AI, fundraising, and you with Nejeed Kassam



Also listen at: iTunesGoogle MusicStitcher


AI is one of those buzzwords that has recently taken over people's minds. We imagine AI as a futuristic, thrilling, and scary opportunity. But we don't always recognize how it affects us or our work. The cool thing is that AI is being brought into our sector and has a lot of potentials to help organizations leverage their data to do more focused work or analyze and find new opportunities.

In today’s podcast, our guest, Nejeed Kassam, Lawyer, CEO, and Founder of Keela, an impact technology company, talk about AI and how it helps small nonprofits manage their donors, mobilize resources, and raise more money.

Myths that Nejeed wants us to walk away from:

  • AI will replace your job as a fundraiser. Nobody's coming for your jobs. You can't automate fundraising. That's not realistic. What you can do is allow folks to be less burnt out, allow them to prioritize more effectively, allow them to see patterns, and focus their work in different ways. Effective use of artificial intelligence in fundraising is going to happen when it's deployed appropriately and then the fundraisers can take that knowledge and make decisions, steward better, and build stronger relationships.

  • Spending time on data has no benefits for your organization. Quality data has many benefits for organizations. Aside from compliance, data helps organizations to prepare for donor meetings, and then to use it for reporting and analytics. Nejeed advises organizations to spend time, be disciplined on their data, and do things right when setting up to save time going forward. Data can be a really foundational pillar for institutional capacity.

Nejeed’s thoughts around AI and Fundraising

  • AI predictions help drive decision-making - Using patterns from data that you have collected will help drive your decision-making. It gives you a good probability of the giving behaviors of the donors from your database. Data can tell your organization a story about your donors that is not recognizable when we don’t see the big data picture. It can also help you identify ask levels or make decisions about where you spend your time and energy.

  • Forecasting helps fundraisers. Understanding forecasting can help you understand your organization’s programming realities. It can understand whether you’re on track for where you want to be. You can make decisions when you have an idea of where you're going. It also helps identify when to ask for support, how much to ask for, and more.

  • Benchmarking for fundraising. Data helps your organization to measure efficacy and focus on thinking about how you are doing relative to your goals. Being able to check yourself, being able to hold yourself as an organization and as a fundraiser accountable is really valuable because then you can lean on all these data points in these predictive analytics and know where you really need to dig in and not.

Favorite Quotes from Today’s Episode

Post your favorite quote on social media to share with us!

“No, you can't automate fundraising. That's not realistic. What you can do is allow folks to be less burnt out, allow them to prioritize more effectively, allow them to see patterns, and, um, focus their work in different ways because of the AI ultimately the effective use of artificial intelligence and fundraising is going to be because it’s deployed appropriately and then the fundraisers can take that knowledge and make decisions and steward better and build stronger relationships.”


“But, you know, that's what I'm saying. It's predicting the future, but it's not, it's guiding behavior. If I can, instead of cold calling 900 people on my list who I don't know maybe interested, if instead, the data is signaling something is possible or likely, or has a high probability of happening that helps me do my job better.”



Resources from this Episode


Nonprofit Software | Keela

KIT: AI-Powered Fundraising (fundraisingkit.com)

The Good Partnership


Transcript:


Cindy W.: AI is one of those buzzwords that I feel like has really taken over a lot of mind space for people of late. We think about artificial intelligence as this futuristic and exciting and scary time or opportunity, but a lot of times we don't really see how it impacts us or our work and, the cool thing is there's actually a lot of opportunity or things that we, ways, I guess that AI is being brought into our sector and has a lot of opportunities to help organizations let's say leverage their data to do more focused work or understand and find opportunities that we might not have seen before. And really, if you think about AI or how this conversation is really talking about a way to filter or funnel your data to generate insights that will help you choose action or choose what action is most appropriate. So there's some really cool stuff in this conversation, all about AI and fundraising in the nonprofit sector. I'm really excited to dig in.


My name is Cindy Wagman, and I'm your host of The Small Nonprofit podcast where we bring you practical and down-to-earth advice on how to get stuff done in your small organization. We know that you are going to change the world, and we're here to help.


