Nonprofit Nation with Julia Campbell

How to Maximize Fundraising Using AI with Michael Gorriarán

September 21, 2022 Julia Campbell Season 1 Episode 52
Nonprofit Nation with Julia Campbell
How to Maximize Fundraising Using AI with Michael Gorriarán
Show Notes Transcript

As we emerge from COVID, it is easy to anticipate that giving patterns will change, again. Simultaneously, new generations of potential donors are accumulating wealth, and they expect to be engaged as “individuals,” not “donor segments.”

How does a fundraiser navigate these trends?

Artificial Intelligence (A.I.) and Behavioral Economics Modeling can successfully address these challenges. Careful observation of donor behavior through A.I. modeling services can provide more accurate insights into donor sentiment. Today’s professional fundraisers can use these insights to individually optimize giving at the scale of direct response fundraising, enabling nonprofit organizations to know precisely how much to ask of each donor, at each moment in time, over a lifetime of giving.

In this podcast episode, my guest Michael Gorriarán  will provide insights on the imperative for A.I. services in fundraising, including how it works, what to expect, and how to implement it with integrity.

You will learn about:

  • The opportunity for A.I. in fundraising
  • How to implement A.I. in a way that is free of bias, secure, and respects donor privacy
  • Case studies from peer experiences with A.I. in fundraising optimization

Michael Gorriarán is President of Arjuna Solutions, an artificial intelligence services provider serving consumer-facing businesses. The firm applies behavioral economics modeling techniques through its patented A.I. services to ensure businesses optimize pricing and revenue realization.

Connect with Michael:

About Julia Campbell, the host of the Nonprofit Nation podcast:

Named as a top thought leader by Forbes and BizTech Magazine, Julia Campbell (she/hers) is an author, coach, and speaker on a mission to make the digital world a better place.

She wrote her book, Storytelling in the Digital Age: A Guide for Nonprofits, as a roadmap for social change agents who want to build movements using engaging digital storytelling techniques. Her second book, How to Build and Mobilize a Social Media Community for Your Nonprofit, was published in 2020 as a call-to-arms for mission-driven organizations to use the power of social media to build movements.

Julia’s online courses, webinars, and keynote talks have helped hundreds of nonprofits make the shift to digital thinking and how to do effective marketing in the digital age.

Take Julia’s free nonprofit masterclass,  3 Must-Have Elements of Social Media That Converts

Take my free masterclass: 3 Must-Have Elements of Social Media Content that Converts

Julia Campbell  0:00  

The nonprofit Social Media Summit is coming back and we want you there, save the dates November 2, and third 2022. And this year, their summit will be virtual. But that doesn't mean it won't be fabulous. And the best part to participate in all of the live sessions, it's totally and completely free. So get on the early interest list to get notified when registration opens, www dot nonprofit social media summit.com. That's nonprofits social media summit.com. Hello, and welcome to nonprofit Nation. I'm your host, Julia Campbell. And I'm going to sit down with nonprofit industry experts, fundraisers, marketers, and everyone in between to get real and discuss what it takes to build that movement that you've been dreaming of. I created the nonprofit nation podcast to share practical wisdom and strategies to help you confidently Find Your Voice. Definitively grow your audience and effectively build your movement. If you're a nonprofit newbie, or an experienced professional, who's looking to get more visibility, reach more people and create even more impact than you're in the right place. Let's get started.


