Alexandra Mannerings Explains How to Make Data-Driven Decisions

alexandra mannerings
Data-driven decision-making doesn’t mean you take the emotion out of your decision-making. It instead, challenges you to look at your decision and ask yourself if the numbers are reflecting your goals and values? 

As humans we are driven by emotion and what feels good, sound right and make sense to us. But sometimes these feelings can make us lose sight of what’s actually working and contributing to our progress. 

Alexandra Mannerings founded Merakenos to help nonprofits better understand their data and analytics to make more informed decisions. She discusses how to dive deep into the numbers while staying true to the heart and soul of your organization. 

Alexandra’s Top Tips 

  1. Use analytics across all departments, not just for fundraising. Your data holds great power and can help all aspects of your organization from your creative programs, to marketing and even HR! 
  2. Humans will always be biased, but we have to be aware of them. People need to practice looking at their data from an analytical standpoint. Try to be cognizant of your thoughts and giving yourself the training, and knowledge you need to understand the numbers. Ask to see the numbers and question what both proves and disproves your ideas. 
  3. Being data-driven does not mean replacing your values with numbers, but means measuring progress in an analytically robust way. It means trusting that the numbers don’t lie and using them as a tool for progress and responding accordingly. Don’t make decisions that feel good, make decisions that work towards your goal. 

Our Favourite Quotes

[07:54] - Data will also call you on your BS. Right. So we, as humans, have a huge tendency to see what we believe, right? Not believe what we see, but to see what we believe. And it's very difficult to shake us out of those preconceived notions, whether they're unconscious biases, whether they're the way that we think the world works, or how something is going to respond. 

[11:49] - When you get an idea that really sticks, someone goes, hang on. What data would have us change our mind about this? Because when you ask it the other way rather than what data supports this idea. If you ask what data would disprove this, you're going to go out of your way then to actually try to find that questioning data that may give you pause.


Let's discuss how to get the most from your data with Alexandra Mannerings.

Hi and welcome to Driven's Fundraising Superheroes Podcast. I'm your host, Subrina Sciscente. And as an innovator in nonprofit technology, our team at Driven is determined to help you unlock your true fundraising potentials. We specialize in donor, member and volunteer management, and you can give us a visit at TrustDriven.Com to learn more.

How can we use our data to make smarter, more value-based decisions? When some think of analytics, they believe it's black and white.

But when you're dealing with nonprofit data, it's much more than that. It's about using the numbers to help guide your mission forward. Alexandra Mannerings started Merakanos to advance how we use data with a special focus on those people and organizations new to analytics. Together, we can transform the world one data-inspired decision at a time. She joins us today to share you can approach data in a more effective and heartfelt way. So thank you, Alexandra, for joining me on the show today.

I'm so excited to be here.

Thank you for having me fantastic. So can you start by explaining the vision behind Merakenos and your journey leading up to it?

Absolutely. Well, my vision is a lot easier than the journey. My vision is to help nonprofits amplify their impact through the power of analytics. And I got here in a very, very winding way. My background is actually in life science, so I have an undergrad in biology. I have a PhD in epidemiology. I study diseases that spill over from fruit bats into humans for my PhD in SubSaharan Africa. And you kind of wonder how I got to hear. But what I learned out of that experience was how to ask the most impactful questions with limited resources.

And that ability has carried me through everything else I've done in my career, whether that's working with a state hospital Association where I ultimately was the director of analytics and figuring out how to take data that had been sitting on the shelf for 20 years and turn it into something that really served the hospitals that we all represented, and especially the rural hospitals who did not have their own analytics teams and how to make it more available for them to really access analytics that gave them actionable insight, not just pretty graphs, but something where they could look at it and go, Wait, I need to change this thing.

And it's showing me the best place that I could make those changes for the biggest impact. And so after working for the hospital Association, I went and joined a startup in the analytic space was helping everyone from small companies all the way up to top five accounting firms, learn different analytic software, and really enjoyed that experience. And realized after that I was really ready to try something on my own. And everyone sort of talks about that entrepreneurial leap. And I realized that I think I'd been building to it for at least five years that this was going to be the thing I wanted to do.

And when I took that steps, rather than really a leap and started my own place, I realized all these random skills were coming to bear, not just my ability to ask questions. As a scientist, I think of myself as a scientist who worked with data rather than a data scientist, but also my ability to talk to people. I love to talk. And that got put to use in my PhD and interviewing and all of that. But really getting to what's the business question people have, how are we going to help them?