Today's guest is Nejeed Kassam. He is the CEO and founder of Keela, an impact technology company, dedicated to empowering nonprofits with accessible software. He's also the founder of Fundraising Kit, K I T, the world's leading AI-powered, predictive analytics tool built exclusively for fundraisers and nonprofits. He is a lawyer by training and also the founder of public policy organizations, Better Canada Initiative and Believe Vancouver, definitely very multi-passionate. He has done a lot of amazing work in the sector. He's also the writer author of the book High on Life with the cool foreword written by former Canadian prime minister, Jean Chretien and co-producer of the documentary Conversations for Change. There is a lot more, I could say about Nejeed but this conversation really speaks for itself. I think there's a lot of interesting things we can start to look at within our sector on using AI. So without further ado please join me in welcoming Nejeed to the podcast. Nejeed, welcome to the podcast.


Nejeed K.: Thanks so much for having me.


Cindy W.: I'm so excited about this conversation. I feel like it's very forward-thinking. Or maybe it's catching up.


Nejeed K.: Maybe but I'm not throwing shade anywhere, but I think at the office, my nickname is old man, and I'm like a curmudgeon. So to be talking about forward-thinking things is ironic and also you don't validate.


Cindy W.: Yeah. I think we could agree that as a sector. We're not quite the most digitally forward. And today we're going to be


Nejeed K.: How about we have a lot of opportunities.


Cindy W.: We have lots of opportunities to leverage what other people have paved the way for us to do. And today, specifically, we're going to be talking about AI. And I think most people like to have an inkling of an idea of what is AI? We think of it as, Amazon recommendations, Netflix recommendations.


Nejeed K.: Which is pretty good. That's a pretty good understanding of what it is. Yeah.


Cindy W.: Okay. Maybe I have, I don't, I think most people have that understanding and how it is emerging in our sector and how we can start to think about leveraging AI for fundraising. So let's start with what is AI because just because I can throw out Netflix and Amazon doesn't mean I have, and certainly, it doesn't mean I listen to it have a good understanding of what it is. So let's start there.


Nejeed K.: So I think what's really interesting about that question is sometimes we forget that the fluency that folks like yourself may have and something like AI isn't necessarily common knowledge. Even the term, AI is an acronym, which not everyone may know. AI stands for artificial intelligence and it's not like the matrix is taking over. So folks don't have to worry, but simply put it's the intelligence or predictive capabilities demonstrated by computers, by machines and unlike natural intelligence, which is displayed by humans and animals, which kind of has consciousness and emotional, it's an evolving decision-making framework, so to speak, that's built on more and more data. So think of it as like a technology that can take all these data points, processes, and ultimately make suggestions or recommendations and it's not going to take over the world. It's not going to be, it's not super scary. It's also not, it is based on what data, right?


It's not like you just say, here's data, go make decisions. There are software engineers and folks, and that, that go in and say, these are the inputs we think are useful. And then they train it on, previous sets of data to check how effective it is. And I think what's really exciting about AI as it evolves, it gets better. So it becomes more accurate. It learns how to weigh things differently. And I'm not an engineer, I'm a lawyer. Let's be clear, but that's the way that I understand it.


Cindy W.: Yeah. So if we use the Netflix example, it's not like the machine is watching all the films and analyzing them for what you might like next. It's saying the people who liked this film, like the more data we collect of like the people who have the same viewing patterns, they then go on to watch these things are those. So I'll put those in front of you.


Nejeed K.: And I think the term machine learning is like one that's also thrown around and that's like where it takes data and it makes recommendations or predictions and from those and the accuracy of those, it learns, okay, this is not as, as accurate. Cindy didn't watch Breaking Bad as suggested or she did, and she binge-watched it. I'm not going to judge on that one. What I am going to know is, over time those recommendations continue to get better. And a lot of it is around pattern recognition. A lot is around like you said before, somebody similar watched something. So maybe, it's making predictions on that. It's not like it's following your eyes and your giggle every time you laugh at a show, it's not Terminator. Although I'd love to have sitting in my computer and be like you should watch this.


Cindy W.: Oh my God fun fact. My husband thinks my son looks like Sean Connor from The Terminator, he keeps bringing photos.


Nejeed K.: I want to meet your son now? I'll be honest with you or maybe your husband. I can't tell ya.

What's exciting about AI that we kinda miss is it is just a tool, ultimately we can use it to help us automate mundane tasks, identify things that no human can possibly see support fundraisers, which is what today is really about in doing their job better. And I want to be clear right at the top of this post.