Julia Campbell  1:25  

Hi, everyone. I'm so excited to be back here with another episode of nonprofit nation. This is your host, Julia Campbell. And today we're going to be talking about AI Artificial Intelligence, specifically how to incorporate it into your annual fundraising strategy. And I'm really excited to have my new friend Michael Gloria, Ron, I'm saying that correctly. Perfect, perfect. He says it in a wonderful Basque Spanish accent. But Michael is president of our Juna solutions, and artificial intelligence services provider serving consumer facing businesses. And the firm applies behavioral economics modeling techniques, through its patented AI services to ensure businesses and nonprofits optimize pricing and revenue realization. So prior to this, Michael was general manager for worldwide commercial market strategy group at Microsoft Corporation. And there, he led the development and execution of worldwide competitive strategy with accountability for market share growth. Prior to this role, he was GM of the national organizations in the US enterprise customer sector business at Microsoft. And then prior to Microsoft, Michael, you spent 18 years with Xerox Corporation, where you were most recently the VP slash GM Industry Solutions organization spanning eight countries in northern and central Europe. So a lot of expertise in the IT and the technology sector. He also holds an MBA from the JL Kellogg, Graduate School of Management, from Northwestern and a BS in marketing from the University of Rhode Island. So he's a North easterner. He's an avid distanced runner, outdoor enthusiast, and I love this I love when people talk about their family and their bio, and active parent with his wife, Chris, and their two children. So welcome, Michael.


Michael Gorriarán  3:38  

Well, I'm delighted to be here. Julia, thank you for the opportunity to speak with you today.


Julia Campbell  3:42  

Yes, fantastic. So I want to talk about your current focus and what you do at Arjuna solutions, and how you really got into providing sort of AI services.


Michael Gorriarán  3:56  

On a personal level. It was a journey that was started by my grandfather, he was very committed to the nonprofit sector. And he was the chairman of the board for the International Institute in Rhode Island. And as I became an executive later in my career in my early 30s, working for Xerox, I had the opportunity to become chairman of the board for the Oregon council for Hispanic advancement. And so I was very interested in the nonprofit sector at a personal level, because I felt like the opportunity to contribute back to the community you're living in was at the core of who we were as a family. And this is multi generational, as I mentioned. And so as I started looking at what to do with my career, when I retired from Microsoft, at the ripe old age of 55, I had the great privilege of meeting the founder of our organization, Adam tracer, who's a professor of Decision Sciences at Johns Hopkins University, and he had looked at out the area of working with algorithms and artificial intelligence instead of big data analytics about a decade ago already. And when he started looking at it, he said, Where can I apply this And he was interested in pricing. And so he applied applied it to pricing, which doesn't necessarily sound like direct response fundraising to me. But what it is, is the opportunity for us to bring sophisticated capabilities into the area of fundraising. And we ended up devoting the entire business just to nonprofit fundraising, and doing pricing optimization for these gift array values. So I'll stop at that. But that was kind of the the way we got here in terms of personal interest, and then the connection with Adam with the technology, and then the focus on nonprofit.


Julia Campbell  5:38  

So what's the opportunity that fundraisers are maybe missing or what's the opportunity that we can see in terms of using this artificial intelligence for fundraising.


Michael Gorriarán  5:52  

So typically, they area of pricing so to speak, and I keep calling this which people find a little bit offensive and nonprofit, but this is an economic definition of pricing is that it is a non differentiated capability. In most organizations, some may have some sophisticated tools, but most use the same tools. They use RFM for segmentation, recency, frequency, and monetary value in determining where the donor sits in terms of which donor segment, and then they look at last gift, or highest previous gift, or an average of three years of the last gifts to determine some type of an anchor amount. And then depending on which donor segment the donor sits in, they'll have some multiple against that base amount, like a 1.25 above that amount, or a 1.5, for the second amount in these gift array values that appear in direct response solicitations. And so we looked at this, we said this is interesting, because what it doesn't do is what the rest of the nonprofit industry has adopted, which is personalization. And what I mean by that is, when you receive a letter at your home, it says, Dear Julian, right, or it's a more personalized salutation, we will help right because it says Dear resident, we know where that ends up right? In the garbage can. And so the personalization, especially like from your alma mater, you see the colors of your school. So it resonates with you, because you recognize those, and then it has the salutation, as I described, it may remind you of your gift in a previous year, it may remind you of the way in which that gift was applied in the in the nonprofit, in this case, your alma mater, like the Presidents fund, for instance, or something of that nature. And all of that then resonates at a very deep level with you, because that is your connection, your affinity. But then when we come to asking you the new amount, some people just say, Oh, the last amount as the first maybe guess, and maybe something above that, as I was describing earlier, or some other formulas I was describing with the RFM. And the multiple. So we do is we take personalization that appears in the salutation and we apply it to pricing. And so we do is we've activated behavioral economics modeling with our proprietary algorithms to measure donor sentiment. And then we take that sentiment and convert it into an appropriate ask amount at that moment in time. And so it does not mean that I always ask you the same amount, it might be that the amounts are a certain range of values in June, which could be different than July or could be different in August. And by doing this without getting into all the mathematics, we're just securing the highest possible gift, not only to secure today's giving, but to retain you as a donor over a lifetime of giving. So it says donor lifetime optimization that the business is founded around.


