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Nonprofit Radio for October 6, 2025: Your AI Brand Footprint

 

George Weiner: Your AI Brand Footprint

What is this thing, why should you care and what can you do to improve it? George Weiner returns to acquaint you with his company’s study of how Artificial Intelligence will influence giving in Q4. Then he explains the implications of the research, including that last year’s content strategy is obsolete. He also brings tactics for you and your content to get the recognition you deserve from Google Gemini, ChatGPT and their colleagues. George is Chief Whaler at Whole Whale.

 

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Welcome to Tony Martignetti Nonprofit Radio, big nonprofit ideas for the other 95%. I’m your aptly named host, and I’m the podfather of your favorite hebdominal podcast. Oh, I’m glad you’re with us. I’d suffer the embarrassment of Onicotrophia if you nailed me with the idea that you missed this week’s show. Here’s our associate producer Kate, to give you the highlights. Hey Tony, here’s what’s up. Your AI brand footprint. What is this thing? Why should you care? And what can you do to improve it? George Weiner returns to acquaint you with his company’s study of how artificial intelligence will influence giving in Q4. Then, he explains the implications of the research, including that last year’s content strategy is obsolete. He also brings tactics for you and your content to get the recognition you deserve from Google Gemini, Chat GBT and their colleagues. George is chief whaler at Whole Whale. On Tony’s take 2. Hails from the gym If she can do it. Here is your AI brand footprint. It’s a pleasure to welcome back George Weiner. In 2010, he founded Whole Whale, a top 100 nonprofit focused digital agency supporting analytics, advertising, AI capacity, and digital fundraising. George is chief whaler. He’s also the co-founder of Power Poetry, the largest teen poetry platform in the US, a safe, creative, free home to over 1 million poets, and CTOs for good. A group of tech leaders at nonprofits that delivers social impact primarily through technology and digital strategy. You’ll find whole whale at wholewhale.com. You’ll find George on LinkedIn, where he is very active. Welcome back to nonprofit Radio, George Weiner. I’m, uh, I feel like we haven’t learned a lesson. You keep having me on. I’m honored every time I get the invite. I was like, wow, I didn’t mess this up. Thank you. Yes, no, you, you’re, you’re, you’ve earned a repeat, repeat appearances, absolutely. Um, you know, I, so I was happy to, uh, read the bio that you provided, but I don’t, I don’t think it captures. I don’t, I don’t, it’s not the bio that I would write if I were you, because, you know, you have this enormous tech background that you do go on, the bio does go on, which I did not mention that you were chief technology officer, I believe it was for 7 years at dosomething.org, which is enormous, enormous, turned into the enormous data capturing. And uh uh assistance for activating young folks, that’s enormous, but I would, so that they’re not that that that is to be minimized, but I still don’t feel like this all captures. I mean, you’re, you’re the, you’re a tech guy who understands it, explains it, uh, simply talk about like, I mean, you, you get in the weeds of tech. I mean, you’re like a coder. You write, you write lines of code. I do, I have, I’ve come in and out of it, interestingly, I used to be very actually like in the technical writing code and then I hired people smarter than me to write much better code and then I came in and out a lot of data, analytics, advertising. I love learning, I love understanding and then. Helping nonprofits find the angle, right? Like how do we leverage this thing? I’ve I’ve studied the whole book, just read page 17 and do it this way is what I love kind of getting at and recently I’m very heavy into coding again, but frankly with AI assistance um and and building up calls writer.AI, which is a great customized platform for nonprofits creating, um, creating AI generated content and I do it in as safe a way as possible. Exactly. Uh, yeah, so that’s kind of what I wanted to, uh, I’m glad that you, you mentioned. I wanted to get to a little bit, Cowriter.AI, a proprietary. Whole well created. Is that right? right? Yes, I, yeah, that, that guy, that’s the coding that I was referring to. Maybe you hired smarter coders to do better at it than than your initial cuts, but, uh, which. Nonprofits can use and train. Cautiously on their own, have the, have the model trained cautiously using their own content. Exactly, yeah, for you, not on you in the sense that we build it so that there is a central source of truth that is stored and protected for you that can then be pointed at any model and then custom built prompts and guidance based on what your team needs to do today, and we delete every chat every quarter and pay for carbon offsets on all of the queries uh that are conducted to try to at least approach carbon neutral as. Uh, difficult as that calculation is. Well, I admire the attempt. Um, you’re also a whole well certified B Corp, so you’re, you’re, you’re committed not only to the environment, but also to the, uh, to the causes of, of, of social impact. We’re trying. I think I’m really excited though about the upside of AI while still like having my little Tony voice in the side of my ear being like, Well, but what about, what about like this, it’s gonna steal the content here, it’s gonna cause this, and I feel like this um AI study that caught your eye sort of like really walks down this tiny tightrope of I’m excited, but also I, there is caution to be had for some of the insights we found. OK, we’ll, we’ll get to some of those, but I’m glad that Tony voice, um, accompanies, I’m not gonna say haunts you, accompanies you. The Tony voice accompanies you because I do have my concerns which I’ve, I’ve shared with Beth Kantor and Amy Sample Ward and then you, um, so listeners are acquainted with my. Uh, skepticism, concerns about the, uh, the, the widespread adoption of artificial intelligence and, uh, large language models, so. I’m glad my voice is accompanying you. Yeah, and I like, I like the, the, it’s like respectful challenging too when you come at it. I think there’s some folks that shut down arguments and be like, you’re wrong, I’m right, you’re like, you have this very like clever way of getting in my head on, you know, the edge cases that come up and I’m like, damn it, how am I gonna answer this for uh for for this type of argument and it’s important. OK, thank you. I’m glad. right. Um, the AI brand footprint. Uh, you study? Because uh you’re concerned that uh AI is gonna have an enormous influence over giving decisions in the 4th quarter that we are now in, right? AI. AI’s influence on giving. See, my questions are so you can’t even, where’s the question? The guy talked so long. I, I, where’s the kernel of the question? Yeah, you’re concerned about the, the artificial intelligence’s influence on our millions of giving decisions this, this, these 3 months. It is going to be an unprecedented influence for AI on philanthropy, and that is maybe a hyperbolic way, an excitable way of saying that AI continues to grow in its answering of questions we have. And as that happens, one of the questions that comes up in Q4 is what are the best animal nonprofits I should give to? What are the best ways to support mental health and youth for charities I should donate to? Where should I give for the most effective cancer fundraising efficacy fill in the blank of your cause? Those questions are going to be answered. In the millions of numbers controlling billions of dollars. I thought it was worthwhile to take a quick look at. The data behind that. OK, so you looked at 6 different Large language models. I’ll let you name them. I have them listed here. Oh you want me to name them? No, no, no, I can go through it. It’s like a lot of technical, um, but I know there are some, some geeks among us and so here was our methodology. Um, we looked at 12 different cause areas and then for each of those areas, we chose 10 sort of iterations on the types of questions. A potential donor might make alongside those. OK, the next step is where should we test this? Should you just choose one model in the corner of the room? No, anything worth doing is worth overdoing, as I like to say. So we looked at 6 major models that kind of comprise the landscape as we see it, Gemini, OpenAI, and Tropic, meta Grok just in case, and um, you know. From there and also sorry, perplexity and from there we then literally send requests to all of them multiple times, get their responses back, and then analyze them. There’s a number of reasons for doing this, but on one level, we also have to understand this is a probability. It isn’t like Google search rank where I can definitively say you’re #3, you’re number 1 like. That’s not how this works. It’s like rolling dice each time, but when you roll dice enough times, you get that little nice little bump, the Gaussian distribution of a lot of things here and then out toward the sides less and less. I wanted to understand a bit more about the distribution of nonprofits being recommended in each of those areas across those models. How many flags of jargon am I gonna get thrown at me right now? No, that’s OK. No, you’re all right. Um, I do want to clarify, Gemini is the Google, uh, the Google product, uh, just to make that clear for folks, uh, and Anthropic, uh, is named Claude. I don’t know if folks might know that it’s Claude or Anthropic. OK, the other ones I think people are familiar with X is Grok, etc. um. OK, no, no, you’re OK, jargon jail. I’m, I’m listening. Uh, I’m, I just don’t wanna go to jail too. I’ll let you know. I’ll let you know. All right, so that’s a lot of questions because you 12 cause areas times 10 questions, different iterations of questions times 6. So 12 times 10 times 6 isn’t that something like 720 or so? Different over 700, but more importantly, like we’re talking about millions of words being analyzed by the end of the day because each of those prompts comes back with a bunch of texts that we then have to parse. Uh, and then from there what we did is we have to figure out like, all right, you know, what is the there there in this prompt and we began to go by counting the number of mentions of unique nonprofits as well as sources of influence. What does that mean? It means a lot because who did the AI point to as, who according to whom is this charity? Verified quality of donation. This is how we get to our first action step because there are some very clear, clearly prominently mentioned influencers that because see this is the value, uh, it’s more than, you know what, we’re not even gonna talk about who was the top named mental health cha or or the most common named animal welfare charity. We’re not gonna do that because I don’t think that’s where the value lies. The value lies in recognizing. Now see, I’m trainable, George. I’m trainable. Uh 63, but I’m still trainable. The, I think the value in this study is, is the, the threshold recognition that artificial intelligence is Capturing you, it cares about your work and, and what does, how does it learn about your work? What does it learn about your work? What does it say about your work when people ask either specifically about your charity or generally in your cause area or any other, any other reason you might show up in a, in a, in a search result? AI cares about. AI is paying attention to, and I don’t know, cares about is maybe a little overstatement, but AI is paying attention to you, to your work. OK, so let’s go to our, uh, our, our first, our first real, real takeaway, the influencers. I’ll let you name name the most common influencers that nonprofits have got to be, if they wanna be, if they wanna be thought of well by by these large language models, they need to be. Uh, um, approved by or have high standing with, who were these influencers? Yeah, I, I think this is an interesting way to answer this question is actually to give you a section of one of the responses from one of the prompts from one of the models, and I just want you to consider the implications and the prompt I gave this model, and this was a Gemini model was where to give for animal organizations like it’s kind of disfluent and sort of how I put it together, but where do I give is the central question I asked. And there’s one prompt that came back. It said before donating, always do a little research. Charity watchdog sites, these sites evaluate charities and efficiency and program spending. Charity Navigator, GuideStar, now Canada and BBB Wise Giving. Check their websites for their mission statements and how they use donations, local reputations, and finally, financial efficiency. A good rule of thumb is at least 70% to 80% of their budget should should go directly to program, not administrative costs or fundraising. You’re right. That’s instructive. All right, so that, that’s a pretty, that’s a, that’s quite a good answer. I like that answer. Yeah, I think unpacking it sometimes though is that what you’re hearing are, it is not going to primary source, it is going to evaluation platforms, your charity navigator, your guide star, your BBBY Giving Alliance, those public profiles matter more than ever. They mattered before, but they matter in your mind. You’re like, oh, you know, like a donor’s gonna like do some research and go check it out there, like, no, no, no, no, no, this is being baked in. Up front, before someone even finds you, they’re finding what other people think of you on these sites and others. It’s different. Huge takeaway, huge takeaway. um Give Well was another one that was named another influencer, yeah, no, but you were, you were quoting from 11 prompt out of that one response out of 1700. So, um, OK, Charity Navigator, uh, guide star, which is now Candid, Better Business Bureau, Wise Giving Alliance, Give well. Take away number one, you, you’ve, you’ve got to be thought of well, you’ve got to be well ranked. Not, not quite for the reasons. I mean, the, so, I mean, George, the reasons that we thought they were important still exist. There are still people who, uh, my dad before he died used to get the Better Business Bureau, Wise Giving Alliance printed guide. And he would check, check to see if uh a charity that sent him mail was listed in the printed guide. So I’m not sure people are using the printed guide too often anymore, but They, they are looking, the people who do go to your site are looking for those little, those little badges, the, uh, the, the high ranking badge, the platinum for Charity Navigator, etc. But now, even more so. The large language models are using the influencers, like you said, George, the 2nd order. Recommendation or, or, or evaluation sites, not recommendation, evaluation sites. In their, in their, in their responses. Yeah, and those types of giving guides, third party validation, there’s a lot to unpack there, but it’s not just, hey, let’s update our website, it’s go there. Another sort of nuance here were um mentions of GoFundMe. I was kind of curious when I was looking through the data of like, alright, how much of this is gonna be like. Find individuals and go that route and what we found was based on the mentions of GoFundMe and the request of like how do I help let’s say um youth mental health or poverty issues, right? Where should I go, how should I help? Like it’s an open ended question on purpose. We, we did want organizations we wanted what is it and how is it advising and for GoFundMe mentions actually, uh, 70% of those mentions came from Grok. So in the land of Grok, which is Twitter, which is X, which is Elon Musk, just to get all of the bingo cards there. It is disproportionately recommending to go to GoFundMe. It also recommended charities, mind you, but I found that interesting. Part of that is in the training set and part of that is in the sort of, you know, we do our own homework but to a bizarre degree in there. So, so Grok was looking at the, the frequency of GoFundMe campaigns for charities as a way of determining whether they were a good place to give. They first off, are meant we are meant, we are analyzing its responses across all of those causes, right? Every single cause, every single chance. So across all of those tested areas, we were seeing that it just surfaced that the user should go find and look. For someone in that cause area to donate to on GoFundMe as a point of helping that particular cause. Why that happens, you can speculate, but actually, you know, it’s training data set. There’s a lot of people that go on to X saying, hey, I need money for this, go, you know, fund me and GoFundMe, and that type of link and that type of cause might be overweighted, plus potentially an underlying, um, frankly baked in mistrust of institutional organizations. That’s a bias. Grok Grok had a, had a bias for GoFundMe campaigns. They all have a bias. Right, let’s talk about some of the biases, yeah, because, because this gets to what The large language models think of your nonprofit in the, in, in some of these biases, and then, of course, we’re gonna talk about what to do, how can you Enhance your AI brand footprint. In, in light of what we’re learning from the survey. So let’s talk about some of these biases like size, the, the, the ones that I, the ones that I saw, the, the, the results, these were. I don’t know, maybe not. 100%, but these were very large charities. They had large digital footprints. Yeah, in in most of the cases where we then do a sort of top 10 breakdown, we have a like a herding effect where it is the the larger, more well-knowns, uh, are at the top, you know, you look at the environment we end up with, uh, just using that as one example, the environmental mentions by model like on Gemini, the top two are environmental defense fund and the Nature Conservancy. And then on Anthropic, which is Claude, World Wildlife Fund comes up first, and the Nature Conservancy, Nature Conservancy wins on Open AI uh on Grok, World Wildlife Fund wins on Perplexity, the Nature Conservancy wins. So you see, it’s like the sort of jockeying, but those are massive organizations and you have to go down pretty far to you get to something like an Earth justice or Oceana or um. I’m trying to find like Arbor Bay Foundation, which is just not small, uh, defenders of wildlife, you know, coming in at the tail. Those aren’t the household names. Nature Nature Conservancy, World Wildlife, or well, yeah, um. All right, what other, so size, so, so our listeners here are in small and mid-size nonprofits. They are likely not in any of the environmental causes you just named specifically, and they are very likely not in any of the biggest names in any of the 12 cause areas that that you evaluated, but. I hasten to add there are things you can do. We’re gonna get there we’re gonna get there it’s coming. The fear not, as Grandpa Martin Eti used to say. Nunjawari In his, in his New Jersey Italian accent, Nunjawari, there are things you can do. It’s time for Tony’s take too. Thank you, Kate. I have a new tales from the gym. There’s a woman who’s been coming to the last. 3 classes, uh, that I take every Tuesday morning. It’s the only class I take each week, just, I just go to this one class. And she’s, uh, she’s quite active, she does, it’s an aerobics class, she’s does all the weights, she does most of the moves, you know, like we’re stepping back and forth or side to side, things like that. She’s, um, she moves pretty well. I, I, I have my eyes on her because uh she stands right in front of me all three times. She’s been right in front of me in class. And the thing is, you know, she, she’s got all this activity for the hour long class. But she’s on supplemental oxygen. She’s got a tank down next to her and the tube and the cannula in her nose, the whole class. So there’s some things she can’t do like she can’t step too far forward or back or side to side because the tube isn’t that long, but she moves enough and she keeps up. You know, like, yeah, I can’t help but see her. You know that just makes me think, I mean, if the woman with supplemental oxygen. Stays energetic through this hour-long aerobics class. Anybody, almost anybody could be working out. There, of course, there are people that are. More compromised than just supplemental oxygen, but. You know, anybody who’s not on, on oxygen, uh, we all could be working out to some degree if if this woman can do it. So she’s, she’s, um, pretty amazing, pretty, uh, uplifting and encouraging. That’s another tale from the gym. That stories take two. Kate I don’t know if you follow like gym, TikTok or anything on like Facebook or Instagram, but now I’m seeing a lot of fitness instructors coming up with different um workouts that are seated for people with maybe mobility issues or any sitting down um disabilities. Um, but that’s great that she can get up and do it and enjoy it and still be active with something that’s probably heavy, you know, to carry around. Yeah, well, the tank is, but it lays on the floor. Yeah, I, I haven’t seen any of that on TikTok or Facebook or online, but I, I have seen chair yoga. Which is, that’s for older folks who do have mobility issues. Balance, you know, balance could be an issue. Uh, there is chair yoga out there. We might, I don’t know if we do a chair yoga class in my little beach town, Emerald Isle, but I, I’ve seen chair yoga. But yeah, the woman is, um, is greatly uplifting. Yeah, she’s uh. She’s moving. It’s awesome. We’ve got Boku butt loads more time. Here’s the rest of your AI brand footprint with George Weiner. Other biases, what, what other biases did you find in our uh Our large language model friends. Our large language model friends, this kind of surprised me. I don’t know if you can classify it as a bias, but it kind of getting back to the herding effect, but