Nobody's coming for your jobs. You cannot hop, but so many folks like my God, we're going to fire the hole. No, you can't automate fundraising, that's not realistic. What you can do is allow folks to be less burnt out, allow them to prioritize more effectively, allow them to see patterns and focus their work in different ways because of the AI ultimately the effective use of artificial intelligence and fundraising is going to be because it's deployed appropriately and then the fundraisers can take that knowledge and make decisions and steward better and build stronger relationships. I think that is really, people, are scared of that, it's just not that scary.


Yeah, it's, that's such an, I hadn't even thought of that. But I totally can appreciate that people that it does feel like, we've, there's increasing dialogue around, the machines are coming for all of our jobs.

I'm happy to retire. I'll go golfing like that if they can cover my job that'd be great.


Cindy W.: Yeah. Well, I've heard, interestingly, I've heard that in the context of talking about universal income, but regardless of the other thing I hear, which I think is super relevant, a lot of smaller organizations feel overwhelmed by the idea of having a lot of donors because it's more information. They can't manage those relationships.


Nejeed K.: And spreadsheets you're right. By the way. And that's the thing like if you're on spreadsheets if you're not using your CRM effectively, if you are, ultimately, if it's just piles and piles, like my desk of paper that you need to know, oh, I talked to Cindy on Thursday, I took notes. I didn't record them, but there's a sticky somewhere. Yeah. The more data you have, the more stressful it's going to be. Yeah, I think, and this is I love this analogy because somebody told it to me really early in my career because I'm not a technologist. There are two fountain pens sitting on my desk.


Like I'm old, one of them was made in 1960. Okay. Like I am, I like it is pretty cool, but. We're not trying to build a faster horse, we're trying to build a car. And by saying, by, if you try to build a faster horse, you're in trouble, you are in trouble, you're going to be, it's scary. It's uncontrolled, it's out of hand, but if you're trying to do something, that's going to actually become more efficient and actually help the smaller works, I believe. It's actually the little orgs, the sub 5 million orgs that are going to benefit the most from AI because they're going to see opportunities that they're missing and ultimately be able to leverage potential gifts much better.


And I think. It goes to unwillingness or a fear of the unknown. For and you said it at the top of the podcast, you said sometimes we've been a little laggard. And I think that's because we operate from a place of fear. And I think that as we transition generations as gen X, gen, Y gen Z, kids become more and more engaged in our sector, they're going to move forward, whether the rest of us like it or not. And I think that changes the train coming. We can either get out of the way or get on it this is my assumption.


Cindy W.: Yeah. And, and hopefully, we can see how it can help us do better. So let's, I want to talk about that, the use case scenario. The other thing that I want to talk about is data in or data out is only as good as data in.


Nejeed K.: Yes. Ma'am


Cindy W.: Pick your order. Is it helpful to understand, how the data is used, how the data is being used before we understand that we have to have clean data or vice versa?


Nejeed K.: I think it's like asking, which came first, the chicken or the egg. It doesn't really matter. They're both important. So let's talk about data in and for, for a quick second, I'm going to go back and lean on my legal background.


The CRA requires those of us that are Canadian registered charities to have good data. And because it affects our ability to receipt, it affects, if we ever get audited, it protects the responsibility that we're given to steward being a charity, ultimately. And that's a designation that it's hard to get and it's easy to lose to be really honest.


Now you don't have good data and you're in trouble. You need to know who your donors are when they did, whether they were receded, where they issued receipts were issued. We all know all these things in Canada, in the US it's not that different, right? If someone makes a gift, you still have to receive that. You have to track all of that. I think what gets dangerous is when we think of data as only for those purposes, and if we take the approach that we don't have time to clean up our data, to record it in our CRM, to have that discipline, it's not make work.


And I think the, sometimes the assumption, especially from young technologists, like me who are like, oh, he's just, that's what he does. No. It's because it has so many benefits from compliance to ease of use, to prep, preparing for donor meetings, of course, and then to using it for reporting and analytics. I think that we. You're absolutely right. That it's only as good as the data comes in, but it's not that hard to have good data. You're not doing any algorithms. You're not doing math or pulling out your Abacus. You're simply just having discipline. And I think that's a, sometimes that's a scary thing, but really even a spreadsheet can have great data quality. And as long as you're disciplined about it, does that answer your question? Kind of

Cindy W.: And to bring that back to the AI piece, like you're only going to get. Good predicts or like a good record.