Because if you don't understand where you're going, you're not going to know how to bring analytics to be on it. And so then I ended up with Merakinos is really an expression of everything I've been working on over the last ten years.

Yeah. That's so interesting that you come from a science background and now you're working with nonprofit. But I guess the motivation is still there, like you're trying to solve problems, you're trying to make the world better, and you really need to be analytical to do that.

Absolutely. And for me, the desire to serve just nonprofit comes from two places. One, having worked with huge companies, I get very set up with organizations that aren't willing to make a change. Right. I don't want to dump something into a black hole. And even if they're going to pay me tons of money for it and not see any impact out of it. And nonprofit are so committed to doing the right thing that if you give them a solution that's going to help them do the work that they do better, they're going to put into work, they're going to take that advice.

And that's the first thing. And the second is just that idea that I want to use data for good. And I want to make sure that this power, it with great power comes great responsibility. And data is great power. And I'd love to make sure that data are used for good, to make the world a better place rather than potentially just to make a company more money. Exactly.

Yeah. When working with nonprofit, what was the number one misconception? You notice that a lot of them have about their data and their analytics.

Do I have to pick just one?

Well, two, I'm going to fudge it. I'm going to give you two.


Yeah. The first is I'm too overwhelmed and too busy to try to figure out how to do analytics. Too busy, too overwhelmed. I don't have time. That's the first misconception. And I get it because we are all too busy and too overwhelmed. And you can do this in small steps, right. You don't have to go from zero to 100 mph in a day. You can find ways to take these little steps to bring analytics just a little bit closer to you to add just one little thing that you're tracking that's going to make a difference in one place, and then you add the next one when you're ready.

So that's the first one. The second one is data. And analytics are only for fundraising. So this I hear a lot. And this is partly because that's for some reason, everyone thinks that numbers go with finance and finance only. In one of my jobs as an analyst, I reported to the CFO, and I always questioned this cause. I was like, Why is the data part of the Finance Department? Data supports everything we do. It shouldn't be just part of the finance, but there's just this inseparable thing from money and numbers, and that's their forward data belongs.

So I'd love for us to think beyond that. How can we use it analytics, not just to make sure we're serving our donors properly and engaging them the most that we can, but also that our programs are the most effective that are hiring practices are the most effective, that our website is working the way that we want, that our social media is doing what we want, that changes any change that you make, that you measure that effectiveness, or that you research how you can do something in a more impactful way.

It doesn't just have to be funded.

Yeah, because the data doesn't lie. I spoke with Kenjie, the AMS guy, and he deals with Ms systems, and he talked about the morality of data and how men lie. Women lie. Data never lies. So if there is a problem that comes up with your numbers, then you have to solve the environment that causes that issue. So it really brings things to the surface, and it makes you have to confront them. And you're right. That doesn't just have to do with finances. If you're following is low.

Well, and what's up with your social media? You're not posting enough. Maybe you're not posting content that people see valuable. So I completely agree. There's so much you can discover from your numbers beyond just the finances and fundraising stats.

And you bring up a great point about the morality of data because data will also call you on your BS. Right. So we, as humans, have a huge tendency to see what we believe, right? Not believe what we see, but to see what we believe. And it's very difficult to shake us out of those preconceived notions, whether they're unconscious biases, whether they're the way that we think the world works, or how something is going to respond. And I brought up the hiring practices on because I think this is a really important one is you might think you're doing a really good job being diverse into hiring practices or getting the best kinds of people.

But unless you're actually looking at your numbers, at your data, you don't really know that. And it's very easy to trick yourself, and you compare yourself on the back and be like, I did a great job and miss a week. Every person that you just hired for your executive team came from the same background. You know, maybe they came from the same company that you came from, or maybe they came from the same educationa background you came from. Or maybe look like you right. But all of your directors and below you've done a decent job in reaching out.

And unless you have those numbers to challenge, we trick ourselves really easily.

Oh, totally. Yeah. You definitely see what you want to believe. And that's why it's so important. You have to have those hard facts in front of you and then mix them again with your values, which we'll talk about a little later. You spoke on this in your previous answer, but how do nonprofits shift that mindset to approach the numbers openly? Is it all about just trying to go in without bias? Is it like a scale that takes time that you have to practice?

Yes. Will never not be biased. Welcome to being human, which is again, why it's important to put numbers against that, because it's just how our brains work. And in fact, if we weren't biased, we wouldn't be as good as we are at being human. There's an upside and downside to being to thinking the way that we do. It helps us get stuff done. We operate much faster than computers and making decisions. But that's because we can shift through what is most important to us much faster. But it leads us to also make mistakes in other places to recognize that we're going to be human, and we're never going to change.