in the number of charities mentioned, like the unique number of charities mentioned really surprised me between the cause areas that we go between. So something like the environment or poverty, there was a range like unique nonprofits mentioned across all of our sampling of 127. OK, I then go to something like cancer. And there’s a 30 organization spread that is massive. I was shocked. I was like, shouldn’t they all just probabilistically find a large number of things? There’s extreme herding in certain cause areas and action step that you might use here is actually do this type of research for your own backyard. Abstract, pull back and say what kind of question might somebody who’s caring about my cause area and my locality put in to find the most effective, best run, greatest places to give for X, and see who shows up, but remember you want to do this on a like incognito, not influenced by your own GPT if you are paying for it, I’ve trained it, you want to actually have it from a cold start. That isn’t biased by your bias and your information because then you’ll be like, obviously you’re the best mirror mirror on the wall, like, hold on. So does that mean you shouldn’t even do it from your your nonprofit, your your office browser? Is anything that’s passing in information to it, like geographic is fine, but I would say any other things that are passing information are essentially tainting it and tilting it toward something that’s more relevant to you when in fact, You want something that is better reflective of the underlying biases and approaches of that model to sort of explore it. We actually use direct to the API calls so we know exactly what information we gave it and exactly what we got back and could kind of like clean out the clutter, so to speak, of any customization going on or cache information or browser um influencer. Here’s a tip. Actually, no, here’s a very, very hard tip, and you’re like, I don’t know where to start. I want you to go to the site open router.AI. And that will let you just mess with whatever model you want and see what happens with you getting full control over the data that are being sent. Router. What does this do? Just look for router. Yeah, yeah, yeah. OK. All right, so that’s a, are you saying that’s like a safe place that that’s it’s less likely to be tainted. It will let you test different models side by side in a way that is safe in this land and gives you control over more of the variables and it’s really like kind of elegant for side by side comparisons. OK, OK, very good. All right. Um, All right, so Let’s move to, um, you know, what, what small and mid-size shops can do. Now, part of what they can do is what you are doing at whole well with AI brand footprint. Which I am helping you to do. I am propagating this for you. So explain what I, go ahead, you flesh out what I just said that I know you’re doing that we are helping you with. Uh, by the way, thank you for reinforcing AI brand footprint. Why we keep using this term is because it is a concept that we created at Wholeal to explain what’s going on when you’re discussing this ecosystem of information that AI is talking about and representing your brand on. You’re not getting a lot of data about it. But you know it’s happening. You’re starting to hear maybe somebody in development department be like, you know, they heard about us from, uh, they said from chat and it’s like, oh, that’s interesting. There’s a whole ecosystem out there and we’re just sort of, uh, scratching the surface of it with this study, but you should begin to care about what that footprint looks like, what’s influencing it, how big it is, if it is growing, or if it is shrinking. Why I am focusing on and why I love the fact that we’ve mentioned it like 17 times now is because we are trying to put this concept out there, imbue it with meaning and connect it to us so that when, not if, and it already happened. That Google overviews, that’s the little AI answering when we do Google searches, talks about this concept. It attributes it to us, it surrounds it with our lengths and our language rather than literally stealing it and just throwing it into the soup that is the overall. Uh, you know, word salad of the AI systems. So to pull back, how do you imbue with meaning, own concepts and encourage attribution? There are some number of tactics, but that is the game we are currently playing and laughing about as our inside joke here. Exactly. So we’re not gonna leave. Nonprofit radio listeners, you know, wondering what are some of these tactics? How can you Create something around your work that’s unique, will be attributed to you when, as you said, when not if the, the, the large language models find it. How do you How do you identify all this, bring this all back to you and your site, your work? Because you’re doing this is exactly what whole whale is doing with AI brand footprint. There I said it again for you. It’s gonna be in the, it’s gonna be in the transcript. It’s gonna probably be in the show note. This what, what, what tactics can we all learn from what you’re doing at whole well? So the content that we used to write for AI engine, for for SEO search engine optimization is dying. The idea that all I have to do is answer a question accurately and I’ll get credit is dead, because also if you realize that the AI could answer the same FAQ question, generic, hey, how many. Uh, how many ounces are in this amount of thing like that information has been commoditized and will no longer bring you traffic. Your 10 facts about this issue is not going to work anymore. What will work are first party data. What kind of information can you bring to bear on this topic? Have you surveyed your audience? Another way to think about it is according to whom? Is there a testimonial, a statement that can be attributed to the CEO, to the founder, to the stakeholder who received the service, because when AI comes and summarizes and takes that content, it actually is sensitive to uh trademarks, first party data attributes. and saying, oh, according to the local animal shelter or youth center leader that this is the thing you’re like, oh, that’s tied and anchored now to something that the AI will respect. I think there’s a lot to unpack in there, but hopefully you begin to see the nuance. This is all about optimizing content. For the, the AI tools, right? You’re, you’re, is that, or this is a subset of optimization. It’s a way to make sure AI respects the source attributable to the content it has scraped and taken from the site. More and more we’re going to see increased traffic from these AI bots that are coming to, you know, answer somebody else’s question and maybe they show where that information came from and maybe they didn’t. And in doing that, these are the initial phase of tactics when you create your content and also, frankly, it’s putting The human back in the content, like the stuff I think we may look back at writing of uh the how to like tie your shoe content like because it gets traffic was not that relevant to our organization like the, you know, 15 cutest cats to promote our thing really was only getting people on a very high level to maybe browse through our site. So this is hopefully return to real content. OK, but that had value. Um, the, you know, the 15 cutest cats. All right, well, well, let me take a look at this shelter. Maybe this is a, if it’s, if it’s a local place, maybe, uh, maybe I’m looking to adopt, maybe I’m looking for a place to volunteer or obviously maybe give, so that, I mean, as, as a, as the beginning of a pipeline. That had value. It probably still does have, it does still have value, but what, you know, what you and I are focused on is the, the AI. Evaluation of your work. And from that perspective, the 15 cutest cats. Not valuable. It won’t drive attention. I mean, we are still humans here. This is where Tony voice is like on your shoulder. No joke, like cats. OK, OK. However, the fact, the nuance, the difference here is that you won’t get the attention you used to for that article. It used to drive attention. Without attention, you’re not gonna get the 1% of those people sticking around and giving you their email, of which 1 out of 10 makes a donation. That flow of traffic has been severed and is in the process of being severed, and you can see this action step. Look at your organic traffic year over year. It is at best flat if not going down. Even the smartest folks playing the game are publicly saying we’re in trouble when it comes to organic traffic. So that game is like, we’re on the decline. Uh, to come back, does that make sense? Yeah, yeah, it does. Um, that is, that’s what I’m talking about, it’s a source of organic traffic. But I’ll play the cute cat game if you want. So let’s say I’m sitting there and I’ve got like a a cat shelter, like, OK, uh, how could I turn this into something quotable, something from, uh, AI won’t steal or if they do, they’ll attribute it to me. Well, maybe back to QAs, I could actually do a study of over the past quarter actually. 70% of the cutest cats, based on my cuteness score versus ugly, cute or ugly, were adopted, yet 90% of ugly cats don’t get adopted. And suddenly I’ve got first party data that you collected, you surveyed, you did it is wildly interesting because I’m like, what’s an ugly cat, right? And now you’re playing the game because when AI comes it’s like according to the Long Island local cat shelter, ugly cats don’t get adopted at a rate of 90%. That’s right, that’s a very great. OK, OK, that’s an excellent example. of, of this, the tactic that we’re talking about. Please, if you go out there and do an ugly cat survey on a rate of adoption, please send it to me cause I think I’d be curious. What’s an ugly cat is the greatest question. You could you could use AI to make the determination so that way it would save you from the whole like internet fallout like according to this AI and how cute is this cat from 1 to 10? You can blame it on AI. OK, you, you mentioned some other things, uh, testimonials, quotes from the CEO, you know, all again, all it’s first party attributable to you, so, so then in and I’m gonna use Google too because that’s the, the primary search engine that people use. So in that Google summary. Powered by Gemini, right? This is all Gemini results that we get when we see the Google doesn’t say AI summary or something like AI overview AI mode. They’ll change it next week, so fine. It’s Gemini. It’s, it’s Gemini, correct. That’s the underlying model you’re correct. So we, we want to get attribution. I mean we wanted to say from whole whale from the Long Island cat shelter. Correct. You wanna be the authority that pops up on the side so you do in fact get a potential click and brand impression associated with the topic of someone saying like uh why do ugly cats not get adopted? Where do we put these testimonials in quotes? They’re basically embedded on your site, right, writing your content, right in line, um, you can use quote tags, you can use uh what’s called schema markup to make sure that when the AI is reading it. Uh, it is more quickly attributable, and in those testimonials, like you can just also just make up quotes like I now have to come up with some sort of clever thing to say, and I think for the AI study, I literally put in there I was like I have to come up with something. So I was like, alright, this season, AI will determine giving more than any other in history, like it’s a little. Over the top and like a pretty safe statement but I said it, I quoted it, I shoved it in with a quote tag inside of there and now that’s one of those attributing factors. There’s another big thing I wanna touch on, but I wanna make sure that makes sense. Uh, it does, but all right, so remember your big thing, but you are in jargon jail for scheme, you are in jargon jail for schema markup. I’m gonna bend up there somehow. Uh, the schema markup is, uh, little HTML tags in there, so behind the scenes, like if I want to make something bold, I put a little B tag on it. Every P tag has that little spacing. H one tags make it at the top header. This is just another one of those types of tags in the system that the machines read and understand more quickly what’s about to come and what lives between the open and close of that tag. OK, very good. All right, you’re out of jargon jail and listeners, the, the person who does your website will know exactly what we’re talking about. This is HTML. AI will know what you’re talking about if you literally, this is an amazing thing. Like your expert is the say, hey, teach me what I need to know about Schema. What, what would I need for schema markup of this page? You can ask AI and it is you’re ready to go developer on that front. OK. The This is the thing that these are the things that concern me. This is where we get into creativity conversations. What was your other big point? The other big point is something that has dawned on me and it kind of, uh, it sucks, technical term, because I’ve realized a lot of my writing is being. Commoditized and is not going to be found by people, it’s going to be summarized. And what I realized is where do we go to be uniquely human, where do we go for trusted information and more and more it’s going to be, I think in audio and video, video that is harder to fake, not impossible, but much more unique, a little bit more messy and so for your tentpole content, your main focused content. You have to have a video associated with it for a number of reasons, including the fact that YouTube is the #2 search engine, including the fact that people are now going to say like, I get that all this text I’m looking at is all like AI driven. I want to hear a human say it cause at least that human had to read it before saying it and letting those words out of their mouths. And third, because it is also showing up very, very clearly in those AI overviews right below, they’re giving video answers to the textual answer that AI gives and then they are having the good old fashioned links which are going to go the way of the yellow pages, I think. So we’re talking about side by side content. Part 111 part is for the humans and the other is for, is for the AI models on our site, you’re talking about videos, videos for the humans, mostly, although AI I understand the, the AI summaries are they’re, they’re finding the video and, and promoting those for you if they’re on point. But then there’s the also, but there’s the, there’s the AI. Needed content like side by side. We’re not talking about having to, I mean. Yeah, I, I, I, isn’t that what we’re talking about? 22 different levels of content, one for, one for the machine and one for the, for the humans who do come to your site. I’m beginning to believe that we’re probably headed toward the, the text of your site is more of like a database for AI to reference and so what is it that you’re going to create that is uniquely human communicating in that way is going to be video. For now, uh, because that, you know, is, it is also getting the lift on these other platforms. It is showing your brand, your people, and your message in your words as opposed to AI summarization of its spit back out in text, sometimes it’s attributed to you, sometimes it’s not. There’s no way to sort of like just sort of like stealing video the same way you do text. So I’m really Emphasizing and have been for a while the library of content that rides alongside my written content, which you know I try a little hard out on but I, you know, make no illusion that AI isn’t literally copying it and shoving it into its system and answering the questions that used to drive attention for us. So how do I play this game? I look at the data, I look at, right, it seems that it’s still on YouTube searching. I can embed it on my page so I increase engagement on my page when people do in fact go there, which is a positive signal in the land of the Google and old SEO and current. So it is a way to think about when we’re talking about your AI brand footprint, it’s a sort of adjacent because of the way Google is showing it and the way that everyone else also copies Google, so like in perplexity, which is a. Kind of Google competitor, I think for AI based um discovery of information and searching. Um, they are all sort of playing this game of amending and appending the relevant videos they find to that topic. OK, this is um. Uh, to me, this is as revolutionary as when back when we were saying you need to have a website. Yeah, this is a big phase shift. This is the yellow pages to website shift. I I don’t make that out of just sort of a throwaway statement. It is a big change and we’re in the middle of it. And so that is also one of those reasons I try to uh rant as much as possible, but wake people up to the type of content you’re about to create. You got a content calendar. Alright, we’re gonna map out 2026 and here are the like. 36 articles we’re gonna write, I’m like, take a beat because I think if you continue to write the way you used to and create content the way you used to, you are um. You’re creating another listing on the Yellow Pages. I don’t want folks wasting their time in that way. And also this should not be a surprise, but if you are using AI to write all of your content, why do you think AI is going to surface what it already wrote or what it can already answer? On your website, it is, you know, you don’t have to think that hard to be like, oh wait a minute, the hundreds of millions of people, like 10% of the adult human population on this current planet we are on uses AI to answer these questions like they’re, you know, you’re disintermediating yourself, you’re removing yourself when you simply press copy and paste from AI and by the way, people can tell when you’ve written it in a lazy way. Oh, that’s huge. That see you I wanted to, I, I. You were, you were given like a perfect summary. I thought, oh, this is a good place to end. I’ll just say that’s George Weiner, Keith Whaler and I’m out. Then you opened up, yeah, but then you opened up and, and by the way, um, you’re, you know, people can tell, yes, yes, uh, we’re still human here. And I don’t know. I, I’m not, I’m not saying that I’ve spotted every bit of artificially and artificially developed content that’s ever come across my screens. I’m not saying that, but. There is a feel of fakeness. To artificially generated. Paragraphs Yeah, it, you know what’s interesting? The human’s ability to do pattern recognition is tremendous. It’s, it’s unbelievable what we’re able to tune our attention to and what our brain simply learns behind the scenes, like we can now smell an AI generated image of it. We can smell the AI generated text, maybe it’s the vivacity, maybe it’s the uh unusually accurate cadence of number of words per sentence. There is something that you pick up and we are all collectively building. This ability to see it and maybe I’ll take a step back if you don’t believe me in our amazing pattern recognition. I want you to think in your mind of a stock photo of the following successful business person shaking hands and you’re like, it’s like you could smell it. It’s just the way it’s image focused cropped and it’s just generic humans doing business thing. The same way you can spot stock, we can now and are building that muscle, that pattern recognition for AI slop, work slop, whatever category of a bunch of AI generated texts, which is why it’s also fun talking about this new phase of like putting the human back in, how do you do that with video? How do you do that with testimonials, how do you do that with actual first party data? Like, I used to waste tons of time writing like very, very long articles and thinking and researching. But like Google searching and pulling back information, instead, I threw all that away. I spent all of my time doing this AI study to be like, I, I get to like geek out on this topic, go way deep. And then put that out instead, so like you’re just channeling your energy of creation in a different vector. I think your uh ability to spot stock photos analogy is is is spot on too. All right. That’s I think that’s very valuable, George. Well, we were here to make sure your audience didn’t walk away with panic. I feel like a lot of conversations go to this like hand wringing, what do we do next? So you have some takeaways. You got a lot of tactics. Uh, let’s let’s give one more shout out AI. That’s the research that’s the proprietary first person data that you’ll find at wholewhale.com. Go, just give him a break. Give him, give him some organic traffic. Just go to wholewhale.com. Don’t, don’t not click through from anywhere, just, just type in Wholewhale.com and go. Do it for George. Let’s see, let’s see if he gets a burst in, uh, in, in the month of October. And you’ll find George at uh on LinkedIn. He and I are very active there together, uh, just, you know, valuable content, George, then valuable, valuable ideas here. Thanks very much for sharing all this. Uh, thanks for giving it a larger audience. Next week, HR for non-HR professionals. OK, that, that, uh, that was supposed to have been this week, but George Weiner came in and he’s related to 4th quarter. So that’s why I squeezed him in because it’s the first show of the 4th quarter. So that’s the explanation, uh, ordinarily I would blame the associate producer, but this time, just this time, uh, it was, it was my own, my own. Uh, I would say rather savvy decision, but it was my decision, however you might characterize the decision, I made it. The savvy decision. If you missed any part of this week’s show, I beseech you. Find it at Tony Martignetti.com. Our creative producer is Claire Meyerhoff. I’m your associate producer Kate Martignetti. The show social media is by Susan Chavez. Mark Silverman is our web guy, and this music is by Scott Stein. Thank you for that affirmation, Scotty. Be with us next week for nonprofit Radio, big nonprofit ideas for the other 95%. Go out and be great.