Nejeed K.: Forget about AI. I'm going to report it.


Cindy W.: Oh yeah.


Nejeed K.: AI is a little scary maybe possibly. Reporting is required. Your board needs to know how many donors do you had last year, how many repeat donors, how many people are recurring givers, what was your donor lapse rate? You can't do that unless you are disciplined. And I think, forget about AI, which we'll talk about it. Yeah. So to me, it's not like a no-brainer like, I don't want to be, I don't want to be pejorative here. I just think it's. It's important. Yeah, it's important. And sometimes, and it's, and the misconception is, oh, it's taking too much time. It's not going to pay dividends. The dividends are going to undoubtedly come from putting that time in. And it's a fraction of what it is relative to the return on that investment


Cindy W.: and it's foundational to you getting the kinds of insights that we're going to talk about, you cannot generate any meaningful insights or look at opportunities unless you understand and have good data. Yeah. Yeah. All right.


Nejeed K.: And I would tell everyone, use your donor management systems. It doesn't matter which one you have. We always used to do. Th the best CRM is the one you use doesn't matter the brand. It doesn't, of course, it matters, but it doesn't matter, ultimately, because if you are in love with it, if you are disciplined in it, the dividends are going to pay off so much.


Cindy W.: Yeah. Yeah. And increasingly like your donors are often the ones entering their data into your system now, right? Like with online giving and all that, as long as you have your form set up properly and you're using campaigns and things like all the, if you're setting it up, a lot of the work is actually done by your donors.


Nejeed K.: And again, it goes back to spend the time. And when spend the time, it does not take a week and do it. It's like just when you're setting things up, do it right. Because it's going to save you hundreds of hours going forward.


Cindy W.: Exactly. Exactly. So back to


Nejeed K.: Says a guy, with about 50 books on his desk, by the way, I want to call my own BS.


Cindy W.: We all, I still use, actually I recently started not still. I started using a paper planner every day and I still use my computer as the main one, but every day I still need to look at things written by hand, but I don't need to carry for anyways. Yeah. I should, as a combination,


Nejeed K.: I agree with you that just don't make it your donor data.


Cindy W.: No, it's not going to serve you. Then we get to talk about institutional knowledge, which feels like a whole other conversation, but the more you put those things in your system, the better that institutional knowledge you build that.


Nejeed K.: And I'm just going to pick up on that for one second because it's interesting a lot of the folks that we, you work with and we work with are like the smaller organizations. And sometimes those are led by these bad-ass women who have been doing it for 25 years and they just know. The problem is at some point they're going to leave. And if you don't want the organization, part, data can be a really foundational pillar for that institutional capacity. And. Instead of leaving a gigantic hole, which is make work by the way for the next executive director, and she's probably a young millennial who's going to spend a ton of time, she's 37 or 42 or whatever it might be. And she's going to be like, spend so much time doing this garbage work that should have been done throughout the time. And she's going to lose a ton of that flavor, the personal, all that capacity.


Cindy W.: Yeah, exactly.


Nejeed K.: And we haven't even started talking about predictions.


Cindy W.: So let's talk about it because I'm so excited. And I think this is something that has so much potential, especially for smaller organizations that are overwhelmed by data. Especially when it's done, in an accessible way. So what are, let's talk about how fundraising can leverage AI and some of the things that you are seeing, that you're leading, or others that I know you have some really good examples. So how come, how can we. I'm going to preface this by saying not all tools will do these things. But it's, I think aspirational and certainly, some tools will do them. So let's get aspirational. What are the ways that you would like to see organizations leverage? Yeah,


Nejeed K.: I got to go back to Netflix or Amazon. God, Amazon knows more about me than I know about myself. Let's be honest. And I hate that. All they're doing is they're using patterns from data that they've collected to help drive your decision-making. Yeah. And are they influencing it? Yes. If you buy a baseball bat, it's probably going to suggest the ball because millions of data points say the people who buy the bats are probably going to buy the balls. Now it might seem so obvious to us, but you do that at scale. You can't do that. There's something there, and so to me, fundraising is a lot about patterns. It's about relationships and it's about tax and the intersection of those two things, let's use a really simple example. Every single person who gives, especially like what I'm going to call like non-one-off, not one time, but one-off givers who just give to a peer to peer campaign, and that's like my uncles, sisters, aunt, asked me for 50 bucks. I did it, right. If I actually have any relationship with the organization, if I'm on their mailing list, if I'm clicking links, if I'm reading something, if I've made donations over a few years, whatever it might be, that is all data in input that is sitting there that no one's ever going to go and dig for.