Even then, we shouldn't change is human. But you can put numbers in place like the guard rails on that. But to your point about shifting the mindset, it is practice. The term data literacy is very popular, and I'm a big proponent of what it represents. I'm not sure I'm a fan of the term data literacy because it implies somehow that we can become as fluent and literate with numbers as we are with work. And that's not true because we are pre-programmed for language. We come biologically primed for language.

We don't come prime to be statisticians, so it's always going to work. It is always going to take practice. It is always going to make a conscious effort to interpret a number that income comes in front of you or to understand analytics that comes in front of you. So I still catch myself sometimes making mistakes or taking shortcuts or misinterpreting something because I wasn't really on it then. And so to take that step of like, how do we bring analytics in? It's about making sure that you give yourself the education you need.

I don't expect that you can just logic your way through it, because statistics are a different scale. Training both in how to do analytics, but also how to understand analytics training for your whole company. It's also been a culture shift because it is easy to fall back on our old ways. So making sure that you put things in place that you are always asking to see the numbers on something or the data on something or the research on something, I like to say that one of the best things you can do is create a role that I call the Devil's analyst.

And it comes from this idea of the Devil's advocate, right? You should always have someone and it can rotate who that person is, who. When you get an idea that really sticks, someone goes, hang on. What data would have us change our mind about this? Because when you ask it the other way rather than what data supports this idea. If you ask what data would disprove this, you're going to go out of your way then to actually try to find that questioning data that may give you pause.

When we look for data that reinforces what we already think, it's very easy to ignore the stuff that disagrees with you. So just promoting that person who's like your job is to ask us what could disprove what we think right now can really cement that culture of bringing data to bear on all the work that you do.

That's so cool. And that goes back to exactly what you said before you see what you believe. So if you put that in your head, you're going to see it.

And I can share a link if you have show notes that you can share as a resource. Great exercise on this about: I'll give you three numbers and you try to tell me the pattern of these three numbers. And if you tell me the pattern, like three numbers back, I'll tell you if it fits my pattern, or I can say it doesn't fit. And you can use that as evidence. Right. To try to test your hypotheses about what you think the pattern is. And it's amazing to watch people when they pick a number set of numbers and it fits the pattern.

Then they'll say, oh, the pattern is numbers double. He goes, no. And then they'll look at them and they'll give them another three set of doubling numbers. And they'll say, yeah, that fits my pattern. Why is it not doubling? And they don't think will give something that doesn't double and see if that still fits the pattern. They keep trying to just reinforce this belief that they have rather than trying to disprove the belief that they have. And it's amazing just how strong this tendency is in us.

And watch the video because it does this amazing job of how everyone he interviews like five different people, and they all do it the same way of just continually trying to find things that reinforce this belief, even when he keeps telling them that's not the rule, but they are like this fits it. And there are other things that could fit it, too. But until you try to disprove it, you'll never make it to where you're trying a new goal.

Yeah. In your opinion, what is a data-driven mindset? We've talked a lot about how you approach data, but what does it truly mean to be data-driven in your decisions?

And I think that's a great question because I have fallen victim to what the problem I'm about to say is that it's easy to think that it means everything you do must be directed by data. And that's not actually what a data-driven mindset is. I like to think of it almost as like the fuel you put in your race car. So being data-driven, right? It's the fuel that's powering you forward, but it's still following your value. So to make this a little bit clearer, being data-driven means that you, as a human with a heart, are deciding what matters to you, whether as an organization, as a team, as an individual.

You're saying this is the direction I want to go. This is what I want to accomplish, whether it's reducing homelessness, whether it's ending hunger, whether it's being a better parent, whatever that end goal is, you decide that as a human and data cannot disprove or prove whether that's the direction you should go that comes from what matters to you. Being data-driven is then making a commitment to always using the analytics. Not this feels good to do, but really is the step I'm taking, getting me the direction I want to go.

So it means measuring progress towards that goal that you set in an analytically, robust way, so that when the numbers don't lie and when the numbers call you out and say that last step you took, it went backwards, you didn't actually get closer to your goal, that you then trust that those numbers have pointed out you've gone the wrong direction or you lost some ground, and you try something different. So being analytically driven, being data-driven and making data-driven decisions means that you rely on evidence showing that you're making progress or showing that you didn't make progress and responding accordingly.