Nonprofit Radio for June 5, 2023: Artificial Intelligence For Nonprofits

 

Afua Bruce, Allison Fine, Beth Kanter & George WeinerArtificial Intelligence For Nonprofits

We take a break from our #23NTC coverage, as an esteemed, tech-savvy panel considers the opportunities, downsides, potential risks, and leadership responsibilities around the use of artificial intelligence by nonprofits. They’re Afua Bruce (ANB Advisory Group LLC); Allison Fine (every.org); Beth Kanter (BethKanter.org); and George Weiner (Whole Whale).

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[00:04:19.33] spk_0:
And welcome to tony-martignetti non profit radio. Big non profit ideas for the other 95%. I’m your aptly named host of your favorite abdominal podcast. Oh, I’m glad you’re with me, but you’d get slapped with a diagnosis of algorithm a phobia. If you said you feared listening to this week’s show Artificial Intelligence for nonprofits, we take a break from our 23 NTC coverage as an esteemed tech Savvy panel considers the opportunities downsides potential risks and leadership responsibilities around the use of artificial intelligence by nonprofits. There are fewer Bruce at A N B advisory group LLC Allison. Fine at every dot org, Beth Kanter, Beth Kanter dot org and George Weiner at Whole Whale on Tony’s take to a give butter webinar. We’re sponsored by donor box with intuitive fundraising software from donor box. Your donors give four times faster helping you help others. Donor box dot org. Here is artificial intelligence for nonprofits in November 2022. Chat GPT was released by the company open AI they’re more powerful, maybe Smarter GPT four was released just four months later in March. This year. The technology is moving fast and there are lots of other platforms like Microsoft’s as your AI I guess the sky’s the limit. There’s Google’s help me, right? And Dolly also by open AI creates images. So artificial intelligence can chat and converse answer questions. Do search, draw and illustrate and write. There are also apps that compose music, create video and coding computer languages. A team at UT Austin claims their AI can translate brain activity into words that is read minds and I’m probably leaving things out what’s in it for nonprofits. What are we risking? Where are we headed? These are the questions for our esteemed panel. Bruce is a leading public interest technologist who works at the intersection of technology policy and society. She’s principal of A N B alpha, November, Bravo Advisory group LLC, a consulting firm that supports organizations developing, implementing or funding responsible data and technology. She’s on Twitter at underscore Bruce Alison. Fine is a force in the realm of technology for social good as president of every dot org. She heads a movement of generosity and philanthropy that ignites a profound transformation in communities. You’ll find Allison Fine on linkedin. Beth Kanter is a recognized thought leader and trainer in digital transformation and well being in the nonprofit workplace. She was named one of the most influential women in technology by Fast Company and is a recipient of the N 10 lifetime achievement award. She’s at Beth Kanter dot org. George wegner is CEO of Whole Whale, a social impact digital agency. The company is at whole whale dot com and George’s on linkedin. Welcome all our esteemed panelists. Thanks, welcome to non profit radio. We’re gonna start just big picture. Uh I’d like to start with you just what are you thinking about artificial intelligence?

[00:05:30.10] spk_1:
That is a very big picture question. What am I thinking about artificial intelligence? I think um there are lots of things to consider, I think first is um all of the hype, right? We have heard article after article whether or not we wanted to, I’m sure about the promises and the potential of chat GPT specifically generative AI more broadly. Um Well, uh you think about some of the image based AI solutions, generative AI solutions that are out there that have been in the headlines recently, of course, as someone who’s started their career off as a software engineer where AI has been around for a while. And so sure, generative AI is a different type of application of AI, but it is building on something that has been both out in the world developed for a while. Pre chat GPT most organizations or several companies just embedded AI into the tools you already use, whether it’s gram early or something, I’m betting ai into their solutions. So what I’m thinking about now is how do we help organizations navigate through all of the hype and figure out what’s real, what’s not real, um recognize where they should lean in, recognize where they can take a pause before leaning in and then of course, underlying it all, how do we think about access, how do we think about equity and how do we think about how embracing AI will change or evolve jobs?

[00:05:59.52] spk_0:
And these just define generative ai for us? So everybody knows what, what we’re referring to and we’re all, we’re all on the same platform.

[00:06:08.78] spk_1:
Sure. So, generative AI is where it is essentially looking at a large model. Chat gps specifically uses a large language models. So lots of text and looks at that and then gives you what is statistically sort of the next uh most reasonable or probable word based on a prompt that you give it. So developing the recommendations as you go along,

[00:06:35.79] spk_0:
Allison, please. Yes, big picture.

[00:08:08.00] spk_2:
Well, a few adjust said it beautifully that this isn’t a brand new idea, although we are in the next chapter in terms of advanced digital technology. I think organizations tony need to get their arms around this right now. Ai before AI gets its arms around them and their organizations, Beth and I started to look at AI about five years ago with support from the Gates Foundation and the promise of it is that AI can eat up the road tasks that are sucking the lifeblood out of so many nonprofits, staffers, they are drowning in administrative um tasks and functions and requirements that AI can do very well in fundraising. It might be researching prospects, taking the first cut, communications with donors not sending it out, just taking the first cut, helping with workflow, helping with coordination. Um And the responsibility is for organizational leaders, not line people and not tech people, but organizational leaders to figure out where the sweet spot is what we call co body between what humans can do and need to do. Connect with people, solve problems, build relationships and what we want the tech to do mainly rote tasks right now. So understanding ai well enough tony to figure out where it can um solve what we call exquisite pain points and how to make that balance between humans and the technology is the foremost task for organizations right now.

[00:08:32.35] spk_0:
Death.

[00:10:18.39] spk_3:
Great. So Alison and Noah said it so well. So I’m just going to actually build on it but go into a specific area that where that is kind of the intersection between ai and workplace well being and kind of the question, you know, well, ai fix our work. Um can it transform like the work experience from being exhausting and overwhelming to one that brings more joy that allows us to get things done efficiently but also to free up space to dream into plan? Um And or is it going to be a dystopian future? I don’t think so. Um And by dystopian related to jobs I’m talking about kind of, you know, we’ll get rid of our jobs like who, who will lose out. And um just a week or two ago, the World Economic Forum released a report that predicts that nearly 25% of all jobs will change because of generative ai and it’ll have a, you know, a pronounced impact by displacing and automating many job functions um that involve writing, communicating and coordinating, which is, which are the things that chat GPT can do so much better than previous models. Um But it will also create the need for new jobs, right? I heard a new job description recent, a prompt engineer. So somebody who knows how to ask the types of questions of chat GPT to get the right and most accurate and high quality responses. And I think I’m building on what Alison said about co body. I think this is the future where AI and humans are complementary, they’re not in conflict and it really provides a leadership opportunity to redesign our jobs and to rethink and reengineer workflows so that we enable people to focus on the parts of the work that humans are particularly well suited for. Like relationship building, decision making, empathy, creativity, and problem solving. And again, letting the machines do what they do best but always having the humans be in charge. And again, that’s why Allison and I always talk about this as a leadership issue. Not a technical problem.

[00:10:50.46] spk_0:
Leadership, right? Okay, we’ll get the leader responsibilities. George, what are you thinking about ai

[00:11:30.47] spk_4:
hard to add such a complete start here. But I would say that just because this is a fad doesn’t mean that’s not also a foundational shift and the way we’re gonna need to do work and how leaders are gonna have to respond. I also just want to say like right now, if you’re listening to this podcast, because your boss forwarded it to you saying we gotta get on this. I understand the stress you’re under. It is really tough, I think right now to be in the operational layer of a nonprofit doing today’s work expecting to make tomorrow’s change. So stick with us. We appreciate you listening.

[00:12:03.93] spk_0:
Thank you, George. Like happening to the co host role, which uh which doesn’t exist so careful care. Watch your step. Let’s stay with you, George, you and I have chatted a lot about this on linkedin. Uh use cases. What, what uh what are you seeing your clients doing with ai or what are you, what are you advising that they explore as their um as they’re also managing the stresses that you just mentioned?

[00:13:00.00] spk_4:
Well, right now we’re actively custom building AI is based on the data, voice and purpose of organizations that we work with. One of the concerns that I have is that when you wander onto a blank slate tool, like open ai Anthropic Bard, you name it, you’re getting the generic average as of who pointed out the generic average of that large language model which means you’re going to come off being generic. And so we’re a little concerned about that and are trying to focus our weight on how you tune your prompt engineering toward the purpose of the organization. We’ve already mentioned, grant writing, reporting applications, emails, appeals, customization, social post, blog, post editing. It is all there if you’re using it the right way, I think.