It's not like I'm going to make up a timeline by hand on a piece of paper like Cindy did this and this and this and this. It's not realistic to do per 500 or a thousand or 10,000 donors. But that data tells a story. It's in the sales world, it's called purchase intent. It's called engagement intent in the donor world. It's called stewardship, and, donor funnel, ultimately, that's what we're talking about here. The secrets that we can take from all that is, you can tell Cindy is probably going to give after she did X, maybe it's opened an email, a newsletter, e-blast 15 times over the course of six months, or maybe it's every November because that's her jam, she's a giving Tuesday guilty giver and they can see that multiple years of patterns.


And there's so let's get into some of those examples. So let's start with my favorite one donor readiness. To me, this is the coolest and most efficient prediction. And, we are really lucky to have worked on this one quite significantly. When somebody is ready to give the data, will tell you. That's super. It's not like I have a premonition. The sun is there and I put my finger in the air and that wind blew. I'm like, oh he's going to give. No, there are signals. There's that purchase intent. AI can help you say there's a pretty good probability that Cindy is going to give in the next two weeks.


Now. That's really cool because you're not just going to sit there, twiddling your hair. You're going to be like, I should probably call Cindy and be like, yo, Cindy, give me that money. Probably not like that though. Definitely not at all. Boards, I sat on had listened to my fundraising calls. They all fired, but no.

But, that's what I'm saying. It's predicting the future, but it's not, it's guiding behavior. That's my contact about before, if I can then instead of cold calling 900 people on my list who I don't know, maybe interested if the data is signaling something is possible or likely, or has a high probability of happening that helps me do my job better, so that's a really great example.


Let's use another one. How much money should I ask Cindy? If you got a new book out, customers, this girl's got money to donate. And so you look at giving patterns, you look at demographic data, you can look at things like job titles. There are so many things and you may be wrong, but you might be right. And if the data is going to tell again a story and it's not, you still have to think. It's if the algo says, ask Sidney for a million bucks, I'm like she serves nonprofits, probably a not right. But it's going to say if, if you ask somebody for the smaller amount, you're often missing an opportunity. So you should at least see what the data is, cause they'll probably give you that small because that's what you asked for. But if the data is saying they could have the potential to give a little more than you might've assumed, because of demographics because of this, because of again, that's where machine learning comes in.

There are millions and millions of data points that help inform these algorithms. You might do better when you ask. So not only do you know that Cindy is likely to make a donation in the next couple of weeks, you've got a decent range of what you should ask her for. Okay, great. What's the next question?


How should I talk? No. How do I, what's the best way to reach out to somebody? Again, if you've been disciplined with your data, if you've recorded interactions and engagement with that person, if there is heavy news out of her reader and they've emailed you before, or you know that there's a ton of phone calls and your previous storage.


Okay, data will tell you something. It'll look at age, it'll look at, social media engagement and look and be like, probably be like, yo, Cindy's kind of girl, that's going to respond to an email better than a phone call. So I should hit her up that way because I'm more likely to elicit a positive response, which is more likely to increase the gift, which is more likely to do better for the organization. So I've just used three examples of where predictive analytics can guide fundraiser's behavior. It's not telling you what to do. It's simply guiding your decision-making. Does that kind of answer your question?


Cindy W.: That's super helpful. So I have a question about data points. So obviously, and I do want to, you might be able to answer this specifically the tools that you've developed or your team has developed, but obviously, we know that data into the system that we're responsible for that our donors input themselves or that our interaction with the system did they open an email? Did they click et cetera? Do you pull another data? I think a lot of organizations feel like, oh, we don't have data. We don't have a lot of information.


Nejeed K.: Firstly before. So I'll answer that question. But first I'm going to say you actually do. I know because every, if you use MailChimp or, whatever, every time Cindy clicks on an email, that's a data point that MailChimp is recording. Whether you want them to or not, it's there. You can get access to that data. Every time a donation is made. Every time a piece of mail is sent out. You prop every car. If you have 10,000 contacts in your base, you probably have a million data points.