I always say that I think we get ourselves in trouble with politicians making the actual policy to achieve a goal because they'll pick policy that feels good or that pleases their constituents or that is politically expedient. And it doesn't then achieve the goal that they started out. And that's an example of not being data-driven, right? Not picking a step based on what is evidence to be most effective and that we don't. Then when you put the policy in place, we don't put it in place with a measure to say, if this thing doesn't go up, if this number doesn't go down, then this policy isn't effective, and we should try something different.

It's like, well, this policy was a huge success because everyone liked it or because it got me elected again. Right? Or it just it feels good. And I think that last part is the hardest, right? It's hard to let go of things that just sound right. It makes sense to us that fit our worldview. But that's again, not being data-driven. That's being sort of, I don't want to say emotionally driven because emotions are really important. It's being misled by understanding what we're trying to do like it's losing the commitment to making the most progress. Being data-driven is committed to making the most progress possible.

You have to really use those analytics, like you said, as the fuel in the car. And it's hard when you have such strong feelings about one thing than another. But how far do you go until you decide that something is not working? What if you just did one test and the numbers are telling you, no, it's wrong. But if you would have waited another week, another month, it could have changed. How do you deal with that uncertainty?

I don't know. Meditation. It's actually not a great way to you're never going to get rid of uncertain. This is something I struggle with a lot because I want to know everything. I want to know all of the things, and that's what I'm going to make a decision when I know all of the things. And it turns out you cannot know all of the things. You really do have to be comfortable in a space of uncertainty. And I think sometimes we avoid analytics because analytics force us to admit how uncertain things are.

And it will always be that way. Things will always be uncertain. And there is not actually one right answer of how long do you give something before you say, well, it didn't work. This is going to have to be based on how many resources you have to put towards trying something, how fast you need to see results, and how many other options you have. Right. If you've got dozens of other options and you try one and it didn't kind of work out, maybe only give it one shot and try something else, because there's lots of different as you could get there, and there's probably gonna be more than one way that works.

Right? I was just talking to another gentleman who said, spend 80% of your time on things that you have found work, and then 20% of your time experimenting, and then every now and then, maybe every quarter, maybe every six months, re-evaluate that against you're doing and drop the lowest-performing of the 80% if you found something in the 20% that does better and move that into your and that you just sort of continually do these iterations. And so maybe to your point as well, like, when do you know to stop trying something?

Maybe the answer is when you find something better. Right. As long as you're committed to always spending this proportion of your effort or your resources or your time exploring potential options. And then when you found something better, you switch it out. Because typically also, it's not that it works or it doesn't. It might kind of work. So you got out of the options that you have, and then you keep iterating and trying other things on the side and then bring that in. When you find something that works better than what you were doing before, nothing's going to work perfectly, either.

We and nonprofits work with such complicated social issues that there's never going to be something that just 100 that follows everything.

No, never. That's a really cool concept. The 80-20 rule. I find that putting it in that kind of perspective and giving you that opportunity to try anything but still focus on things are working.

Yes. That's beautiful.

I love that.

That was from Boris Kiva.Org strategy. So full credit where credit is due. I did not come up with that.

Fantastic. I'll make sure to have that resource in the description box as well.


When you're going into your database, are there anything that you recommend? Nonprofit? Consider when making decisions. Are there key metrics? Every organization should look for maybe key areas that they should be tracking.

So there's rarely one actual literal metric where everyone should be tracking. I don't know donors who convert from one time to long-term donors or whatever that is. There's not really one thing that I say this is what you must track, but instead, there are types of metrics that everyone should be tracking. So the first thing you start with is what is your organization's purpose? Why do you exist? And again, whether that's to save pet's lives to fight a disease, whatever you determine, this is my reason for existence.

And you should have at least one metric that tracks that outcome and not the output, the outcome. Right. So are you making progress on reducing homelessness? Well, that means that you should be tracking the rate of homelessness in your area. And if you don't see it going down, right? Then your organization is not doing what it said it was going to do, and you need to be able to do that. And I think oftentimes we think about this only from the point of view of what I'm going to tell my donors.

But if you're committed to being a data-driven organization and outcome-oriented organization, you need to be measuring that for yourself. Right. If you're not doing the thing you said you were going to do, what are you doing with your resources? And that's a very uncomfortable question to have to face. And I get that. And I am not saying that question facetiously. That's a deep, hard question. So that would be the first thing that every organization should be tracking. Right. Are we moving towards the outcome we said we were going to?