[00:13:22.32] spk_0:
And that gets to the, the idea of the prompt engineer to that, that Beth mentioned what, what you’re so avoiding that generic average with sophisticated prompts. George.

[00:13:47.96] spk_4:
Absolutely. Yeah. I mean, we jokingly call it the great jacket problem where I showed up to a conference and I was wearing the same gray jacket as another presenter and I was like, we both walked into a store and we both thought that the beautiful gray jacket we put on was unique and that we would be seen as such for picking out such a great jacket. When in fact, when you go in to a generic store and get a generic thing, you get a generic output. And my concern is that without that leadership presence saying, hey, here’s how we should be using this with our brand tone voice and purpose that every single new hire out of college. We’re running into the social media game. Beth has already played this game, Allison, we’ve already played this game where we handed the intern the Twitter account because they used it in college. We’re gonna just replay that again and I’d rather just skip that chapter

[00:14:22.42] spk_0:
and that we’re going to get into this too. That, that generic average also has biases and misinformation. False. Well, they’re not false, false information. Um How about you? What are you seeing your clients? What are you advising usage wise?

[00:16:24.89] spk_1:
A couple of things. So, first, I think Allison touched on this as well is that you can sort of take a breath. You don’t have to embrace everything all the time for everything. I know it can seem right now that everyone’s talking about generative ai and how it’s going to change your world. Um But you can sort of take a breath because um as I think Allison and Beth both mentioned, right, the technology is only good if it’s working for our mission, if it’s working for organizations. So really taking the time um as a leadership team to really be clear on what you want to do, what differentiates your organization and make sure your staff is all aligned on. That is the first thing that um advise organizations to do. The second is to think about then the use of AI both to help your organization function and deliver it services out in the world. But then also to think about how it impacts your staff. So I think sometimes we can get caught up in, we’re going to use A I hear it’s going to like, you know, we’ll be able to fix all of our external messaging will be able to produce more reports, will be able to produce more um grant applications, all good, all valid. But remember also, your staff has to learn how to use it and staff has to learn how to make the prompts. Your staff also has work internally that they are doing that. Perhaps AI could be used to help speed up the their task and free up their time and their brain space to lean into what humans do best, which is some of the relationships and having empathy. So thinking also not just about how AI can help you maybe generate more culturally appropriated images for different campaigns around the world or how generative AI can help you fine tune some messaging or how generative AI can help you better sort of segment and deliver services to, to your communities that you serve. But also how you can use AI to do things like help with notes, help with creating agendas, help with transcripts and more what are some of the internal things to really support your staff that you can, you can apply AI towards

[00:16:48.76] spk_0:
Alison that’s leading right to some of those rote tasks that that you mentioned. Um So I’m gonna put it to you in, in, in terms of uh Kirsten Hill on linkedin asked, what’s the best way for a busy nonprofit leader to use AI to maximize their limited time?

[00:18:49.78] spk_2:
So people are looking for some magic solution here, tony and we hate to disappoint them, but AI is not magic fairy dust to be sprinkled all over the organization. Uh This is a profound shift in how work is done. It is not a new word processing, you know, software AI is going to be doing tasks that only people could do until just now. Right? Any other year going back, um people would have had to be uh screening resumes or writing those first drafts, um or, you know, coordinating internally. And now basically the box are capable of doing it, but just because they’re capable of doing it doesn’t mean that you should, you know, unleash the box on your organization. Our friend Nick Hamlin at globalgiving, a data scientist said AI is hot sauce, not catch up a little bit. Goes a long way. We Beth and I have been cautioning people to step very slowly and carefully into this space because you are affecting your people internally and your people externally, right? If a social service agency has always had somebody answering questions of, when are we open? And what am I eligible for? And when can I see somebody? And now a chatbot is doing that, tony, you have to be really careful that one, the chatbot is doing its job well and two that the people outside don’t feel so distant from that organization that it’s not the same place anymore. So our recommendation is, that’s

[00:18:52.67] spk_0:
a, that’s a potential. I mean, it could, I guess mishandled this could change the culture of an

[00:19:36.78] spk_2:
organization. Absolutely. If you are on the outside and you’re accustomed to talking to Sally, who at the front desk and all of a sudden the organization says to you, your first step has to be talking to this chat bot online. Instead the organization has solved perhaps a staff issue of having to answer all these questions all at the same time. But it’s made the interaction with those clients and constituents much worse. So we need to first identify what is the pain point we’re trying to solve with AI is ai the best solution for doing that and then to step carefully and and and keep asking both staff and constituents, how is this making you feel? Right? Do you still feel like you have agency here? Do you still feel like you are connected to people internally and externally and to grow it from there? There is no rush to introduce AI in everything that you do all at once. There is a rush to understand what the impact of automation is on your organization.

[00:21:00.42] spk_0:
It’s time for a break. Stop the drop with donor box. Over 50,000 nonprofits in 96 countries use their online donation platform. Naturally, it’s four times faster, easy payment processing. There are no set up fees, no monthly fees, there’s no contract. How many of your potential donors drop off before they finish making the donation on your website. Stop the drop, stop that drop donor box helping you help others donor box dot org. Now back to Artificial Intelligence for nonprofits with fewer Bruce Allison. Fine Beth Kanter and George wegner. Beth, I see you taking copious notes. I think, I think there’s a lot you want to add.

[00:23:39.85] spk_3:
Oh, there’s so many good points made and I was taking a lot of notes because like nowhere to jump in. Um So a couple of things, uh George said, uh we, we did the social media thing and we turned it over to the intern. Let’s not do that again, but I’m not sure that’s gonna happen because with social media adoption, if we think back, uh you know, the dawn of social media started in 2003, it really wasn’t until six or seven years later. And I remember it quite distinctly when the chronicle, Phil apathy and organizations were really embracing it. There was a lot of skepticism because social media adoption was more of a personal thing because it started as the individual, it wasn’t immediately brought into the workplace. Um And I think chat GPT will be a little bit different because the benefit there is, you know, the sort of the allure of efficiency saving time, right? And or it can help us raise more money. So I think we might see it develop more quickly in the workplace and if nonprofit leaders are, are part do smart adoption, then there will also be the training uh required and the retraining and the re skilling. And I think for me, the most important thing about this is that it is going to change the nature of our work and that if you just let that happen, you’re missing an opportunity because we have a chance to really kind of accelerate workplace line learning, both, you know, formal and informal to, to re skill staff that in a way to embrace this, that’s not going to cause more stress and burnout. The other thing I was thinking about the great jacket and I love that um Metaphor George, I love it. Um In that, you know, if nonprofits are turning to and buying the $20 a month subscription for Chat GPT, they’re getting the Great Jacket version and missing out on the opportunity to really train it. But the other hand, if they’re just going without an organizational strategy, are they being trained in, are they put entering confidential information into Chat GPT? Are they using their critical thinking skills? Because we know that uh chat GPT can hallucinate and pick up crap? Right? Are they really, you know, are they, are they doing that? Like, are they just saying, write me a thank you letter for this donor versus write me a thank you note in the tone of in a conversational tone um that recognizes this donor, you know, quality blah, blah, blah, right? And um and then go back and forth and refine a draft. So, so there’s a piece of like um uh I guess technical literacy that has to be learned and that’s like the technical problem. But then there’s also this whole workplace learning and workplace um uh you know, reengineering of, of jobs and bringing in new jobs and different parts of descriptions that also need to take place as well. So we’ve got to prepare the organization’s culture uh to adopt this in a way that is ethical and responsible.

[00:24:07.24] spk_0:
George you feel any better.

[00:25:12.72] spk_4:
I’m not sure how I felt to begin with, but the uh the, the piece to add on as a nuance, there is not just the generic output but the normalization and ability for people to identify A I created content is going to explode. What does that mean if I were to show you a stock photo right now? Versus when I took on my phone, it would take you 0.5 seconds to be like, yep, stock photo, stock photo, stock photo. And we have all seen the appeals that go out with generic Happy Family with Sunset and background. And I think what’s going to happen is the text that is generated by folks that are using gray jacket G P T s is that your audience is going to see it, identify it and shut it down mentally. It’s like driving past that billboard or that banner ad. It’s going to be a wash. It may seem unique to you. But I think, uh, I think that’s another thing that we’re going to see happen. I just want folks

[00:25:13.82] spk_0:
to know, okay, I just want folks to know that that Great Jacket is a real story. You, you and you and another guy did show up with the exact same jacket

[00:25:21.64] spk_3:
at some point and 10 conference, wasn’t it in New Orleans?

[00:25:24.91] spk_4:
It was, it was a fundraising uh fundraising conference. And actually the other guy’s name was George. So there was two Georges to great jackets. I felt very um silly.

[00:25:38.76] spk_0:
Yeah.

[00:26:29.31] spk_2:
So, um the ultimate R oi Beth and I feel and we wrote about in the smart non profit is what we call the dividend of time that is to use AI to do those rote tasks that I talked about a few minutes ago in order to free up people to do human things. And George the opportunity isn’t we hope to send out more messages or to be, you know, continue down the transactional fundraising path. The opportunity is to use your time to get to know people and to tell them stories and to listen to them. So with or without A I organization stuck in that transactional hamster wheel tony for raising money. And if they can’t get out of that AI is definitely not going to help them. The opportunity here is to move that entire model into the past and say we’re going to create a future where AI gives us the time and space to be deeply relational with people. That’s the opportunity.

[00:27:17.67] spk_0:
Well, I’m gonna come to you in a moment and talk about how we can prevent the, this generic average, this gray jacket uh from taking over our culture. But Alice and I just want to remind you that when I had you and Beth on the show to talk about your book, The Smart non profit, I pushed back on the dividend of time because it feels like the same promise that technology has given us through the decades. And I’m not feeling any more time available now than I did before I had my, my smartphone or um whatever, whatever other technology I’ve adopted that was supposed to have yielded me, yielded me great, great time. I don’t, I don’t, I don’t feel any, any greater time.

[00:28:42.12] spk_2:
I don’t believe that that was the promise before. And certainly what we found with the last generation of digital Tech tony is that it made us always on and everything became very loud and very immediate. No question about it. And this next chapter in AI is not guaranteed to give us time. What we’re saying is there’s an opportunity to work differently and to create this time if leaders know how to use it. Well, that’s the big if, if we’re just going to sit back and said late, let’s ai supersize our transactional fundraising and send, send everybody 700 messages a day because that’s worked so well said very sarcastically then no, it is not going to make us any free up any time. But what we are saying is this technology has the capacity to do all of that work that is sucking up 30 40% of our time a day and we could be freed up. But only if we use it smartly and strategically,

[00:28:51.05] spk_0:
how about, you know, how we can help prevent these generic averages with their biases and marginalization of already marginalized voices. You know how and, and just from the fear of taking over the institutions, culture, how, what are the methods to prevent that?

[00:33:20.42] spk_1:
Um Sorry, I think I would start with an analogy that I’ve used before. That technology is not a naturally occurring resource. There’s no like river of technology that we just walked down to and scoop up and now we have technology and it immediately nourishes us to some of what Alison was just mentioning. Um in order to actually use AI effectively, it takes intentional management, it takes intentional decisions about how to use it when to use it and why to use it. And so that definitely applies when we think about how do we differentiate, differentiate ourselves even as we use AI and also how do we make sure that we then are being intentionally inclusive? Um I don’t know of any technology that just by happenstance has been inclusive. Um And so it requires intentional decisions. So some ways that bias can appear in generative ai systems are with some of the, the coding that is done inherently with some of the data sets that are used. Even with large language models, they reflect right now every on the internet. Um I know a lot of great people on the internet, there’s a lot of things on the internet that do not align with my values, um or even my actual lived experience. Um And so how do we then think about sort of combating that? So I think one, we’ve already touched on prompt engineering to make sure that we are asking it the things that we want to get back if you ask chat GPT, for example, um to describe what, what are risks with chat with generative AI will give you one list. You refine that prompt to include specifically what a risk with chat with generative ai including or specifically affecting women or people of color. It will give you a more refined response. Chat GPT a month ago. If you asked it, the doctor and nurse were arguing because he was late, who was late. It would tell you the doctor was late. He asked the same question but said because she was late, it would tell you um it was the nurse that was late, that now has changed because the people who are programming to GPT have manually made those changes. So as we think about how we can use it, it is through some of the software that we’re building on top of it, some of the plug ins that you decided to take advantage of, to not take advantage of how you might be able to use it on your own sort of proprietary information with the right parameters in place to keep it on your, keep it with your own data in ways that make sense for your organization there. Um I think it’s an opportunity for funders to fund the creation of new data sets or fund the creation some more responsible plug ins or fund um you know, new open source developments as well. So I think that’s an exciting play there. Um And then I think also there is an opportunity to use chat GPT or sorry, generative AI in ways that really do enable more representation. Um Working with someone who is um an advocate for women’s rights in India. We’re talking through ways that she could more quickly generate posters and informational materials using generative AI for both images and text for different places on the subcontinent that she couldn’t physically get to. Um And that she didn’t have talent on the ground to get to. That is different though I’ll say from the announcement from LEVI a couple of months months ago that they were going to use chat cheaper generative AI to create a diversity of models rather than hiring people or buzzfeed recently saying um shareholders meeting that they would use AI to help create authentic black and Latino voices presumably um instead of talking to actual authentic black. So um they didn’t, she was a statement a day or two later saying no, no, no, that’s not what we meant, we meant something else. Um But, but my point is there are ways to think about how you can use generative ai as a nonprofit organization to better reach and connect. But also make sure that you are still doing it in a way as I think all of us have said so far, that really does center people that does center communities and isn’t trying to necessarily replace those relationships.