Julia Campbell  8:40  

So tell me more about donor sentiment, how is that measured?


Michael Gorriarán  8:44  

So we literally can use the existing data that a nonprofit has in place to start modeling out a hypothesis around where we think the donor is in terms of their persona grouping in classic marketing terms, you can measure their affinity to the organization they're contributing to. And within that persona, grouping, the points of elasticity that are related to that particular donor. And then we test those by calculating the gift to raise and then they go out into direct response could be direct mail, it could be email could be online, it could be through telemarketing, or through texting, however, it's going to go out as no problem as long as they're engaging the donor. And then based on the donors response, or lack of response, and the rest of the group that's being solicited from the nonprofit, we can start to use a type of AI that's called reinforcement machine learning to make another estimate on their sentiment and then use the other aspects of our architecture to calculate new values for the next round of gift arrays. And when this is done over and over and over again, through monthly solicitations or quarterly solicitations, whatever the cadence is, you start to develop an asset which is the behavioral model of each individual donor in their relations. shipped to the nonprofit. And that model is only pertinent to that specific nonprofit organization. We don't look at who the donor is we model their behavior, we don't care where they donate elsewhere, or actually who they even are, I'll come to that in the future comment. But we are trying to understand their behavior model and then get the right gift arrays in place. And that usually results in an average uplift in giving by getting the amount correct at the right time, and average uplifting giving of about 12% per year.


Julia Campbell  10:29  

What I think is so interesting about this is that we know that businesses, especially can lead consumer facing businesses do this, I'm just thinking about how I was shopping for a bathing suit, and absolutely follows me first of all, everywhere I go, but they know exactly what to offer me what kind of discount the price point what I'm looking for, I mean, the they have a lot of data on my purchase history, and my behavior. So this is what you're talking about, really, with behavioral economics modeling.


Michael Gorriarán  11:04  

It is like that. So that's a particular form. And with that is internet based and digital marketing based orientation to all of the options you're considering. And it may even have all of the purchases you've made, potentially, depending on what but market information is available in nonprofit fundraising, the difference is with us is we don't care what else you have purchased anywhere else or in fundraising terms where you've made other gifts, because that's irrelevant. Your sentiment to your alma mater, as I was discussing earlier, may be different than the sentiment to your local Little League team, because your friend asked you to make make a donation to the team that her child plays on. Right. So this is kind of common sense, right? That you have a different level of passion, commitment and elasticity as a donor, depending on the circumstances with the nonprofit, in some instances, like wealth screening, which we don't do, and in some instances, like tracing donors and making an average gift, people bank their solutions on those insights, we find those insights largely imprecise in terms of knowing how much Julia will contribute to her alma mater at this moment in time. So we're getting very specific around your demonstrated behavior in the context of one nonprofit at one moment in time. We don't care who you are as a donor, or where else you've made your gifts. And that's our competitive advantage of really getting precise around your sentiment at a moment in time, and getting that extra amount while we retain you as a donor.


Julia Campbell  12:31  

Is this entirely digital? Or do you take use? Do you take into account other gifts that might have been made that were not online?


Michael Gorriarán  12:41  

That's a really great question. We do this through any channel in terms of the outbound solicitation. So it could be email, it could be direct mail, it could be telemarketing texts, etc. On the acceptance of the gift side, we don't differentiate in terms of the channel that the donor makes the gift time because you and I both know that most of the time we're getting something that is a direct mail piece. And that may end up on my desk as a reminder, and then I go online, and I make my digital gift electronically with a credit card. So we've still find it so funny that this is still the case, the best performing channel period is direct mail in terms of getting you to make the gift.


