Let's discuss analytics with Alexandria Mannerings.
Hello and welcome to Driven's Fundraising Superheroes podcast. I'm your host, Sabrina Sciscente, and as an innovator in nonprofit technology our team at Driven is determined to help you unlock your true fundraising potential. You can give us a visit at trustdriven.com if you'd like to learn more. We would love to get in touch with you and talk about your fundraising goals.
So are you making time for your data analytics? Understanding your data is crucial to not only help you work smarter, but to give back to your community. Alexandra Mannerings started Merakinos to advance how we use data with a special focus on those people and organizations new to the data space. Together we can transform a world, one data-inspired decision at a time. And Alex is here to show you how. She joins us today to discuss how you can tackle your nonprofit analytics more effectively. So thank you so much, Alex, for joining us on the show for the second time.
Yeah, it's always fun to talk with you.
Can we start off by talking a little bit about what is data analytics and how can they help organizations work smarter?
Yeah, well, I think to answer, what are data analytics you have to start with what's data? I think that this is often a question that seems so easy to answer, but I think we default, especially in the nonprofit based assuming that data is either money number of dollars or data might be counts of things. How many volunteers did you have? How many people attended your program? But really, data are anything that you can capture and store in a way that you can go back to. Data can be images, data can be sound. You can do analytics on that, though they're a little bit trickier. But you can data can be free text, it can be people's responses, thoughts, comments on things. Data can be medical outcomes. They can be emotional outcomes. Right. If you're doing, like, a psychological review on traumatized children, those outcomes are data. And so it's really important to make sure that we cast our net as broadly as we can when we're talking about data, because that influences then the kinds of analytics that you think about and what you're actually able to do. So analytics, in its simplest sense, is taking any of those kinds of data and turning them into information that we can understand and use.
So I don't know if you've ever had this experience of, like, somebody just like, dumping information on you, whether it's this like, massive spreadsheet of stuff or like, dropping the whole pile of surveys on your desk or whatever it is. But that's raw data. It has not been analyzed. Analyzing takes that huge spread of data and compresses it into usually some summarization numbers. Right. Averages, totals. But it also can do things where it helps us understand details that are happening within that data. So for example, you might do a trend and say, is this trend meaningful? Is it actually going up? Is it actually going down, or is it just random variation? You can do analyses that tell you how different pieces of your data might move together. When this goes up, does something else also go up? So we talk about doing kinds of analysis where you are actually trying to get "did X cause Y?" And then analytics can help you answer those questions.
Yeah. Do you find that it's easier to start off with a list of questions, or can going in with just like an open mind and almost blind also be beneficial?
That's a great question. The danger with analytics is there are literally an infinite number of questions that you can ask. And so I think if you just go in being like, I'm going to see what the data tell me, you can very quickly go down rabbit holes that are not going to necessarily provide actual information that you need right now. Now, as an analyst, I love playing with data. I love going down those rabbit holes. If I had all the time in the world, I would do that all of the time. Because you do find some really interesting, unexpected things. So there are certainly times when just exploring and poking around and seeing what you find can give you things that you didn't expect. But because we usually don't live in a world of infinite time and infinite resources to be able to do those kinds of things, I do usually really recommend that you start by saying, what do we need out of this? What are our biggest challenges we're trying to solve? Why are we even approaching the idea of analytics? So I actually usually ask a set or develop a set of questions/answers to head into the analytics with.
And the first is looking at the broader organization, the broader efforts of the team. What's trying to be accomplished here and figuring out, all right, what is the most important thing to you guys? Right. So if you are a program manager, you want to understand the program, you want to understand what that program does to try to achieve the thing it's trying to achieve. Have you defined that outcome, that goal where you're headed, and have you defined the process that you're currently using to get it, and have you defined your constraints and your values? I think it's really important that you understand, what do we believe in here? Because the data can't tell you where to go. They'll tell you, oh, we see that students that got twice the number of hours with their mentor did better than students who didn't get that time. But if you look at the number of mentors that you have and realize you can't afford that number of hours, if equality is really important to you, you may choose. I don't want to have to segment some of my students and give them twice as many hours to get that better benefit and then leave the rest to not get that.