[00:34:11.43] spk_0:
Beth our our master trainer, I see a need for training for leaders for for for users. I mean, I’m not seeing any of this happening now, I’m not seeing how to use, you know, but is there, is there a training issue here for, for people at all levels? You’re sorry,

[00:35:55.78] spk_3:
sorry about that. I don’t want them back. Absolutely yes. But we, I make a distinction between training and learning. Alright. So training professional development, formal ways of learning particular skills and those might be more around the technology, literacy, literacy skills like, you know, prompt engineering, for example. But then there’s also the informal piece of learning which is informally uh discussions with different teams about how it’s changed their job, right? Or uh or, or reflecting on a job description or, or job workflow that needs to be changed and then sharing that with other departments. Um So, you know, so there’s kind of like workplace learning that is connected to with the workplace culture. Um and which in some ways has nothing to do with the technology. It’s kind of like as a result of the technology. Uh what do we now have the possibility to do because we have this freed up time or because we have not spent so much time staring at a blank screen and not doing anything because of blank screen syndrome. You know, chat DBT has like helped us get to that first draft quicker and maybe human editing has done the second and the third, third draft. Um uh and we’ve gotten a better result. Um And that has improved our end results with our fundraising goals or whatever we’re trying to accomplish. Um you know, what comes next. Um So those are the pieces of learning that, um you know, that haven’t been possible a lot of times in nonprofits because we’re so busy trying to get the stuff done on our to do list and, and or were being overwhelmed. So, um so what, what is possible now that we’re able to do our jobs better and we’re able to take on these different tasks. How can we improve our results? Um And outcomes,

[00:36:24.68] spk_0:
George, how are you teaching your, your clients who are hopefully translating that into learning about using non using generative ai are you, are you talking directly to leaders? Are you, are you training users on, on better like skills like better prompting? What’s what does teaching training look like for you?

[00:38:14.82] spk_4:
I mean, we’ve done our best to put out as much free content as possible, first and foremost, to try to, you know, raise the tide of understanding for nonprofits and we’re putting all of that out as fast as I can think to create it internally. We’re having weekly training sessions on use cases for us and we’re actively building and improving on client custom created GPT uh endpoints that pull their data in and their purpose in. I want to go back though to Beth talking about what actually, you know, education and this looks like and we could train you on how to swim over this podcast. We could talk about all the things you need to do. Like I’m watching my daughter learn to swim. There’s no storybook, there’s no encyclopedia, there’s no webinar that you could watch that would teach you how to swim. There is a fundamental component of this. If you jumping in the water and interacting with the tool learning, coming back, realizing where it frankly lies to you. As I am really happy, we have all pointed out where it hallucinates where it’s helpful and where the opportunities are. And by the way that’s gonna change next month and so it’s not a single point in time and, you know, this, you, you’ve been an engineer for, you know, a while and seen it’s like the, you know, the code you played with, you know, a month ago, it’s just different tomorrow and what’s possible is different tomorrow. Um On the other side of the coin, I’m a little concerned, you know, we have gone through and maybe you’re getting anxiety when you hear yet another tool. Yet another tool. There’s over 1600 tools listed on just one site, future tools dot IO. And there’s going to be even more tomorrow. There are 95% of these things that are just going to be gone within a year. So I’m also cognizant of the rabbit holing that can happen in this.

[00:41:48.75] spk_0:
It’s time for Tony’s take two. I’m doing a Give Butter webinar later this month, debunk the top five myths of Planned Giving. I am especially excited about this one because the Give Butter host Floyd Jones and I are gonna be together co located face to face person to person in person real time. So, uh the energy that he brings and I try to keep things light moving. I think we’re gonna have quite a bit of infotainment on, on this one with Give Butter debunked the top five Myths of Planned Giving and it’s Wednesday, June 14th at two p.m. Eastern time. But you don’t, you don’t need to be there you can get the recording. If you can’t make it live. Watch archive. I used to say that on the show, listen, live or archive now it’s just listen, archive no more live but this is listen, live or archive bonafide. Uh If you want to make a reservation, you go to give butter dot com, then resources and resources and events. Very simple. So make the reservation. If you can join us live, that would be fun because I love to shout folks out and I’ll answer your questions. If you can’t sign up and watch the video, it’s all at give butter dot com resources and then events that is Tony’s take two, we’ve got the boo koo but loads more time for artificial intelligence for nonprofits, I’d like to turn to some of the some of the downsides even more explicitly. So we’re all talking about efficiency and uh the the time time saved the dividend of time. But um at what cost, what potential cost, short term, long term, um We’ve already talked about, you know, they’re being a bias towards dominant voices that are existing, dominant voices remaining dominant. Um For you had a great example of someone in in in India, right? Trying to, trying to represent folks that she can’t get to see. So there, I mean, there’s a potential upside but you know, all this at, at what uh at what potential cost and then there’s, we haven’t even mentioned, we mentioned false information, but in the video realm, deepfakes, video and audio, deepfakes, photograph, deepfakes. Who wants to, who wants. I’m being an egalitarian there who wants to uh launch us into the, the risks and downsides part of the conversation.

[00:41:54.45] spk_1:
I’m happy to start, I’ll say for the record, I am generally an optimist. However, um there, there

[00:42:02.41] spk_0:
are some things uh we’ve taken judicial notice.

[00:44:17.34] spk_1:
Thank you. Thank you for the record. It has been noted, I appreciate that. Um So again, just reiterating what we’ve already said, intentionality really matters here without intentionality. Um Things can go really wrong because General Ai has the ability to hallucinate. Um And because General Ai is reacting to what data already exists, recognize that sometimes the things that decisions that we can make based on that could be really wrong. So um if you can think through and imagine how Ai might be used to help with hiring processes, um even with a more standard version of AI, for example, Amazon a few years ago, put some work into developing a system that would identify people who were best poised to be managers and succeed in senior management at Amazon. The results of the AI show that white men from particular schools were best boys. Is this actually true based on skills? No, but it was based on the data that they had, which was trained on their internal data, which showed being a company and Northwest, it just reflected what their practices had been in some of the things they changed. Amazon end up not rolling that out because they had a human in the loop there that sort of looked at what was coming out and showed that in reviewed and determined this is not actually in line with our values is not in line with what we’re trying to do. Um So I think uh pushes to completely remove a human from that decision making loop are ways that generative ai can go really wrong very quickly in organizations think we’ve already started to talk about some of the bias that can appear in results. Um give the example already with gender that is true for um along a number of other demographics as well. And so not correcting for that or recognizing even that even with these large language models, even with something that’s trained on the internet, um not everyone is represented there. And so making a lot of decisions based on what’s there may not give you and may not give you the most inclusive and equitable response that you want. I think those are two ways that this can go wrong.

[00:44:33.58] spk_0:
Allison anything you wanna, you wanna add to this? Sure.

[00:45:47.94] spk_2:
Um So the AI revolution is far bigger than Chad GPT in generative AI AI is going to be built into every software product that an organization buys in. Finance in hr in, you know, customer service in development. Those products were created by programmers who are generally white men and then trained on historic data sets, which as you just mentioned, are deeply biased as well. So you have a double whammy that by the time the product gets to an organization, it has gender and racial bias baked right into it. This again is why it’s a leadership problem, tony, we need organizations to know what to ask about these products, to ask how it was built, what assumptions were made in building and how it was tested for bias, how you can test for bias before that hr software program you just grabbed through into your mix is screening out all of the black and brown people applying for these positions. So these are real everyday concerns about integrating AI into work and why we need to be careful and strategic and thoughtful about how we’re integrating it into organizations.

[00:47:32.67] spk_3:
Yeah, Beth, I really want to pick up on a point that a film made about um the concern about not having human oversight at all times. And one of my favorite examples of this comes from Kentucky Fried Chicken in Germany. And um they were using a generative ai tool that was um that could develop different promotions that they could put out there. And the data set that it was using was a the calendar of holidays in Germany and of course, then some promotional language like 5% off cheesy chicken, right? Um And they got into trouble because there, there was a lot of social media messaging that was just put out their generated by the generative ai and the message was um happened on November 9th, which is the anniversary of Kristallnacht, which is considered the beginning of the Holocaust. And the, and the promotion was, you know, enjoy $5 off a cheesy chicken to celebrate the night of broken glass. And, you know, and so I think that the issue is, is that we begin to put so much trust into these tools that we think of them as human or the equivalent of human intelligence. And that, you know, we just take it for face value and we don’t have that human intervention with those critical thinking skills. And um and that’s where harm could be done um to the end users. Um So I, I just really think it’s comes back to that co batting example that we’ve talked about and again, the, you know, the need for leaders to really be reflective and strategic in how they executed. It’s not just about learning how the right prompts to ask GPT chat to get a particular output.

[00:48:10.15] spk_0:
There was another example of that uh at, I think it was at a college. Uh they put out a press release and at the bottom of the email, it said, you know, generated by chat GPT or something. I mean, so a human, you’ve all talked about humans being involved with the technology you know, a human hadn’t even scanned it to, uh, to know to take that, that credit line off the, off the email. So, you know, like blind usage.

[00:48:58.01] spk_3:
That’s an interesting thing to, to think about. Like, um, do I disclose, like, if I, if I was writing a post an article and I went to GPT chat to, like, because I needed to get it from 1000 words to 750 words. And I could ask it, you know, too long. Didn’t read standby for some text, please reduce from 1000 words to 750 words um which I actually have used, but I don’t take a cut and paste and I actually sat and compared what it, how did, how did it change the language? And one thing I did notice is it took out any sentences that had a lot of personality to them and it transformed it into this very generic kind of text, you know. So again, it requires a human editorial oversight. If you will,

[00:49:20.80] spk_0:
George, you want to talk about risks downsides.

[00:50:17.62] spk_4:
Yeah, I would say this is more of a bigger picture risk that I see as the net result of we’re talking about GPT tools being built into everything we use. One is that, you know, if, if you were using it blindly, you were the product you’re handing over information. Uh There was a actual open ai hack. Well, a hack or data leak where all of the conversations that were being uh stored on the side were accidentally shared and open. And so I think that’s something to be aware of bigger picture. I am watching very closely. The impacts of chat, first search chat, first search bard and being barred is Google’s AI that is now rolled out out of their private into a public beta is going to destroy organic traffic for information based searches to nonprofits. Inside of what I believe is the next two years. The second order effects of that are so many that we would need several podcasts to understand, but I’m no longer telling clients that we should expect more organic traffic next year. Versus this year.

[00:50:57.37] spk_0:
You experienced this with your own with the whole whale site. You, you had, you had, you did a search and it gave and the search tool gave you back some of your whole whale content. It did credit it. But then your concern was that that credit was purely optional, but right, you, you experience this with your own, with your own intellectual property.

[00:52:14.75] spk_4:
I’m watching it across a lot of, you know, we get roughly 80,000 month in terms of monthly users looking for information that we put out there. I test what that looks like when I do similar searches on bing as well as perplexity dot AI and now barred. The thing that scared me the most is that bar just sort of decided not to even bother with the footnotes in its current iteration and just gave the answer to one of uh several articles that dr significant traffic to our site. There are two types of traffic that S C O is providing. It is informational and then transactional. And so for the informational, I would encourage your organization to do some of these sample searches and begin to plan accordingly. And it makes me a little sad that that part of nonprofits ability to be a part of the conversation when somebody’s asking for, I don’t know information about prep and HIV information or something about L G B T Q rights history doesn’t get you engaged with the organization. It just gives you the answer and there’s something missing there that I think is going to have negative downstream impacts for social impact organizations. And

[00:52:22.87] spk_0:
you expect to see declines in there

[00:52:38.37] spk_4:
will be a decline, significant declines. And that’s concerning to me because it’s cutting non profits out of the conversation that they have traditionally been a part of when people are looking for information. And especially in a time where we’re going to have a rapid increase in disinformation because these tools can be used to create that at scale.

[00:54:19.95] spk_0:
We already have enormous disinformation. It’s hard to imagine it growing exponentially or logo rhythmically. Um I’m interested in what you all think about my concerns. Uh Executive summary that it will make us dumber my my, my reasoning behind that is that a lot of what we’re suggesting, not just us here today, but a lot of what is being suggested is that, you know, it’s, it’s a tool, generative ai is a good tool for a first draft. Uh Beth, you mentioned the Blank Screen syndrome, but to me writing that first draft is the most creative act that we do in writing or in composing, it could be music. And my concern is that if we, if we’re ceding that most creative activity away, and then we’re reducing ourselves to editor or copy editor, not to, not to minimize the folks who make their living editing and copy editing, but it’s not as creative a task for a human as sitting in front of that blank screen or that empty pad for those of us maybe start, maybe start with pen and paper and, and then we’re seeding the most creative activity away and reducing our role to editor, which is an easier job than starting from whole cloth. And so I fear that that will make us uh dumber, reduce our creativity. And I’m saying, you know, generally dumber, you’re all being so polite. You could have just jumped

[00:56:12.96] spk_3:
in. I was well, I, I didn’t want to just interrupt you. Challenge you, but I do want to challenge you. I agree with you, but I also disagree with you. Um So one piece of this one thing that I worry about and it might be um science fiction, but I, um, and I haven’t yet seen research on this, but I do know there’s this thing called Google Brain. You may be familiar with it. Um You’re trying to remember something and you can’t remember it because you haven’t exercised your retrieval muscles from your brain. So you go to Google and you start Googling to, to remember something and it’s a thing called Google Brain. And there was a study that showed that people who were using Google Maps or the other or Apple maps um to navigate. Um it is making their geospatial skills less robust. Um And so the recommendation is you don’t want to completely lose your ability to navigate that you should like get a map, get to go back to a paper map. So there’s definitely some and there is research around this that there’s definitely when you’re doing something in an analog way, if you’re writing it down, it encloses your brain in a different way than if you’re typing it. So the thing that I worry about with this is less about it being creative, taking our creativity away because I think if if you’re trained as a prompted engineer, you could be trained to like brainstorm with it right in a way that sparks your creativity versus takes it away. But what I’m worried about is how does this affect, how will this affect the human brain? Um You know, down the road another decade or so that if we’re not using our brain skills of encoding information and retrieving information and it’s like a muscle, you know, is that going to make us more at risk for dementia or Alzheimer’s down the road? Um, I know it sounds crazy but that’s like the thing I worry about.

[00:56:47.28] spk_0:
I don’t think it’s crazy. That, that’s what I’m concerned about. I’m, I’m concerned on a world level that we all collectively will, will just not be as creative and I’m calling that will be dumber. I

[00:57:49.77] spk_1:
don’t think the amount of creativity and innovation is sort of finite and that if we use tools that we’re no longer going to be creative, I think we have computers now to help us draw, to help us um write, we can write on a computer versus before we had to use different paper, we had to only draw with a limited set of tools when we got, um you know, computer aided graphics and more, we just had more different ways to see the world, more different ways to uh to figure out what images we wanted to see and how we wanted to engage. Also someone who likes to write a lot. I’d say I’m really grateful for my editors and the fat that their brains were different than mine do when I start writing. And so um those skills are complementary. But I say that because I think that we will have to change sort of will evolve, how we think, what we think about and how we work. But I think that is a different type of creativity, different types of innovation rather than us just no longer being creative. Yeah,

[00:57:55.80] spk_0:
I didn’t mean eliminate our creativity but reduce it. It’s

[00:58:10.94] spk_2:
important tony to stay out of these binary arguments of AI is so bad or AI is so good, it is going to be a mix as technology always has been. I was just reading a book the other day that talked about the introduction of moving pictures and how how appalled people were that, you know, they could see these images over and over again, right? And was going to take away all of people’s creativity.

[00:58:23.12] spk_0:
The same thing when when silent movies became talking,

[00:58:36.56] spk_2:
you know, we do this every time we are changing our brains. I’m not saying that we aren’t, however, there is going to be an explosion of creativity of jobs we haven’t thought of yet of opportunities, we haven’t thought of that comes out of this next chapter that we are just beginning now. And I think it’s important to go into this with as much information as we can cautiously again, but with a sense of X with a sense of excitement and adventure as part of this because something really, really interesting is about to unfold.

[01:00:49.90] spk_3:
And I just want to also affirm what Allison just said this kind of new creativity and it was making me think of. Um I think it was about a year ago that dolly came out, which is the image generator um that works by looking at patterns and pixels of images that are on the internet. Um And, and create something new based on your response. And I know um and I heard an artist talking about this, like, you know, there’s this whole debate about, you know, should, is it our tools like dolly that are analyzing pixel patterns and images created by real artists? Are they stealing their work without their consent or without their compensation or is it or is this like creative thinking tool? So I, you know, I was messing around and I have a black and white Labrador party, you know, a Labradoodle party, black and white guy. And so I, I asked, you know, create a image of a black and white party. Labradoodle surfing a wave and the style of Hokusai. And it generated for um images in the style of Hokusai. Some of them were silly. Some of them were, oh, this is really interesting and it prompted me, oh, what would it do if I asked it to do this in the style of Van Gogh or the style of money? And then I started getting all these other ideas about things that I wanted to do. And before I knew it, I had 1000 different images of a black and white party. Labradoodle doing all kinds of things that I wouldn’t even have thought of if I hadn’t seen, like, the response that it gave me from the first one. Um, but so is that different than if I were to, if I just did a brainstorm with myself about what I could draw, if I could draw anything, or is this aided creativity much in the way that an artist would go out, you know, and look at landscapes for inspiration.

[01:01:22.10] spk_2:
Yeah. Now one place, one place in a lot of trouble, tony is the fact that our policy makers are so far behind on AI, right, we’re gonna have enormous copyright issues. We have enormous ethical issues coming up of when AI should be used in policing. The department of Defense is experimenting right now with completely automated lethal drone weapons. Is that really who we want to be that we have robots killing people without any human oversight on the ground at all or, or in, you know, some, some headquarters at all, there are really profound policy issues that we should be talking about right now and we are way behind on those

[01:01:51.16] spk_0:
George you wanna comment on the role of government or, or push back on my

[01:02:45.37] spk_4:
uh the role of government is beyond my pay grade. If I’m honest, um you know, I’ll stick to my scope. I will say though tony in 2004, podcasting became a thing, new technology before that there were gatekeepers there and I think you’ve done very well as like as far as I know the longest running podcast for nonprofits, like it opens up new opportunities. There are over two million images created on Dolly per day and that was back in October. So I’m willing to bet it is increase the output, you know, at, um and on a personal level, like it has increased my output and I have, you know, had a lot of fun building and working with it. And as it, you know, unblocked me for, for the new creation of content undeniably though the way we use tools then shapes the way we change. And I do agree, there is a depth of knowledge potentially lost in being able to simply say, write me an article about this thing and then I tweak it as opposed to that part of learning an approach. And I think academia is um really reeling from how to teach this next generation. And I’m, I’m curiously watching how they train the next generation of people coming into the workforce on

[01:03:24.54] spk_0:
you all gave, well, let me say you all gave your all optimistic about your, your, your, your all probably more optimistic. I’m, I’m, I don’t know if I’m skeptical, I’m just concerned, I’m just concerned about the dumbing down of the culture and the culture, meaning the world

[01:03:31.72] spk_2:
culture, you

[01:03:33.67] spk_1:
know,

[01:03:36.64] spk_2:
have you seen our culture? How much dumber?

[01:03:39.30] spk_0:
Yeah, we’re starting at a pretty low level. That’s, that’s how bad I think it could get. Yeah. Yeah,

[01:05:17.38] spk_1:
I just wanted to uh um just emphasizes, I don’t think we spend enough time on one of Alison’s last points about the, um the copyright issues, the ownership issues, even as the data economy has exploded since the age of big data was declared. Um We have created systems that really extract from certain people, some certain populations, historically marginalized populations rather than enable and empower these same populations who stated we then rely on or I should say corporations in general sometimes oftentimes nonprofits as well. Um And that is just um increased at scale with generative ai with AI more broadly, right? And that um you know, especially with generative ai and things that scrape the whole internet of things that people put out there no longer as George uh mentioned no longer at attributing sources, no longer pointing to source material, no longer giving credit to people. Uh Same with artists and music and others. I think that is a huge issue. And I think one um from an ethical perspective, ethical perspective, especially for a nonprofit whose mission is to empower marginalized communities. And that’s a particular nonprofits mission. It’s a big question to consider of how and when should you use generative ai systems that do not um attribute information. Um And don’t sort of close that loop back to the people who powered the systems?

[01:05:25.25] spk_0:
All right.

[01:05:26.81] spk_1:
I don’t know, that’s a positive note, but it’s a note that was,

[01:07:14.66] spk_0:
that was more mixed and positive but great valuable points, you know, great promise um with potential catches and leadership, the importance of leadership and, and proper usage and all. All right, thanks to everybody for Bruce, you’ll find her on Twitter at underscore Bruce. She’s principle of A and B advisory group, Allison, fine president of every dot org where there are fires to put out. You find Alison on linkedin, Beth Cantor at Beth Kanter dot org and George Weiner, Ceo of whole Whale whole Whale dot com and Georges on linkedin. Thanks everybody. Thanks very, very much. Next week. What power really sounds like using your voice to lead and using your executive skills if you missed any part of this week’s show, I beseech you find it at tony-martignetti dot com. We’re sponsored by Donor Box with intuitive fundraising software from donor box. Your donors give four times faster helping you help others donor box dot org. Our creative producer is Claire Meyerhoff. The shows social media is by Susan Chavez Marc Silverman is our web guy and this music is by Scott Stein. Thank you for that affirmation. Scotty B with me next week for nonprofit radio, big nonprofit ideas for the other 95% go out and be great.

Nonprofit Radio for November 29, 2021: How Much To Spend On Ads In 2022

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George Weiner: How Much To Spend On Ads In 2022

“The Nonprofit Advertising Benchmark Study” will help you decide what’s the right amount for you to spend on advertising if you want to be comparable to your peers. At the very least, it’s a place to start your research on ad spending. George Weiner explains the study. He’s chief whaler at Whole Whale. Does that make him the Captain Ahab of nonprofit tech? Listen to find out.

 

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[00:02:18.04] spk_1:
Hello and welcome to Tony-Martignetti non profit radio big nonprofit ideas for the other 95%. I’m your aptly named host of your favorite abdominal podcast. Oh, I’m glad you’re with me. I’d suffer with odo toxicity if I heard that you missed this week’s show How much to spend on ads. In 2022, the nonprofit advertising benchmark study, we’ll help you decide what’s the right amount for you to spend on advertising if you want to be comparable to your peers at the very least, it’s a place to start your research on ad spending George winner explains the study. He’s chief whaler at whole whale. Does that make him the captain ahab of nonprofit tech. Let’s find out on tony state too. It’s the holidays and fourth quarter sponsored by turn to communications pr and content for nonprofits. Your story is their mission turn hyphen two dot c o. It’s a pleasure to welcome George winner to nonprofit radio He is the chief whaler of whole whale, a digital agency that leverages data and tech to increase the impact of nonprofits and for benefit companies. He’s also a co founder of power poetry, the largest teen poetry platform in the US A safe creative free home to over 500,000 poets. George was chief technology officer of do something dot org. He managed the site overhaul twice. Winning a webby award and helped build a community of over a million and a half young people taking action. He’s an evangelist for democratized data and measuring success. The company is at whole whale dot com and at whole whale. Welcome chief whaler. How are you George

[00:02:21.24] spk_0:
wow, incredible intro. Thank you so much. tony Thanks for having having me on

[00:02:26.63] spk_1:
my pleasure, my pleasure. What’s what’s behind the company name? Whole whale which which leads you to be whole whaler. What’s that about?

[00:03:23.44] spk_0:
Yeah, we found it a decade ago and at the time the thought that struck me was ultimately the way that the nan, you know the Nantucket whalers in the massachusetts whalers in the 18 fifties would look at a whale and they would basically bring back lamp oil and throw the rest away in times of abundance. We tend to be a touch wasteful instead of using everything from the bluebird of the bone. And I took that lens and said, I felt like many social impact organizations weren’t looking at the opportunity to leverage data and technology and the rising web in that way. And they simply said, hey, hey, here’s the internet and the most powerful tool we’ve ever been given. Let’s put a donate button on it and call it a day. And so I kind of gave rise to how we view this time of abundance with an eye toward the best ways of leveraging data intact to increase the impact.

[00:03:57.64] spk_1:
Very interesting, very interesting. Uh you uh you drew that analogy between the two interesting. All right. Um, so are you the captain? Ahab, do you have a deep revenge? I’m trusting you know the story of Moby dick. Do you have anything deeply uh hurting you that you need revenge for? That’s going to drive the the ship of what was it the uh um The pick, watch the pick what it’s going to drive your whole whale peak watt into the ground.

[00:04:11.14] spk_0:
I’ve got a few white whales. Maybe I chase, you know, I got a few white whales maybe that I chase. Yeah. The other reason I found it all whales. Because of the nautical puns. I mean, you’re virtually swimming in them anywhere you turn and with regard to the things that chase, you know, the nonprofit ad study is one, but we like we go after interesting projects along the way. In addition to our our day to day work as consultants. We also develop products and try to put things out there in the ecosystem that help others do their jobs better. Or learn more about whatever field they’re wandering and so I can get distracted by a white whale or two.

[00:04:48.64] spk_1:
Okay, I see the navigate your helping folks navigate. Yes. The of course, the nautical you’re right. The uh nautical puns are are abundant.

[00:04:52.84] spk_0:
Yeah, they have to be they have to be

[00:05:05.44] spk_1:
navigate navigate the waters. All right. All right. So, we’ll see if your captain Ahab flush that out. See if you’ve hopefully you’re not going to do to uh to hold whale. What what the captain did too

[00:05:07.82] spk_0:
Made it 10 years so far. But I think that’s mainly because of an amazing team that basically just puts me in the right direction. Okay. It’s

[00:05:18.64] spk_1:
very gracious of you. All right. What is this this uh, nonprofit advertising benchmark study? What’s this thing all about?

[00:06:18.54] spk_0:
We wanted to answer a simple question and you know, never underestimate the power of a simple question because it can lead to, you know, a lot of, you know, threads essentially how much should a nonprofit spend on ads? This idea of advertising and promotion, it’s available technically informed nine nineties and with the help of cause I. Q. Which is a, you know, been a great partner in this. We analyzed seven over 7000 organizations to get that answer. And we chose organizations with a number of filters because clearly, you know, there’s 1.8 million nonprofits out there depending on how you’re sort of measuring the five oh one C threes and they’re like, oh that’s too many. And also, uh, you know, a third of them are not necessarily even over the threshold for reporting to the I. R. S. So we wanted to find 1 to 10 million in revenue organizations between one Million and 10 million and also met other certain criteria. And then we just sort of just, we dove in headfirst to to answer that question, how much should non profit spend on paid ads.

[00:06:36.34] spk_1:
Okay, so among your sample size of 7100 or so you you were you looked to see there’s I guess there’s a, there’s a line on the 9 90 that where folks where organizations report advertising expenses and that’s what, that’s what you were looking for,

[00:07:08.64] spk_0:
correct. It’s, you know, publicly disclosed because that’s, you know, the game and we look at that. So, you know, there are certain assumptions there that, you know, we note in the study of what that line is and what it isn’t. So it isn’t necessarily saying All right, this is the amount that people spent just on facebook ads or google ads. It’s advertising and promotion of the organization. So, you know, that could extend to people Paying for print ads. It could even in some cases, but we filtered it out for the most part include the cost paid to advertising firms to run ads. Uh, though many according to, I think the analysis, we didn’t hear only about like five or 10% in that range actually incorporated in there. And those are the outliers.

[00:07:43.14] spk_1:
Okay. Okay. And this specifically, thank you. Cause I was going to ask you about, you know, how, how we define advertising or how you define advertising this study and it specifically doesn’t include google ad grant money. Right? That’s, that’s different.

[00:07:58.84] spk_0:
Super important. Thanks for bringing that up. And it’s one of those sort of, you know, expert pieces there that it’s an in kind amount that doesn’t get reported on that line. Right? The google ad grants and you 10-K per month in money spent is something that would show up on your in kind value, not actual dollars out the door.

[00:09:03.04] spk_1:
Okay. Right. So it’s captured elsewhere. So it’s not part of the study here. Okay. Um, and so I was struck, you know, I’ve I’ve known that these that the vast majority of nonprofits are are smaller, you know, smaller revenue amounts. But I guess just reading it again, reminding me refreshing my recollection that uh, 93 a half percent of nonprofits are under $1 million dollars in revenue and and only 1.5% are over $10 million dollars in revenue. So that this this, you know, our universe of nonprofits, that this is not related to advertising, although, I mean, it is in terms of big words spend more on advertising, but that’s not my point. My point is just it just drives home that so many of our of our colleagues in nonprofits are organizations that are under $1 million dollars in revenue, 93 93 a half percent are under a million dollars, vast majority. Again.

[00:10:11.74] spk_0:
Yeah. And it’s important to note that, you know, because I think maybe in our minds or because of the narratives, we see large organizations and assume that they’re all like multimillion dollar, but like, this is the truth of it and that’s Yeah, that 1.44, exact five oh one c three nonprofit organizations and, you know, there’s a very, very small fraction of them that kind of live above that line, You know, like roughly speaking the same is kind of true in business ecosystems as well. You know, it’s hard to get to a million dollars. You realize that. But also when you look at the market how, how much of a long tail there really is. And we wanted to sort of remove that and say like once an organization has kind of, I mean not saying you’ve made it, but you were in rarer air, certainly after crossing $1 million you were running something that certainly has enough residents to to reach that level. And then we also filtered by age to be like, well, wait a minute. Maybe they’re like one night, you know, overnight successes or pieces like that. So we looked at organizations over under this century funded founded in uh, and and took a look at that as well. But it is, it is a curious point. We wanted to start with that context because I don’t think it’s given enough in the sense of nonprofit industry and you like immediately sort of have this availability heuristic meaning like I remember the last thing I saw and either it’s the red cross or you know, you know, pets down the corner saving one pet at a time.

[00:10:41.44] spk_1:
The availability heuristic. Thank you dropping that tech guy. You know, you have to, uh, well you, I’ll keep you out of jargon jail because you you explained it immediately

[00:10:47.03] spk_0:
go to

[00:10:51.64] spk_1:
The availability heuristic now it’s a good one. I love it. Uh huh. And you uh you flushed it out so folks understand what you’re talking about.

[00:10:56.23] spk_0:
I still will probably end up in jail though, won’t you

[00:11:10.64] spk_1:
didn’t do it? Well that could be we’ll see how we’ll see how the conversation goes. But you didn’t do it pretentious li like you know the all you would have available, all you, all you would have within your within your thinking at that point would be subject to the availability heuristic.

[00:11:13.34] spk_0:
Yeah. And then just leave it. Didn’t just leave it

[00:11:47.74] spk_1:
there and then make me ask and then make you flush it out. Which would have put you in jargon jail. So all right now because your availability heuristic. Thank you. This is a technology guy. Data data data guy, expect those things. I have a book like that. What’s that way out of this cool book from college. Alternative interpretations of data based conclusions. I think availability is in there. You know, uh confusing correlation and cause and effect. That’s a very common one. This is cool book. Can you show it to you

[00:11:50.24] spk_0:
what’s strong? I like that

[00:12:07.34] spk_1:
rival hypotheses. That’s what it is, rival hypothesis. Alternative interpretations of data basically. Okay, so enough pretense for now let’s talk about the study a little more. Yes. What stood out for you uh findings. What what what was most informative to you that and you think our listeners need to know?

[00:13:12.84] spk_0:
Yeah. The top line, I feel like we have that that cliffhanger is like how much should number I would spend. It’s like how much should we spend already? What’s the number? So I felt like we owed it to the audience to give them a number with a lot of asterisks and the number which is the median spend for our sample was basically $12,070. Which roughly equated to 5% of the median revenue as a as a ratio. So you know, knowing nothing else turning off the podcast right now you’re like, all right, we should be like at least considering that. and 60% of This sample, 60% were actually spending on ads or spending on advertising and promotion. I’m going to use that interchange of advertising and promotion and add but advertising activities 60% did and 40% did. Not. That actually surprised me. I actually really believe that there would be a lot more organizations spending. So you know that that was a bit of a surprise to me.

[00:13:16.87] spk_1:
Okay. Right. Not even not even 2/3

[00:13:20.04] spk_0:
are spot on your Yeah, exactly. Exactly on

[00:13:22.59] spk_1:
Advertising. Okay. And the median spend is 12,000 between friends. We can around the 70 away.

[00:13:29.35] spk_0:
So 12 spot me 70 12,000

[00:13:31.94] spk_1:
dollars. There you go. That’s that’s your

[00:13:34.30] spk_0:
feet for being option.

[00:13:48.14] spk_1:
Um uh And the average was, you know, it was interesting. The average was quite the average of like 4.2 million wasn’t the average was very highly skewed. Do you remember that? Right? It was it was something very huge.

[00:14:37.04] spk_0:
Oh yeah. I mean you had to throw out the average because of these outliers. There’s like these massive positive outliers and there’s some nonprofits out there that are spending quite significantly. And you know, you sort of begin to touch on lobbying. There’s like um, you know, there was a pro life America group up there and the millions of dollars and you know, that’s that’s not data that necessarily is going to help you, right. If Bill Gates suddenly walked into your boardroom, you’d all be average billionaires. Not helpful. Right. That that kind of insight is not helpful. So that’s why we chose the median. I’m sure it’s in your book of trying to avoid those mistakes. And you know, we we went through and tried to explain why we chose the numbers we did and also give access to the full data dashboard if people are interested in it.

[00:14:51.34] spk_1:
Alright, so, well, since you mentioned access to this, how do how do folks get the summary and I’ll be sure to say at the end, you can remind me if I don’t, how do you get the, get the, get the summary of the study.

[00:14:55.14] spk_0:
So it’s at a whole whale dot com slash advertising tried to make it pretty simple. We have a nice infographic there and the ability to download the entire report.

[00:15:05.74] spk_1:
Okay. And there’s a little fee there’s a $5 fee if you want if you want

[00:15:08.89] spk_0:
All this. So yeah the full data and dashboard if you want to go digging into it. Um and getting access to that. Yeah that’s the $5 fee. But you get the whole study which is you know 21, pages of awesome. That’s you know that’s available

[00:17:05.14] spk_1:
right? That’s free. That’s absolutely free. Okay. Hold well dot com slash advertising it’s time for a break. Turn to communications. Are you making your plans for 2022 for fundraising? Marketing communications. Do you need help Look at turn two? If you’re like if you’re thinking about fundraising you need to raise more money in 2022 marketing and communications. Your content, the stuff that goes out all that that you’re creating for your donors for general awareness. If you need help with it, think about turn to because that’s what they do. They have a background in non profits. They understand the nonprofit community so you’re not you’re not hiring an agency that only works with you know cos they understand the nonprofit space. They can help you develop your content, help you hone those messages, Get those messages out like you’ve been hearing me talk about, right? So if you need to raise more money or if you need to go to the next level in marketing communications In 2022, think about turn to because your story is their mission turn hyphen two dot C o Now back to how much to spend on ads in 2022. uh, the, the 80 20 rule applies here is one of your, One of your takeaways that uh, the top 20% spend about 80% of the ad revenue. Yeah.

[00:17:06.54] spk_0:
It always seems to happen and I always seem surprised when I find that power law, right? The 80 20 shows up on like

[00:17:21.64] spk_1:
how again does it keep happening time after time after time for in a wide variety of applications, 80 20 applies. How is our world,

[00:17:57.74] spk_0:
especially in actually, especially in financial distribution. But I was actually, I did the analysis. I was like, it probably won’t in this case show and I was like, you gotta be kidding me. So it wasn’t exactly 80 20 it was 24 76. So that’s about 24% spent 80% of that ad spend coming back to your point on average is being like much higher than in a potential bit misleading. So you do have that handful of organization spending quite a bit and the top 13 organizations in our study spent over a million dollars. So you kind of see that heavy, heavy waiting. And if it’s like, all right, you know, the purpose of our organization is the public awareness of this particular issue. And the way we do it is just turned donations into advertising and they’re nonprofits that just do that And they skirt the line awful close to lobbying, but they stay this side of fair.

[00:19:23.04] spk_1:
Okay. All right. Um, and you know, it occurs to me to this is, um, you know, you’re stuck with a lackluster host who is, whose thinking is not, uh, completely linear at all times, uh, if ever on that. But, uh, this is not, this is not a survey. So we’re not, we don’t have biases across people giving what they think is a good answer. You know, we don’t have those. So that just goes back to my book, the rival hypotheses, you know, self reports, not self reported data. Um, you do have the problem, like you said, you had to correct for whether people use agencies to produce their ads, but you were able to sort that out. So there’s, there’s some potential differentiation in, in the way people report organizations report advertising on their 9 90. But overall this is more reliable than survey data.

[00:20:13.84] spk_0:
Yeah. And I’m glad you brought that up. This is, you know, reported. So, I mean if you get back down to it, Yes, there is a human somewhere in the financial department of this non profit making that decision, but they’re doing that at a very macro stage and we’re pulling raw data were not wandering in and saying, Hey, how much do you spend on ads and you know, looking at, You know, 250 random nonprofits that decided to randomly fill out a survey with limiting information or you know, not being wanted to be fully transparent for whatever reasons. This is you know, this is source data from the organization according to the I. R. S aggregated by cause I. Q. And then we analyzed it to really find those answers and it’s something that we just felt was lacking. We felt like there was a lot of uh, we’ll call it a qualitative As opposed to quantitative type of research out there being like, Oh, here’s the official number. And then you look at the fine print and realize it was the opinion of 60 people not problems. And I’m like, I don’t know

[00:20:33.14] spk_1:
a lot of the answer. A lot of times people say, well it depends, it depends. So you’ve drilled down more than it depends.

[00:20:58.94] spk_0:
We’ve drilled down more and also been able to look at individual cause areas. So not just lumping one of my, one of my watchwords is being careful of lumping together the entire industry into one tidy bucket and assuming they all behave the same way. And if you know one nonprofit, you know one non profit and so we actually have, you know, in one of our findings divided up The type and cause focus of the organization to kind of get a better idea in that distribution, which tells, you know, another different story, which is all the more to say. I, I hesitated but knew we dessert we we owed the audience an answer of $12,000. But even that can be misleading depending on the industry and sub industry weren’t

[00:21:34.94] spk_1:
well, like arts organizations for instance, spend the most right. That’s what you’re, you’re, you’re teasing us a little bit. But I don’t do that to nonprofit radio listeners. You can, you can attempt it, but I won’t allow it. So, uh, let’s, uh, so arts organizations spend the most on advertising, right?

[00:22:15.54] spk_0:
Organizations were the highest spender. Again, surprising to me because I actually thought it would be health, I thought would be the health industry spending, you know, more to research awareness and pieces like that. But you know, frankly at the end of the day, you need to get people to attend to show up to, you know, go to these, you know, one time events to museums on location and so these, these were the highest highest spenders for sure. And promoters of performing arts sports and similar events were at the top and then the lowest, you know, because you look at the high and low, we’re less surprisingly the grantmakers and giving services like if you’re giving grants, guess what people find Jack, If you’re writing checks, people, people

[00:22:42.04] spk_1:
find you events, you, you you, you teased out what ad spending does too event income and I’ll let you, I’ll let you reveal what was found.

[00:22:46.44] spk_0:
Yeah, this is a bit of a nothing burger on the face of it because I had, here’s a case where I went in with maybe a touch of an agenda. I had an agenda. I’m going to admit it that I thought if a nonprofit was spending research, going

[00:23:03.00] spk_1:
back to my book, researcher bias.

[00:23:38.74] spk_0:
Yeah, this was researcher bias. But I left it in because I was so surprised that the ad spend of a nonprofit did not correlate. Uh, mind you even causation that didn’t even correlate in any meaningful way to hire event income. So event income is another thing technically reported by non profits in the 990 that you can pull aggregate and analyze. I’ll put an asterisk there, there are problems with that reporting which may be impacted this. But ultimately there was no correlation at all with this idea that or eggs without ads and with ads had any meaningful difference in the amount raised with event fundraising.

[00:23:56.54] spk_1:
Okay, Alright. Clearly you had, you had a hypothesis because that’s why you pulled the event income Data off the 1990s.

[00:25:03.44] spk_0:
Yeah. I mean, my hope was to show like I had, I had to hope that we would show that guess what if you’re spending to promote your event, your event does better And therefore an aggregate. They should be the folks that are making more money, you know, when in fact, you know, the of our, I can give some context with the data size. Uh, about 68% of our group had listed event fundraising expenses and 58% of that group reporting event fundraising revenue from their form nine nineties. So right in there, you’re like, wait a minute. They didn’t all make money, nope, just events that happened. Right? So I think it’s important. Not all events have maybe the purpose of fundraising, but that’s where they get listed. There’s also the question of in the accounting department, did that gift of $100,000 that came in five days after the event? Did that go to the capital campaign or did that go to the fundraising event? There’s a lot of mushiness there. So, you know, I’ll blame it on the data, but I wanted to report it because it was something that sort of keep us honest moment.

[00:25:09.32] spk_1:
Yeah. Okay. But it’s valid. You reported as a finding. So

[00:25:13.12] spk_0:
like we. Did you believe it? Believe it. Ok.

[00:27:30.84] spk_1:
It’s time for Tony’s take two. It’s the holidays and it’s the fourth quarter. The holidays. I hope you enjoyed your thanksgiving time with family, friends. Time for yourself. These things are important. You’ve got to take time to rejuvenate yourself to relax however you you know what’s best for you, however you do that for yourself. However you relax. It might be some people, some people relax by, you know, adrenaline rushes and uh, you know, zip cordon however you relax. I hope you did it over thanksgiving and I hope you will continue it throughout the holiday season. We’re in the midst of Hanukkah right now christmas coming up whatever your holiday is. I hope that you will be good to yourself as well as of course your family, loved ones and friends that you’re getting together with and it is the fourth quarter. So there’s a lot of pressure. I know vast amounts of, of fundraising revenue come in, not only the fourth quarter, but even in just in december. I know. So I know you’ve got those pressures. What am I talking about? Balance, balance, take care of yourself so you can take care of your non profit Please do both please for the holiday season this year. That is Tony’s take two. We’ve got boo koo but loads more time for How much to spend on ads in 2022, the older organizations, older organizations spend more then then I’m sorry older organizations spend, uh, less likely to spend. I was thinking of larger. I was thinking of size, not age, older organizations spend less. That that seems to make sense. There are, when we presume that older organizations have have greater, uh market awareness because they’ve been around longer.

[00:27:59.74] spk_0:
I guess the, I love this take away, this was actually, I have to give credit to Kobe on our team who ran the ranch and manage this analysis. He just, he was like, I would love to look at the date funded because the underlying hypothesis here is that older organizations that were incubated and created in a time frankly pre web 12. Oh, would see less value in their operating less value in paying to play in creating ads and creating advertising. Oh, that’s

[00:28:14.34] spk_1:
the hypo that you think that’s the car. They haven’t adapted to our digital ad environment.

[00:28:15.80] spk_0:
I mean it’s, we call the rising generation digital natives. Right? How much time do you spend on Tiktok? Like there’s, I think there’s a fundamental reality.

[00:28:24.88] spk_1:
I’ve never, I’ve never been there. But they can hire people, I don’t need, I can hire somebody to do Tiktok for me to watch you George. That’s so cynical about older organizations. But let’s all right.

[00:28:40.44] spk_0:
You’re right. Let’s, can we, we can tell them the numbers though. It wasn’t that massively off. I will always say that. Um, the,

[00:28:41.84] spk_1:
alright, let’s start with, what’s an older organization. How did you, how did you group or cluster or how did you define age?

[00:28:48.66] spk_0:
How did I unfairly categorize this poor lined group of nickel. They

[00:29:03.74] spk_1:
Are capable of even hiring people to do something post 1950. I mean these these folks are still watching black and white television and some of them are still listening to silent movies in your mind.

[00:29:07.04] spk_0:
Uh, that side, the set with this century. So it’s over under year 2000, which I thought was an interesting inflection point and also it’s nice and round. So

[00:29:17.86] spk_1:
There’s a whole bunch of before 2000. Okay.

[00:30:20.54] spk_0:
Oh yeah, no, no, I think That, I mean it’s a long swath of time to look at and you know, the difference is really that this uh in last century 41% had no ads versus in this century 37%. So you know, 4% points total difference in just the binary decision. Should we have that ads, advertising and promotion? Um and then the median spend of this century was not that much higher, but it’s about 400 ISH, $400 higher uh simply by nothing else controlling for everything else, right? Like nothing else. You just, you’re just going to spend a little bit more if you are going to spend and you’re more likely to be spending on that. So it wasn’t mine, I kind of wish there was a bigger differential. So it’s actually much tighter than, and I have to, I have to say the counter narrative here is that do something dot org, one of my alma mater’s uh what Founded in 1993 and still had a well well above average as fund. So they are like the outlier that I didn’t even have to take that long to find.

[00:30:38.34] spk_1:
It’s impressive that you were the chief technology officer and do something that’s uh that’s a, that’s quite a renowned organization for What, what’s their sweet spot, 14-17 or 14-18 or 14-20 year olds taking, taking action and all the data that you gathered from them. That’s uh that’s quite a, that’s quite a job to have had. It’s impressive.

[00:31:17.44] spk_0:
It was an interesting time to have that role. I’ll say that for sure because there was a lot of, you know, a lot of tech being just sort of introduced web to, oh, just becoming of age, you know, you look at facebook pages and that existing for the first time in that period of time being like, what do we do with this now? I don’t know, what do we do? Like there was a lot of like, have you used this thing, you’re like, oh God, what is this thing now that I have to go figure out and then building our own platform as well and then sort of, you know, data privacy and protection and jumping in? I’d say the biggest piece we figured out was SMS in that period of time, the power of text messaging, which is still, you know, widely under appreciated, I’d say in the social impact sector. I do take, you know, huge nod of the hat to twilio dot org and what they’re doing out there, but it’s a, you know, it was an interesting time to be the cto

[00:31:49.34] spk_1:
and what was the the prime age group for? Do something? I was trying to guess, I was like 14 to 18 or something. Yeah, it’s

[00:32:15.14] spk_0:
extended, you know, I think they would extend it to through college age, but there’s like different sort of calls to action along the way. While I was there, we were predominantly focused on teens and tweens and that sort of intro level to volunteerism and engagement to foster a lifetime of social engagement. And and since then they’ve really evolved because guess what? You know, if you know, if you have half a million people that are of this age, it’s silly to say like, all right, goodbye, Good luck. I’m like, yeah, we can still provide services. We can still provide ways for them to engage. So it’s kind of an interesting quandary. I think for a lot of youth focused organizations to be like, where do you draw that line?

[00:32:53.54] spk_1:
Well, and because eventually those folks are gonna turn over 45 and then they’re just gonna be dinosaurs. Like like you hypothesized about the older organizations so you better get all the data you can now or you know, do something better because once, they once they turned 45, I mean they’re practically dead and they can’t adapt. Their their minds

[00:32:55.86] spk_0:
are impossible.

[00:32:57.15] spk_1:
Yeah. Their brains are neck roast it.

[00:32:59.63] spk_0:
Well actually, no, not completely, but maybe 4% more of them. Like 4% points. four

[00:33:04.59] spk_1:
percent. Alright. Yeah. Good. Thank you for bringing us back to the survey study. So we’re not a servant to the study, definitely

[00:33:11.63] spk_0:
stepped in it. I’m sorry.

[00:33:22.84] spk_1:
So uh let’s talk about what I what I had confused with that larger larger organizations. What did you find out about that? That’s that’s it.

[00:34:56.04] spk_0:
Well, I think the interesting thing is not that hey, surprise larger people with more money, spend more money. This just in from things you probably already knew uh is that it’s the same ratio. So that sort of golden ratio of 5% of revenue just paired out for small and large organizations. My assumption there that I went in there with a sort of hypothesis was that The smaller organizations would probably disproportionately spend at a higher ratio, but it didn’t turn out to be true. So in fact, that sort of 5% of revenue held across large and small organizations, albeit yeah, large organizations spent more. So if you do like if you just take away a quick thing, you could accidentally assume that oh, large organizations got big by spending more. You’re like, well no, they’re larger and they still spend at that ratio, which is in and of itself interesting because it is a much bigger number. And between those two groups where we divided it between large and small, which was over under I should note small organizations being 125 million. So small and 5 to 10 million. So we just basically through the line down the middle and it just equated to the large organizations had a median those three X. In terms of revenue three X. The amount that the 1 to 5 did and that was the same ratio three X more in ad spend. So you know, if you were doing lazy reporting, you say like large organizations spend three times the amount you’re like, Yeah, but that’s just a symptom of numbers. Okay, okay.

[00:35:04.24] spk_1:
And employees, you also, you also tracked the number of full time employees at, at organizations.

[00:35:06.54] spk_0:
Yeah, this was a bear. Would

[00:35:08.42] spk_1:
you find related to? Why is it a bear?

[00:35:17.44] spk_0:
It was a bear. It was just really difficult to do to like segment based on, you know how many employees and they’re simple.

[00:35:18.71] spk_1:
Isn’t there a simple question on the 9 90 I’m not an accountant. So I don’t

[00:36:07.13] spk_0:
know the total number of employees is technically available. So we were able to grab that number and then parse it out. It’s just parsing it between under 55 to 9, 10 to 14 employees, 15 to 19 employees. Uh and sort of scaling that up. The biggest jump happens really from, you know, organizations with less than five employees just aren’t really spending on ads. And the hot take there is that, you know, surprise, it takes people to run the ads running ads and promotions takes dedicated, you know, person or part of a person to truly run. And then as you sort of scale up, you have like random ebbs and flows, but the biggest jump really is that like if you have an organization with under five people, um, they’re really, they’re nowhere near that median ad spend because they come in at like uh 500 bucks versus immediately get to $2000. Um, once you get 5 to 9 and then the next biggest jump, you know, happens once you’re over 24 people, just larger organizations

[00:36:42.33] spk_1:
acquaintance with what goes into this, what why it takes you just said it takes a person or at least a part of a person for organizations that aren’t doing this for the, for the, Well for the 60% that that aren’t spending, what what did they have to devote time to?

[00:36:47.93] spk_0:
It is the most valuable asset of nonprofit has its not the revenue, it’s the time of the people working and to run an ad on facebook to place an ad in the paper. Let’s say if you’re running to place an ad on this very podcast, it takes time for someone to email setup, established the price, manage and test the R. O. I.

[00:37:34.53] spk_1:
It’s not create uh an obstacle where it doesn’t exist. All you have to do is email tony at tony-martignetti dot com. If you want to be a sponsor of nonprofit radio it’s quite a fluid process. George’s referring to George is referring to a university is not that well acquainted with right. This study is not on podcasting advertising When, when he does that one. I hope I will be a part of it. But uh, the, the, I don’t know about the podcasting universe, but if you want to be a sponsor of nonprofit radio it’s a very fluid, easy process. You’re talking directly to the host of the ceo. Just

[00:37:44.47] spk_0:
email you, do it right now. We can do it right now.

[00:38:00.62] spk_1:
Yes, you can do it right now. Whole whale. If whole whale was a sponsor, you would know how fluid and simple it is. But you haven’t taken, you haven’t taken that leap. So all right, let’s not go outside the bounds of the study as we’re, as we’re trying to draw conclusions. So

[00:38:16.22] spk_0:
We take, you know, we sort of take it as sort of maybe a core number that, Oh, you know, we can spend $12,000. A median number, but it doesn’t happen by itself. I mean, nothing simply does. So that that type of ad management was in the back of our mind saying like, you know, you have to track the R. O. Y. You have to create the report for the boss. You have to set up the landing page or whatever it may be that you’re running ads for. It’s not just as simple as saying like, oh cool, no problem. You know, through your credit card over there and you know, let it run. So that’s the thought that it takes labor to implement ads

[00:38:39.72] spk_1:
and your study makes the point that even if you’re hiring someone to do the ads for you. You need someone to oversee the work of the consultant or the firm.

[00:39:07.32] spk_0:
Yeah, I mean the numbers bear out that, you know, it definitely tracks as you go up in a number of employees, um that you’re, you’re able to see a higher spend. Now. That’s also corollary to the amount of revenue. But we also show that along the way, which, you know, incrementally increases but doesn’t necessarily follow along with the differences that you see. So, you know, I don’t purport that this study will show you how to set up your advertising department. But it will tell you that you can’t assume this thing happens in a vacuum and without labor.

[00:40:03.11] spk_1:
Yes. Okay. Well, and you’re a digital uh, advertising agency in at least in part, how much of a full time employee would you do you estimate it would take for? Well, it doesn’t really well for a smaller organization, let’s say they let’s say someone has just 10 employees or fewer like what, how many, what percentage of a full time employees time would that size organization Spending on ads if they were gonna move themselves from the 60% that don’t advertise at all to the 40% that do

[00:40:20.81] spk_0:
an interesting framing there. If you had 10 employees according to a study, you’d have an average revenue of roughly call it 2.3 million your median ad spend for that cluster would be $2100 and You know, oh, that doesn’t require a full time employee. But it’s gonna take about, you know, 2100

[00:40:28.61] spk_1:
That’s in a year. That’s 2100 a year. Yeah. So you’re just spending a little under $200 a month.

[00:40:40.21] spk_0:
I mean, Yeah, I mean and and and 2000. Yeah,

[00:40:42.12] spk_1:
22,000, exactly. 200 a month will be 2400 a year. So you’re saying 2100. So

[00:41:49.21] spk_0:
Yeah. So for that number that’s, that’s easily managed in 10% of somebody’s time depending on the type of Advertising activity that you’re doing. The hope is that that allows you enough to test where there’s value and increase that number, ads can be spent for a number of different reasons. And this is getting back to maybe why my assumption on fundraising was so flawed is ads may be spent to spread awareness of a topic a theme to increase public awareness. I don’t know for vaccines for health research, it’s not necessarily direct 1-1 ratio with fundraising. However, and if you are able and you are trying to turn one ad dollar into two donated dollars and you figure out a way to do that. It’s the hope that spending that and having somebody paying attention to it may figure out a way to get a bit of a little bit of a money engine going for you, spending adds to increase donations to the organization. And that Is a hope of this study. It is a hope that you’re saying, you know what it is worth, frankly 20% of someone’s time to play around with some amount of money that would let us learn and then do more of what works.

[00:42:29.30] spk_1:
Okay. Okay. That’s a pretty good wrap up actually. But I’ll give you a chance to to make it official because we’ve, you’ve covered all your explicit findings in the, in the study which folks can get at whole whale dot com slash advertising very appropriately named simple. Uh, simple U. R. L. Whole whale dot com slash advertising. What would you like to leave folks with George?

[00:42:33.10] spk_0:
I think that point lands hopefully. Well, and I haven’t offended any older organizations out there from which only only older,

[00:42:41.44] spk_1:
only older people. You haven’t offended any organization, not just the older people

[00:42:45.18] spk_0:
For 4% of them. Right.

[00:42:48.60] spk_1:
Well, that was, that was a differential, but it seemed

[00:42:51.50] spk_0:
Like it was 100% of those people were offended before.

[00:42:56.31] spk_1:
No, no, no, no. The way you did it, it was, it was wider.

[00:42:59.55] spk_0:
It was

[00:43:00.19] spk_1:
good

[00:43:00.78] spk_0:
differential. I

[00:43:07.30] spk_1:
understand the differential was 4%. You didn’t offend only 4% of People over 45. Now you offended most of us.

[00:44:35.79] spk_0:
That’s, that’s pretty good. Well, I’ll be joining you shortly so I can, I can join in the offending club. I uh, I will say that there is a lot to, to dig into here, but it is really meant to be an asset for anyone making the case in an organization to say, Hey, why don’t we try this? Why don’t we try to spend? It seems like our counterparts that are in this field are doing so in learning and so if you’re not spending, you’re not learning. And when I look at platforms, social platforms, that purport to be, you know, ways for you to get this organic traffic that will magically come to your site. So if you are posting, for example, on facebook, on instagram, frankly on most social platforms, you are losing money because the platform truly for for non profits for companies. Social media platforms are advertising platforms first and social platforms. Second, one more time. If you are a company, if you are a non profit Social Media is an advertising platform 1st And in an organic social network. 2nd, possibly distant second. Depending on which one we’re talking about. And so it’s like sending somebody in your communications remarketing department to work without a computer by not saying, hey, here’s a bit add money to play with and learn what might be possible for us. So that’s, that’s the thought I want to leave you with. I think.

[00:45:10.89] spk_1:
All right and not just me. All our listeners. Thank you. He’s George wegner He’s the chief. Well, at whole whale, whole whale dot com hotmail dot com slash advertising for the nonprofit advertising benchmark study. If you’re on twitter there at whole whale, thank you, George. Terrific. Thanks for sharing and thanks for being a good sport captain. Ahab. Mhm.

[00:45:11.69] spk_0:
Thanks Tony. Thanks for having us

[00:45:42.49] spk_1:
next week purpose driven marketing. If you missed any part of this week’s show, I beseech you find it at tony-martignetti dot com. We’re sponsored by turn to communications pr and content for nonprofits. Your story is their mission turn hyphen two dot c o. And let me remind you how simple it is to be a nonprofit radio sponsor. Just email, tony up, tony-martignetti dot com. Our creative producer is Claire Meyerhoff

[00:45:57.79] spk_2:
shows social media is by Susan Chavez. Marc Silverman is our web guy and this music is by scott stein, thank you for that. Affirmation Scotty be with me next week for nonprofit radio big nonprofit

[00:46:15.59] spk_1:
ideas for the However, go out and be great.