Cindy W.: Yeah. Wow.


Nejeed K.: If you add them all up it's the campaign they gave two. It's the issue that they care about. It's a time of day. They made a donation. It's you don't think about those as individual data points. Forget about aggregated data. Forget about census data. Forget about all wealth screening data, just in your database. There's you are rich with data. You just don't know it. And so the assumption that we don't have data is generally wrong.


If your organization is 15 minutes old, and you've just got, your charitable status, you probably don't have much data. And we're, But you begin building that, but if you've been around for a few years, you've got data.


Cindy W.: Awesome.


Nejeed K.: It's just like the assumption is everyone has dance moves. You just don't always know it. Everyone has data.


Cindy W.: You've not met my husband. I would not qualify him as having dance moves, but yeah, no, one knows. It's okay. He knows I'm not shy about sharing that information.


Nejeed K.: So then the second question is like, how do you bring in more data?


Cindy W.: Do we need to? And if so,


Nejeed K.: Every data scientist on my team says always, yes. You need to know but if you can, you should. How about that? And again, it goes back to the number of tools that you use every day that are going to give you data. When somebody, if you're using a ticketing tool, there is data there. If you're using an email marketing tool like MailChimp there's data there, if you have a wealth screening tool, like windfall or wealth engine or donor search or whatever, it might be, there's data there, at if somebody has social media, connect them, put their account there, cause you can do the social following.


And if they tag your organization or they retweet something, you've done that's data. The more of that you bring into the treasure trove, the richer, you will be from a data perspective. And do you need to? No. But should you, because it's a missed opportunity if you don't and great pieces of technology are going to help or mostly do it for you, to be honest, you do it, they don't, it'll spit these kinds of things out.


Cindy W.: Cool. We've covered a lot. I don't even know what time it is. I'm running out of time. Are there any other uses before we get into where our listeners? Okay. Yeah.


Nejeed K.: So two, one is forecasting. I think this is so actually I want to go back, but before I talk about forecasting, I think one of the biggest missed opportunities as fundraisers is moving folks from one time to recurring donors. To me, this is. Golden. This is the Willy Wonka egg, the golden egg of predictive analytics. That's how old I am. All the young people who miss a name, like who the hell is Willy Wonka?


Cindy W.: There's new Willy Wonka. Isn't there? Johnny Depp do Willy Wonka. Anyways, we're talking about Gene Wilder.


Nejeed K.: Yeah, exactly. Gene Wilder like way back, okay. Anyway, so there is a Gene Wilder of metrics. We're going to go with that. And that's, to me the one-time to recurring, I think because for so many org and every thought leader in space talks about predictable revenue for fundraising organizations. And there is a study, I think, next after did it that found that your donor lifetime value not inconsequential multiple more, if you can get somebody even on a 10 or $20 a month, recurring donation. So to me, my favorite one is that metric. How do I, how, who is likely to become a recurring donor who's made a one-time gift or either a few one-time gifts or, whatever it might be. To me, that's the mother of all ones. And the reason I brought that up is that that goes to the other use cases.

There are two things I want to talk about one that's directly related to AI. And one that looks at the efficacy of all of us, that we should be thinking about it in the context of data.


So to me, forecasting is the coolest one sales organizations have been doing forever. How much money am I going to sell this year? If I'm a tire salesman, I might, I probably have that based on, and there's probably AI in there saying, oh, winter's coming. You're probably going to sell more. And there are all these kinds of data points. If tire salesmen are using it, we should be using it. And there's nothing against tire salesmen. And by the way, they take all my money all the time, every four years new tires. I think to understand that because that can help us understand our programming realities. It can understand whether we're on track for where we want to be. It can, there's so much there. And so artificial intelligence can help you that it can help for any, pardon me?


Cindy W.: I would say, as long as you make the ask, I have, or I've worked with organizations where they'll just stop doing their year-end campaign. And they wonder where that why there's no money coming in


Nejeed K.: Make the ask people. And that goes back. And that goes back to what we were talking about. AI isn't going to solve that for you. You still have more coffee, that's going to solve the problem for you, and I think, again, it goes back to a tool, right? So if you can say but the really exciting part about forecasting is like, we are not on track this year. We better ask a lot, right? We need to push harder. We need to, reallocate some of our resources. We need to get the ED and the board members dialing. We need to maybe run a campaign. We need to put out a new peer-to-peer. You can make decisions when you have an idea of where you're going, and so that's a really cool and very innovative way to use artificial intelligence in fundraising.