I would rather a lower ultimate benefit and make sure everyone gets the same number of hours. And I just bring that up as the fact that you have to kind of know what the boundaries are in your activities before you start asking the questions of the analytics, because it would determine the kinds of questions that you would want to ask. The other thing the way I like to talk about this, to give a concrete example that most of us use is when you open Google Maps. So Google Maps is an analytics machine. That is all it is, right? And when you open it up, typically you open up because you have somewhere you want to go. Now, the specificity of where you want to go may be different. You may open it up being like, I really want a burrito today. I don't know where I want to get that burrito, but I really want a burrito. And so you would use Google Maps to search around. You'd be a little bit more open and looking around and seeing what kind of burritos or Mexican restaurants you can find. Or like, if you're me, you open up, you're like, I got to get to school in 23 minutes because my kids are going to be late.
Right. So I've opened it up with a very specific destination and a very specific parameter on that destination. I need to get there fast. Right. Whereas with the burrito, you might be kind of flexible in terms of like, well, I'm not in a huge rush. I might pick a place that's, like closer, even if it might be a little bit slower, because maybe I'll bike there. You'd have different parameters on that. And what's really interesting with Google Maps, so once you tell it where you want to go, it will now give you two routes. It will give you the fastest route, and it gives you the most fuel efficient route. And again, this is where your values come in. Google can't tell you which one of those routes is better for you. You have to know what matters most to you. Does getting there fast matter the most to you? Does getting there efficiently matter the most to you? And maybe it's a balance. Maybe, like fuel efficiency is important. You want to reduce your energy consumption. But if it's 15 minutes longer to go the fuel efficient route, you may not pick that, but if it's three minutes longer, that's worth it to you.
Again, none of those are questions that analytics can answer. But analytics can tell you once you determine that, right? Oh, I want this route or that route, and it's going to make sure it gets you there within those parameters exactly how you want it to do.
And that's a really good example because values are really important. And organizations are doing work that's ultimately supposed to help some sort of community or group of people. And you have a really interesting article on your website that talks about the moral obligation that nonprofit have to use data and their decision-making. So I'd love to hear why data is not only important for productivity, but also for these moral obligations for nonprofits.
Continue the Google Maps analogy. I mean, imagine if you're an ambulance driver and you've got somebody having a heart attack in the back of your ambulance and someone's like the hospital we normally go to is closed. We've got to reroute. Let me pull it up on Google Maps. And that'll help us figure out how to get there faster. And if like you as an ambulance driver, we're like, no, this is a mission of the heart. I'm going to go on intuition. I'm going to figure it out as we drive. A very tongue in cheek example. But for me, that is why Data does have this moral imperative. We do things in nonprofit because we want to change the world. We want to change and address and solve these incredibly deep intractable problems in our communities, around us, in our organizations. And Data can be a very powerful servant to help that. When we choose not to use analytics to help us learn how to do better in our programs, learn how to accomplish the things that we're trying to accomplish better, to be more responsible stewards of the resources that were given. To me, that's a little bit like letting down that side.
You have a tool that is here that we can bring in and to not do it means that you're making a choice. And it may be a choice because of limited resources and maybe a choice because you don't have people around you to be able to do it. I get that part, too. This is not something you just wake up one day and you're like, okay, I'll do it right. But still, at the end of the day, if it's not something that we're willing to resource, that we're willing to invest in, that we're willing to try to learn, that we're willing to take the steps to at least long term, bring it into our organizations. We are handicapping ourselves. We're reducing our ability to do more with the resources that we have. We are reducing our ability to learn. We're reducing our ability to improve. And if at the end of the day, our goal is to reduce hunger, our goal is to help more students graduate high school. Our goal is to increase equity in our communities. How can you do that if you're not measuring how your programs impact equity, how your programs impact graduation rate, how your programs actually help get the people who need food, food.