Then the next thing you should measure is the major tactics that your organization does to achieve that outcome. So if going back to the homeless example, you should be tracking the level of homelessness in your area, and you can decide how you're going to measure that. Maybe it's spent sleeping on the street. Maybe it's a number of individuals without at least a long-term temporary residence. You get to determine the best way based on the thing that you're doing to capture that outcome, and then you go to the next step for those tactics.

So if the way that you're doing it is building the tiny house village, maybe that's one of the tactics that you're doing. Well, then you should compare every dollar spent on tiny houses. What sort of proportion of people are then given long-term houses, or how much does that reduce the rate? You should be measuring the effectiveness of that tactic. Or if it's job training, you should be tracking for everyone who goes to the job training, what proportion of them then end up in long term housing, like with a stable housing situation.

So then most of us use more than one tactic, and that goes back to our 820. Right. Then you can look at your tactics and compare them against your outcome and say, are our tactics moving us there and the ones that are underperforming? Maybe you move away from those tactics, and that's a hard decision to make. Maybe this is something your organization is done for 40 years, and you have to look at it and go, Wait, it's not doing what we want or the situation changes.

And maybe it worked 40 years ago, but now we're in a new world and it's no longer effective. But if you're not tracking the effectiveness of that tactic, you won't know that. And you might keep doing it even though it's not moving you towards your final, final goal. 

That's a really good answer, because normally when I ask these questions, I'm always looking for that one number and there is no one number that's going to fit every radiation is different.

And I think about it when I was talking to my accountant about what gets to be considered business expenses. Right. And I wanted him to tell me these things count as business expenses. And he said, well, the IRS defines it as things that are usual and necessary to accomplish your business. And the reason they're so seemingly vague is what would be usual and necessary to run a circus will be very different from what's usual and necessary to run a school. And as soon as you said that, yes, that makes so much sense.

Right. Like, I'm not going to be buying lion food for the school, but that would be a required expense for the circus. And that's the same thing with these metrics. Right. The category is the same we need to feed the school. We need to feed the line. But the way you're going to do that is going to be entirely driven by what your organization exists to do.

That's a great example. Before we head off today, do you have any final tips on creating and executing a data strategy? It's a big question.

I think it goes back to that here- I'm going to step it back, and I'm going to make it a little bit simpler. Start with looking at the things you're already trying. Most of us are still tracking something, right? We tend not to just completely fly blind. We're counting something. And so look at the things that you're already measuring and see if they are outcomes or if they are output. And what you'll probably find is most, if not all, our outputs, because outputs are easier to count.

And by outputs, I mean, the things that we do that generate some sort of tangible thing. So we oftentimes will measure how many dollars did we give out or how many meals did we send, or how many lunch kits did we create? Those are all outputs, but they're easy to count because I can go 1-2-3-4-5-6 lunches. Right. Reducing hunger is a lot harder to measure to give people that one actionable stuff that they can take. Now, look at the things that you're measuring, determine how many are outcomes versus outputs.

And if you find that nearly all of the things you track or output challenge you to come up with one outcome measure that you could track. See if you can either feed one of those output measures into an outcome measure or maybe create a new outcomes measure and put that next to all of your other output and start tracking it as well. And I think what that does for us is it starts to shift that mindset. It starts to shift out of just counting donation dollars and starting to really measure the impact that we have.

And that goes back to my goal of why I exist and why I have a business. I want to amplify your impact. And what gets measured matters, right? What gets measured improved. So if we shift from measuring outputs to measuring outcomes, our outcomes are going to get better, our outcomes will get bigger and our impact will be amplified.

That's fantastic. Well, that is the perfect place. I need to end our conversation. Can you let our listeners know how they can get in touch with you?

Absolutely. So you can check out my organization at Moroccans com. That's M-E-R-A-K-I-N-O-S com. And I'm on LinkedIn. Alexandra Mannerings. I don't think there's another Alexandra Mannerings out there. So if you look me up on LinkedIn, I'd love to connect with you or chat with you or ask any questions that you have. I'm happy to answer them.

Fantastic. Well, thank you again, Alexandra, for joining us. To understand more about Merakinos and Alexandria and everything that she does. You can visit her Merakino.Com. That's M-E-R-A-K-I-N-O-S com. And if you want to learn more about ALS A Driven, maybe even sign up for our newsletter, you can give us a visit at trustdriven.Com there. We have all of our resources, past podcast episodes. And like I said, there is a form for you to sign up for a newsletter where you will get our content delivered straight to your inbox is not fantastic, so convenient.

Thank you so much for listening. And we'll see you next time on the fundraising superheroes podcast.

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