Julia Campbell  13:23  

Well, a lot of my fundraisers out there are gonna love that


Michael Gorriarán  13:26  

Good question. So the reason that we know that is the gift values that we circulate through direct mail is we normally start with direct mail, and then we fold in email and other digital channels as we go along, is the direct mail, solicitation values start to become the gift values that the donor actually provides. So we can see their shift to the amounts. And this theory is called nudging. And there's an author at the University of Chicago called Richard Thaler, who writes about behavioral economics modeling and his article in his book on nudging is what we're doing is we're finding that exact precise point to move the donor to through these constant direct mail solicitations. And then they move to that value while they are retained as a donor. So we don't want to lose donors in the process. And by doing that we secure these extra funds I mentioned only about 12% More in the first year. And most importantly, if I add one other thing our clients are typically experiencing between a 50% return on investment in a 200% return on investment with that 12% growth. So our costs are very nominal and doing the providing the service.


Julia Campbell  14:39  

I love a lot of things about what you just said first is that I know that there are several, several, many of my listeners that will hear artificial intelligence, and they will immediately write it off and say, well, we can't do that. Or Julia is just on her tirade of digital and she's neglecting To mention what you said that direct mail is still has the highest ROI, return on investment. So what I love is that you can use these tools, this technology that's available, this behavioral economics modeling, and incorporate multi channel. It's not just about clicks on a website, it's not just about text giving. It's about every way that donors giving and encompassing all of those different ways, and creating a model on that. And I think that is incredibly powerful, because I would never tell a nonprofit not to use direct mail, if that's something that's working for them.


Michael Gorriarán  15:37  

Exactly. And I think one of the interesting things that we deal with with customers all the time, and the smaller ones are not as challenging as the larger ones we have some very large clients is that sometimes people get myopically focused about the benefit that is derived for their particular function, like I did direct mail. So I can only get credit for my direct mail gifts and this attribution challenge that organizations face because of the scale that they're dealing with. And it's one of the more frustrating things for us, because then we have to go up into the organization further to explain the business case to the CFO, or the CEO or the CEO, is that we have customers that are receiving incredible amounts of gains in digital channels, then the values that the customer is seeing in those digital channels are the ones that were recommended in the direct mail channel. So we always have to, in the beginning of our engagements, get the groups aligned with each other, so they know where the correlated benefits are coming from. And then that helps us secure the funding to help them raise fundraising.


Julia Campbell  16:35  

Right, the events team wants credit for the events, the annual fund wants credit for the Annual Fund, the direct mail, they all want credit for their own solicitations. So a question that I have is what kind of data do we need in place? What do we need to collect to make this work? Because if you're a nonprofit, and you just have like an Excel spreadsheet, is that going to work? Or do you need a more robust data collection system? Like how can we start to collect this data?