And if you're not tracking, say, the efficiency of those things. And that's a loaded word. So I'm going to put quotes around efficiency because there's a whole conversation around that. But the idea of, are there ways that we could use the resources we have that get us more for the investment of those resources? If I've got 20 volunteer hours, someone coming in and they're going to help me, I'd like to make sure that I help or have the biggest impact with those 20 hours. But if I'm not tracking the impact that those 20 hours have or the impact that my $1,000 grant had, how do I know if Program A or Program B is a better use of that? Or how do I know if Fundraising Approach A or Fundraising Approach B does better? Right. You need to track those things and use analytics to help. You know those things.
Yeah, that is such a good point. The data doesn't lie. Right. So it is extremely helpful and kind of giving us the hard facts and what direction to go. I'm curious if you have any advice, because I know it can be really overwhelming to start. I'm not a person who's going with numbers, so when it comes to data analysis, I get very anxious. So for people who are kind of similar in my situation, how do you recommend they get started? What are the most important things that they should be focusing on?
I get that completely, and I understand it. I will also say that I think a lot of the nonprofit space has a little bit of PTSD around data, in part because I think in the past, data has been used as the big data stick. You aren't doing good, and I'm using data to tell you that you're not doing what I want you to do, whether it's from funders or from government. There's a lot of, like, external accountability and sometimes on measures that don't align with what the organization is trying to do. So if someone's like, well, you didn't feed enough people, like a total number of people. And if your point is like, well, I'm not trying to maximize the number of people to feed. I'm trying to feed the people who fall into this category, like maybe have medical needs. I'm really focused on those people who have medical needs, and we're giving them, like a medically tailored diet. So the total number of people that I'm feeding is not our goal. Our goal is to target a specific at risk group, and I fed 95% of that. So actually, I'm doing great. And you're telling me I'm not doing good.
And so I think that that can contribute to the sense of overwhelming. And so the first thing that I really recommend when trying to get started with analytics is saying, how do we want to learn? How do we want to improve? What are the goals that we are trying to achieve? Not them, not in an ideal world, not any of that stuff that doesn't let you up. You want to say why is it that I show up here today? Why is it that I'm doing this thing? And what is one piece of information would help me do that better. And better again, is a vague term that you have to define. What are you trying to achieve? What are you trying to change? And so if you can sit down and it will also depend on your role. So if you're an executive director, what is it that your organization truly is trying to do that if it did it, you could happily quit and say, we put ourselves out of a job like that was what we meant to do. And if you can define that to yourself and to your organization, I bet you can get to a point where there is also a number or a piece of data or a piece of information that could serve as your compass.
So you can start with just that one thing. So if you are an organization who tries to rehome pets, it might be as simple as how many pets per week do we rehome? Right? And that is something that is not overwhelming to track. It's not fancy math. It's literally one number that you can use to say, are we on track? Now, as you do this, somebody might come to you and say, hey, actually, it's not the total number of pets we rehome. It's how many pets who need a home have we rehomed? What proportion of unhomed pets are there that remain that we've rehomed? And so you could even start with one thing that's very easy to track and then realize, you know what, that actually isn't what we're trying to do. We're not trying to rehome 100,000 cats. We're trying to make sure there's zero cats without a home. And those are actually different things. But it's okay to start with one number. And then as you work with it and you see what you're actually trying to do and see if it aligns with where you're trying to go, you can modify it.
So when you realize it's not total number of cats and in fact, it's actually how many cats don't have a home, then you could go towards a proportion, right. How many unhomed cats do we have versus how many have we home? And then you might start tracking that. And now that one is one step more complex because that's really two numbers. Total number of unhomed cats and total number of cats that you got a home. But you're getting more sophisticated every time you kind of work on this. And so that would be really where I would start is just that one thing that you feel comfortable that can help you have that Compass to say, our ship is sailing in this direction. Are we pointed towards the thing we're trying to achieve, or did we get off it? And then what usually will happen is as you get more comfortable with that number. So let's say you have the proportion of animals you're rehoming, and you realize that you're at like 40% pretty consistently there's 60% of these unhomed animals aren't getting a home. Well, now you can try things that will potentially influence it.
So you could say, all right, let's try doing an adoption drive. And if you realize after the adoption drive for that week, you got up to 50%, you go, oh, that actually kind of worked. And then someone comes in and says, you know what? Instead of the adoption drive, let's set up Twitter accounts for our animals. I think that's going to get them some traction. Let's say you do that and you get up to 60%. Well, now you actually have some comparison. The adoption drive got you to 50%. The Twitter profiles got you up to 60%. You can make an informed decision. I'm going to go with the one that got me closer to my compass. And so now you might add in some metrics onto your Twitter accounts saying like, okay, we realized the Twitter accounts are working, but it's actually not all the Twitter accounts that were working. Twitter accounts work really great for the adorable animals or the puppies or whatever it is, but they're really not working for our mature path. And so, again, you're just slowly adding levels of sophistication to whatever extent you can handle to just help you home in on the things that make the biggest difference.
And I'm hoping people are seeing that these analytics are not driving the bus. They're like your speedometer and your engine temperature and your RPMs. They're just giving the driver who is in full control pieces of information that can help them refine the drive to make that drive more successful.
Yeah, definitely. Sounds like you just got to get your feet wet a little bit and try test, see what matters. I guess experience will come knowledge and expertise.
You don't have to feel like you have to do it all yourself. I mean, there are lots of people like me out there right where this is what we do. We are geeky and we live and breathe data, and we love it. And we're all very happy to have conversations with you. And it doesn't mean that you have to just outsource it and leave data to somebody else. But somebody could help you figure out where is the best place to dip my toe in, where could I get started? And then you could practice it and work on it and refine it and touch base again with somebody who could come in. Also, keep in mind that a lot of the software we use has analytics baked in. You guys are a great example. You have a lot of stuff already there where somebody very thoughtful or somebody's teams of very thoughtful companies have said, what are the kinds of questions that most nonprofits have around fundraising or around money, around the people that we reach out to, and those are already part of it. So your fundraising software can have that. Again, if you use social media, most social media platforms have some basic analytics that will give you some idea of your reach that you can get started with.
There's free software like, I think Hootsuite gives a free trial, things like that to track social media. You can try that. And again, just get some general information about it. So there's a lot already out there that doesn't require you to run analytics on your own. You don't have to build your own spreadsheet. You don't have to have a lot of technical experience. What you have to have is the willingness to ask the question and look for those answers out there.
I love that you could definitely get started with what you have around you. Like it doesn't have to be the scary thing. So after an organization to get started, how do we decide what KPIs are most important to them? We talked about kind of testing and trying new things, but how do they nail down okay, these are the things that we're going to use to measure performance.
That is a really great question. And I don't remember the person who said it, but it's the idea that not everything that matters can be measured, and not everything that is measured matters. And that is very true. I'm a huge proponent of analytics, and there are some things that analytics can't measure. The first thing that you do have to figure out is one of the things that we do can be captured. We talked about data is something that has to be able to be capturable. So there may be things you do that can't be captured. One of my favorite examples is if your nonprofit is a religious nonprofit, you're a Church or a synagogue or something like that. My mom's Catholic Church. Their goal is to save souls. And I have not come up with a way yet that I can measure how many souls have been saved. Right. And again, a little bit tongue in cheek, but it's true that's their ultimate goal, but it can't be measured. And there are many other things. Pain is a great example that as a medical professional, the medical field has struggled so much to figure out how to quantify pain.
And so we've relied instead on things that we can measure. And those can be like self surveys. So I asked somebody on a scale of one to ten, how much pain are you in? And so when we are picking your KPIs, I fully recognize that some of our organizations out there are going to have things that are very important to you that can't be measured. So instead, we're going to pick things that we either know through research around these things that are difficult or impossible to measure will get us there, or you're going to pick things up to your best guess get you there. Right? So again, for the Church, I can't tell you if you're saving souls, but maybe the best thing you can do is how many people attend your services. Or maybe we actually need to measure engagement. Not just how many people are attending your services, but how many people attend more than one thing at your Church. They go to services and they come to a meal program during the week or whatever it might be. You can decide what is it that I think gets us closest to that?
And I don't know if we actually realize we've been using KPI. Did we define that as a key performance indicator? Because a lot of people throw the term KPI around and be like, what are you talking about? It's an acronym for key performance indicator. So the reason that I'm really focused on what is it that we're trying to do? You want your KPI to be relevant, get at those things that are the most important for your organization. But be measurable. It has to actually be something that you can track over time. And then I add a third thing, which it needs to be actionable. So is it something that you can actually directly influence? So many of us work on things that maybe our nonprofit cannot actually completely address? We do one little part of it. So I work with some nonprofit that are in the Appalachian region, which is one of the most economically depressed regions in the United States. And we talk a lot about that. Really, the thing we want to change is how many people are living in poverty, how many counties are still economically distressed. But any one nonprofit is probably not going to change the number of counties that are economically distressed.
But what we can do is use evidence and research to get to interventions that we know affect individuals. Right. So if I can say, well, if I do an entrepreneur program, I know that the people who complete my entrepreneur program double their income. All right. Well, I can assume that if I could double people's income, that's going to help lift the county out of economic distress. So I'm going to measure how many people I get to double their income knowing that long term, that's going to change the thing. So I'm not going to necessarily have as one of my primary KPIs. So I think it should be something that sits on your dial that you see. But I'm not going to have as one of my actual KPIs, those economic distressed counties. I'm going to have the sub things down. So I'll watch the economic counties. I'll make sure that I'm understanding that context and knowing that that's our ultimate goal. But what will be primarily on my driving dashboard is number of people who attend my programs and of the people who attend my programs, what proportion of them are doubling their income?
Okay, so just kind of keeping an eye on it. And again, kind of like seeing what works best for you and what is moving you forward.
Yeah. And I think, again, you want it to align with the things that motivate you and that you can respond to. So if you're looking at a number and you're like, I don't know what to do with this number, how does that help me? What am I going to do with it? I always joke about the salt that says not genetically modified. I'm like, strictly speaking, that's true. But that's because there's no genetic material to modify. Right. Like, you cannot genetically modify salt. And so we have a tendency sometimes to make KPIs that are that way. You're like, this is in fact, true, but it doesn't give me anything that is valuable. It doesn't help me do anything. It's not going to help me make a decision about which salt to get because they're all genetically unmodified, because they have no DNA. So you also want to make sure when you're picking these KPIs, that when you look at that and you get information back. Okay. I know what to do with this. I know how to respond to this. And that's why I said if you've got a big gnarly problem that you're solving, having that as one of your KPIs, I do think is important because it will inspire you.
You'll understand what is the work left to be done, but you need to be like, is the thing I'm doing getting us there. So you want to pick then some sub KPIs that are more actionable to say, like, yes, I'm going to fund this program more, maybe pull back some funding from this other program, or we're going to tweak this program and see if we can adjust it and get better results, more towards what we're trying to get to you or in terms of your fundraising activities. Right. It doesn't help me to just say you want to know, okay, we're 75% of our fundraising calls. That's important. But when I sit down to figure out which donor to call, knowing that I'm 75% of the way, there isn't going to help me know which donor to call. So I'm going to need some stuff below that in terms of understanding that as well.
Before we go today, I would love to hear a little bit about how nonprofits can partner with each other with their data analytics. You mentioned this on your website. I thought it was such a cool idea. So how have you found collaboration between organizations to be helpful when they are sharing data and getting information on each other?
There's so much here. This is something that gets me really excited. There's a lot of different ways that collaboration can really help nonprofit. One of the first is, again, we have that sense of overwhelming. We have limited resources, and data analytics feels like something that's a nice to have. But how could I possibly invest in them? Well, if you can partner with similar organizations, what you can find is that the cost of analytics, when you split it ten ways, becomes very reasonable. So, for example, I work with a consortium of small critical access hospitals in a very rural part of the state. And what they've done is they've all come together. They've set aside a small amount, each of them that they've combined to allow them to invest in, having an analytics consultant support them and the things that they do. And so for any one of them, the full cost of that would have been probably prohibitive. But they can easily split it among themselves. And because so much of what they do overlaps, rather than duplicating those efforts, we just do it once and we share all of that information across the region.