The other way isn't really about AI, but it's about measuring your efficacy and that's, I really am a big fan of benchmarking. So a lot of folks in the sector, and it's not related directly to AI, but I bring it in because I really have this mission to get folks focused on thinking about how am I doing relative to my goals relative to our sector, whether it's by cause areas like your MTE code, org size, or your geographic location.


You know if 15, I know these are made-up numbers, but if the benchmark for percentage of recurring gifts to one-time gifts is X 18%. If I'm at four, what, exactly I should call Cindy? If I'm at 40, I should keep Cindy away because I'm doing better than the benchmark. But knowing being able to check yourself, being able to hold yourself as an organization and as a fundraiser accountable is really valuable because then you can lean on all these data points in these predictive analytics and know where you really need to dig in and not.


And so it's almost both the pre and the post, right? It's what's going to inform how and what you use and also how you're going to measure your efficacy gains. Does that make any sense?


Cindy W.: That makes total sense.


Nejeed K.: I'm a big benchmark guy. I love benchmarking


Cindy W.: Yeah. I love that. All right. Clearly, not everyone is doing this work. You are where can our listeners learn more about you? And you have. Keela which we're very familiar with. Cause we use it with a lot of our clients, but you also have KIT and that integrates with a lot of other CRM. So tell us a little bit about if this has piqued your curiosity. Yeah.


Nejeed K.: I'm a crazy person, so I've decided that instead of being stressed just about one tool I decided I should have two because having an 18-month-old baby isn't enough. Here's what I would say. Let's talk about the little folks first. I'm really blessed to work at a company, a social business, a B Corp that is really dedicated to empowering small nonprofits and at the heart, our CRM pieces, and Keela is an incredible CRM this data and analytics, it's got the power of predictive analytics in it. It uses incredible technology it licenses technology from KIT which I'll talk about in a second to power small non-profits. So if you're small and you're excited about this and you want to work into it every day, and you're looking for a CRM, I wouldn't be doing my job if I wasn't saying come check out Keela talk to Cindy about Keela.


But, now, as I went down this journey of the last few years, when you started using artificial intelligence and thinking about that for years at Keela, I realized that there is a huge market hole. There is whether you're small or, big, this is something that almost no organization is adopting and really every organization should.


So I decided to be crazy and start I think a new adventure and that's we built this thing called the fundraising kit. So if you go to fundraisingkit.com, it can talk to you all about some of the things that we've talked about, this some great reading and resources, just to learn about how we can use it and fundraising kit or KIT, we call it. It's like a toolkit. That's why it's called that. It's a tool. It's an AI or a predictive analytics toolkit for nonprofits. It integrates into incredible tools like Salesforce and Razor's edge and neon and wealth engine. And I and Windfall, Donor search and Mailchimp. We really are like our repository in a space where we want to make data the fluency of the sector up and be that driving force.


Really privileged to be able to have incredible teams working on both these projects. And ultimately my goal is to make every nonprofit empowered by artificial intelligence in a not scary, not damaging, exciting way if that makes any sense. Yeah.


Cindy W.: Yeah. I love it. Thank you so much for being part of the podcast.


Nejeed K.: I will shout out to your team as well because they are awesome and we love working with them. They are amazing and we look good. I'm not that smart for someone is a group of amazing women and men are much more intelligent than I have. So I'm very grateful for that.


Cindy W.: They're awesome. And so are you, this was a great conversation.


Nejeed K.: Thank you. Thank you. Thank you to our listeners. And yeah, this is so exciting. So I hope that And the folks I know it's going to be ready, make that coffee, do your thing, but we always get through it as a sector every year and go ask for money.


Cindy W.: All right. See you all next week. Folks, that's it for today's episode of The Small Nonprofit, I'm your host, Cindy Wagman. And this show is brought to you by The Good Partnership. As a reminder, if you want more resources around raising more money for your small nonprofit, visit The Good Partnership.com and download our free fundraising strategy guide. I'll see you next week.


Listen to The Small Nonprofit podcast on your favourite platform: