creating scoring. The final RFM score is just the sum of that recency, frequency, and monetary scores. For our purpose, each of these scores is going to be from 1 to 100, so that the RFM total is a simple 3 to 300 score. So that column isn't so complicated to think about. Instead, we need to focus in on those recency, frequency, and monetary scores themselves. They each use the same logic, so we'll be able to take one example which will look at recency here. So how do we get a recency score? And it's the same logic for how we're going to do a frequency score or a monetary score. Ultimately, that logic is based on a percentile rank. What a percentile rank does is it looks at a value within an array of values. So I'm looking at one record's last gift date in the context of all my donors' last gift dates to think about where does that donor's last gift date rank in that scheme of the most recent versus the most distant donors. It's a simple formula in Excel. It says percentrank.exe. It will be the key of what we do, and then we're going to do a couple simple transformations to turn it from a 0 to 0.99 percentile to a 1 to 100 score. So let me take a moment to explain the percentile rank function itself and then the simple transformations that we do to it. So the formula takes as its inputs a couple three main arguments here. The first argument is the array. This is the total data set that you're examining. So for the R score is going to be all of your donors' last gift dates. For that F score, it's going to be all of your donors' total number of gifts and so on, right? And that's the kind of framework by which you want to evaluate the particular donor is that array. The second argument is that particular donor's value. You see it's the X here in the formula, right? So our array is going to be the column B, and then the value that we're examining here is just this first 11-5-1992 value, right? So we're saying, where does 11-5-1992 stand on that list of all the gift dates that we see in the system, right? So that's the X value, B2 here. And then finally, this last argument, you see significance, right? That significance is how many decimal points do we want Excel to calculate out to? For the purposes of getting a 1-100 score, we want two decimal points. So it's going to get us, this percent rank is going to get us any value between 0.00 and 0.99. And we're going to transform that to a 1-100 score. The other parts in this formula are just a matter of completing a transformation. So percent rank generates the real value. We multiply that by 100 to go from 0-1 to 0-99. And then we add 1 to get the 1-100 score that will be useful when we're really thinking about a score, not just a quantitative variable. The same method that we use here, generate recency, is identical to what we would do for the frequency or monetary score. So in each of these cases, we're thinking of a percent rank where we're thinking about the array that we're trying to rank within and the value we're ranking. And then a couple of transformations to take that value, turn it into a score. Finalizing and checking work. So getting the final RFM score is easy once you get that percent rank formula done. It's as simple as adding the R, the F, and the M. A simple sum will do that for each constituent in here. I would recommend copying and pasting all these values and saving them as values themselves so that you don't have the formulas because that percent rank will update as the values change. So you don't want to have that as a live formula in your workbook, but that's something you want to approach, score, and record, and move on. The other thing is you should spot check this RFM score to make sure it makes sense and that you've got that formula right. So for instance, some simple checks you can do. The most recent gift, last gift date, should have the highest R score. If someone has one gift, they should have a one for an F score, right? Your largest donor all time, they should have a 100 for that M score. Do a couple checks like these to make sure that your RF and M score are all sort of logical and they're adding up together to reflect the actual giving data that you've got in your workbook. And I want to take a moment to pivot to thinking about some further applications for RFM. You can use RFM for giving and that's a great starting place, but you can use RFM for engagement metrics as well. And this is a recommended thing if you're trying to think about how are people engaging in terms of ticketing if you're an organization that's collecting tickets for events, volunteer data, anything like this can be tracked using an RFM, right? And this is that same idea of that percentile rank, but it may be that instead of last gift date, total number of gifts and total lifetime giving, you're doing something like for a volunteer RFM score, last volunteer date, how much time have they spent volunteering if you're tracking that, the number of times they volunteered if you're tracking them, right? For a ticketing situation, when's the last time they purchased a ticket, you know, how much has been spent on tickets, how much, how many times have they gone, have they purchased the ticket, right? These sorts of things can be used again for that kind of three-dimensional score. It's the same idea where you're using a percentile rank, translating this as a more general way of thinking about historical engagement rather than just looking at the lifetime, you know, big value of lifetime giving, lifetime, you know, volunteer engagement, etc. With that in mind, I'm going to pass it back over to Jess to talk about wealth screening. To make your analysis as robust as possible, we suggest laying on RFM scores with the ability to give, which is achieved from wealth screening. There are different screening vendors that provide capacity estimations as well as biographical information and giving history. It's crucial to consider not only gift capacity but also the source of an individual's wealth. For instance, if their wealth primarily is in stocks, targeting them for a stock gift may be the most appropriate gift option. Wealth screening services use publicly available resources to compile their final gift capacity, so it's important to really understand that there is some limitations when it comes to screening. We cannot screen those that are overseas or those that are lacking publicly available assets, such as trusts, might have a really conservative value for that gift capacity. Gift capacity comes in a high and a low range. In the sample chart, we're showing you the low end of that range. The graph breaks down the results by estimated gift capacity, which provides an evaluation of how much a household can give to all charities they support over the next five years. So if someone can give $100,000, that means they can give a five-year pledge of $100,000 or five annual payments of $20,000, for example. That's why it's really important when talking to these households to really understand where your institution falls in their philanthropic priorities. And also to note, we do find that the wealthier prospects tend to have a more conservative capacity because their assets might be in undisclosed sources, such as trusts. That's why we really call this directional. Nothing replaces research done to determine a custom gift capacity. Some of the resources that are used to determine wealth include publicly available charitable donations, Federal Election Commission, GuideStar Foundation, LexisNexis Real Estate, Biographical Records Archive. This is our world famous CCS onion chart. This provides a quick snapshot to show you our process of how we went from everyone in your CRM to looking at RFM scores, narrowing down top prospects, looking at the top RFM scores and screening results, and then finally layering that on with giving and last gift date to find your top major donor prospects. We are ultimately suggesting to look at the prospects in the focus circle in gray, but we call this an onion chart because you can peel back the layers. So once you've exhausted the focus list and are looking for more opportunities, you can look at the tier one list to find more opportunity. I'm excited for Ashley now to walk us through RFM scores in action at HCC. Thanks, Jess, so much for the introduction. I'm excited to be here with you all and equally as excited that you've chosen to listen to this presentation. I know the data analytics is not the something that everybody is jumping up and down to say let me be a part of that and let me listen to that presentation, but it's so incredibly important and I found through my work with CCS and the success of our campaign how important the data analytics, the RFM scoring, how that was crucial to the success of our campaign. So I'm excited to share with you some of the results of our success because of our RFM scoring with CCS. So the overview of our work with CCS at HCC was the RFM scores were used to rank each household in the CRM on a scale of three to three hundred and following the completion of the RFM analysis, top scores were screened for estimated giving capacity through a wealth engine. CCS then explored the results and analyzed prospective donors through two segments. First was our donor universe, so those were households that were identified as on our radar and then the non-donor universe and that was all other households. 18 months following the completion of the analysis, the initial analysis, HCC provided CCS with a refresh data to conduct a follow-up analysis and this analysis was intended to assess the accuracy of the RFM scores and the performance of the prioritized prospects. CCS's impact analysis focuses on the 462 top prioritized prospects that they recommended in 2021. So the process with the RFM as it compared to our work at HCC and the data collection analyzed two data files containing over 28,000 constituent records. With the RFM in scoring, CCS supplied RFM in scoring on each of the 5,061 screen donors based on giving. With the wealth screening, there were 21, a little over 21,000 top households for estimated wealth and giving capacity. And then exploring the results, we prioritized current donors and identified new strong prospects. All of that segmented and prioritized for us 462 households. So reviewing the relationship of the new giving since 2021 to our RFM score, after receiving the file of the updated lifetime giving, CCS analyzed the distribution of new giving since the May 2021 and the original RFM in scores. You can see this chart here really like how it captures the growth of our donor constituents between the different RFM in scores. And you can see that there's a positive correlation between RFM in scores and new giving. New giving from those with an RFM score of 286 or more was 14 times the giving as those under at 151 RFM score. It's pretty significant. So when we look and evaluate our major donors, the top 50% of the RFM in scores gave 23 times more than the bottom of the 50% of scores. And the largest new gift for the top 50% of RFM in scores was a little over $500,000. And then the largest new gift from the bottom 50% of those scores was $29.5,000. And you can see in the slide too that our average new giving for the top 50% was about a little under 6,500. And then the bottom 50% was right at 2,500. 51 households gave $10,000 or more since May of 2021. And at this chart at the bottom of the screen, you can see that I'm in the range of the 286 to 300. We had 24 households. Their giving THC was 1.8 million. Capacity was 2.7 million. And we secured 66% of that. So I think it's pretty significant. And I don't think we would have had the success that we did in this category if we were not using RFM scores to kind of lead our ways and the conversations we've had with our donors. So some outcomes that I want to highlight of the 462 priority households that CCS identified, 35 households were validated as prospects for our campaign. We raised a little over $2 million. 18 of our prospects met the minimum $25,000 that we require to establish an endowed scholarship. We did secure a $1 million gift. And we received five estate gifts, one of which was valued at over $500,000. And that was a donor that was on our radar. We had been in communication with him. He attended a donor recognition ceremony that we had and was just amazed at the work that we were doing, the students that we showcased that evening, and said, I'm going to change my estate plans and I'm going to make HCC number one. And so that was significant for us. And we knew that he had the potential because of the RFM scores that we received. And so we definitely did the work needed to help secure that change. So some of the other things that we did not realize, some of our untapped potential, we were encouraged to have more conversations with individuals about their legacy giving because of what the score showed, which also led for us to have different priorities for our fundraising and our relationships. And then we also identified a need to increase our cultivation stewardship efforts with prospective and current donors from the college's board of trustees, our foundation board, our alumni board, current employees, and college retirees. It just really showcased to us that they are people that were closest to us and we weren't asking them the right questions as far as their giving. Thank you so much for watching our presentation today. We hope that you enjoyed it. If you have any additional questions, please email us at analytics at ccsfundraising.com. Thank you again.

RFM scores

prospects

philanthropic engagement

data analysis

donor targeting

Hagerstown Community College