So that's the first thing. It's just that resource sharing. Right. That again, if you want to hire an analyst, but you can't afford a fulltime analyst, hiring an analyst for a 10th part time is going to be very difficult. But you could have one analyst work full time across a group of organizations that all need very similar analytics. The next is pooling data. So not just pulling financial resources, but you can start to pool data. So I also supported a group of education nonprofits that support native students. And they said part of the problem is that native students oftentimes get asked very similar questions. Right. I'm going to survey students and see what they need. And then someone over here is going to survey students that they need. And you're forcing these students to have to supply this information to multiple sources. And I heard the same thing happen with genetic research. But if you have a very rare genetic disease, you'll end up with like ten different genetic research groups that are like, oh, could we have some blood samples? And they're like, well, I gave blood samples last week. Couldn't you borrow from them?
And they just sort of laugh and they're like, no, that was that group. Where is this group rather than pooling that information together? And so if you can get together with organizations, you may find again that you can split that data load, which reduces the resource cost, but also treats the subjects of your data, the people who actually supplying the information with respect because it's saying, we're only going to ask you this once, we're only going to ask for a blood sample once, so you can bring that together. The next is you can then also start to extend the data that you have. So if I have one group that I'm looking at and you have another group that you're looking at, but there's some differences. We can broaden our understanding in this space by bringing that data together as well. The next thing that you can do with collaboration is again looking at how you can all grow to be more effective with the resources that you have. So if my area is again, I do say medically tailored food deliveries and this group does food deliveries like for single moms, we can figure out how our data, we can use our data to understand well what parts of our program work for different populations.
So if I end up with someone who's got medical needs but is also a single mom, I might not necessarily have the information or the data to know the best program to serve her, whereas again, the other organization, if they end up with a single mom with medical needs, they wouldn't. But together we can bring that data together and really figure out how to tailor our programs effectively rather than just necessarily taking completely blind guesses. So those are all ways that those collaborations can really be helpful. And I realized I want to add one last thing. Many of us who do work with small populations, like I said, with native students or with people with particular medical needs, or like a strange genetic disorder, a rare genetic disorder. When you work with those small populations, data can be difficult because they're so small. We talk about the tyranny of the asterisk for small populations like native students, where they end up just getting left out of academic literature and the census. When you see studies about the census, you'll go to look up numbers and it'll just have a big asterisk saying not enough to report data, not significant.
But this is a really important population. Like, how can you just wipe them out? Because it's not big enough. But by again, bringing together nonprofit that work in some of these small spaces, you can start to build up a big enough group of data as well as a big enough presence that those data will stop being ignored. So that's the last piece as well, that there just is a value to aggregating and coming together and that strengthen numbers.
There totally is. Well, thank you, Alex, for joining me on the show. Can you let our users know how they can get in touch with you?
Absolutely. So I have a company called Merakinos that's Merakinos and I do all things data literacy, data analytics, data education, analytics, support for nonprofit to amplify your impact. And I also have my own podcast, which is sort of how we connected as well, called Heart, Soul and Data. So I try to just have approachable conversation about analytics on my website merakinos.com as Sabrina referenced. I do have a small blog. I try to put some ideas and thoughts up there, and then I also am on LinkedIn. I think I'm the only Alexandra Mannerings out there, so you can always find me there.
As Alex mentioned, the website and our podcast will be Linked for you. I highly recommend that you check it out. I found it really helpful as somebody who isn't as fluent in data as I would like to be. And if you'd like to learn how to organize your database more efficiently, we would love to get in contact with you again. You can give us a visit at trustworthy.com. Not only can we talk about data, but we also have a ton of resources available on our website that hopefully I will help you with your fundraising journey. As always, thank you so much for listening to the Fundraising Superheroes podcast and we hope to see you next time.