Michael Gorriarán  17:06  

So we do serve larger organizations, because reinforcement machine learning tends to benefit those that have the ability to constantly engage the donor? Yeah, and you did a lot of data points. Yeah, we actually don't, it's funny, it's one of those things I was going to mention. One is about what I was talking about there is really the need to have the ability to constantly engage the donor. So you're testing different values, right. So that's what I was alluding to in terms of the volume thing. So if I've got an organization is sending out, you know, once a month solicitation, then I get 12 times to learn during the year, right. So that's, that's really important. Because we're behavioral learning and adaptive technology, we're not a static technology. But what's interesting is that we only need a few actual data points to get going. So number one is I need some way of identifying the donor. So I need ideally, just some, some simple contact ID, and it needs to be unique and persistent. Now, in some cases, people give us the first name, middle name, last name of the organization, assuming the individual donor and the street address, etc, etc, we prefer not to get all of that detail, because we are trying to remove bias from AI. And if I have some correlated element of my guess, on the estimate for the gift amount that is associated with your surname, or associated with your street address, I've already introduced massive bias into the way in which I asked different values from donors, and the ability to just secure them and retain them. And that tends to drive towards a homogeneous database of donors. That is not fully optimized because you have that massive bias in there. So we do is we would prefer not to get PII, just eliminate the PII and give us a unique persistent ID. And let us model that number, if you will, that alphanumeric code and that persistent alphanumeric code. Let me model ABC one, two threes exhibited donor behavior. So I'm really unbiased about knowing how much to ask. So just simple non PII. The second one is I would like to get ABC one, two threes donor history, how large were the gifts? How often were they made? Was it an annual amount? Was it a sustained or amount? Were you giving monthly? All the different things about the values? And when did they respond? What was the nature of the campaign that they responded to? Because I start to get behavioral insights about the psychology of this donor. The last category of information is because a lot of people don't work on spreadsheets any longer, thank God, and they are working in CRM systems, customer relationship management systems, or marketing automation systems, you know, through cloud services. They have a lot of information about their interactions with donors who open the emails who do Downloaded the article that was sent out who's working on the website as a registered user and looking at different materials who responded to the RSVP for the barbecue or the walk. And we find a lot of times people have all kinds of miscellaneous relationship oriented data with their donors. And we look at all of these combinations, the alphanumeric code, their demonstrated behavior and giving and their interactive behavior to develop a model. And then once we get that model rolling, we go through rounds of learning that take place over a nine to 12 month period, to then start perfecting how much to ask each of those individual donors. So this is a very thorough response to a detailed topic. But we don't need lab data points we need if you have more than 25,000 contacts, we need some basic hygiene like I was talking about about, you know, let's identify the person and look at their gifts history. And then I need three to five other data points. So it's a very, very small number of data points.


Julia Campbell  20:57  

So with artificial intelligence, I can already hear some of the objections from some of my clients and students, maybe some of my listeners. And it seems to me that they're missing the point. So I already can hear the objection, where AI just sort of makes everybody a number. And it's very impersonal. But what I love that you wrote on your blog, you wrote that AI presents a distinctive and desirable solution, because it can treat every donor as unique individuals. And the result is a more direct, intimate and relevant giving experience for donors, which leads to higher donor retention, and greater unrestricted operating revenue over the long term. And I think the mindset shift that we need to make here as nonprofits is that this is about the donor. And this is about the direct, intimate and relevant giving experience and making it a better experience for the donor. Is that a challenge that you run into when you're talking with clients?


Michael Gorriarán  22:02  

Yes, and one of the things that we really focus on is we use cloud computing infinitely scalable cloud computing behind our algorithms, to actually take four to eight days to make each individual donor solicitation personalized at the moment of the mail being sent out, or the email being sent out. So the thing we have is our top line headline is we deliver personalization of gift array values at material scale, we have some clients doing 10s, literally 10s of millions of solicitations a month, because that's the nature of the scale of their operations. That's not everybody that's on the extreme. The reason I picked that is because we're then delivering all those gifts to raise at scale in a very highly personalized, intimate calculation for that individual donor. And that's what gives them this extra edge in fundraising. It's almost taking the psychology of the donor and being able to express it in the form of a gift or a value. That's correct.


Julia Campbell  23:05  

I want to talk about more about how to create AI that is ethical and bias free. And I know you have a white paper all about that. Because I do think that's a huge, that's a huge obstacle that a lot of nonprofits will come into when making the business case for AI and fundraising. So can you talk more about how, first of all we can make the business case for it? What do we need to consider around ethical advice, free AI and fundraising?


Michael Gorriarán  23:36  

So I think that the the first reactions, and I've been working in artificial intelligence for about a decade now, but for years with our junior solutions, the ethical thing was the first thing that I ran into, because people's perception at the chief development officer level was that if I'm using AI, to know how much to get as a gift from a person, that I'm manipulating them somehow, and versus actually finding them. So this is a really important insight, is it we are not manipulating somebody with AI, we're trying to precisely find their relationship where they are financially in relation to the nonprofit at that moment in time. So I look at it as the advantage of AI, not an ethical or unethical use of AI is the ethical use to find them where they are expressed a precise and well reasoned value that sustains them as a donor. So if you get over the ethical aspect of it, which I think we're very good straights in that respect, and we've been doing this now for many years, then it's the question of, are you doing this in a way where you are not compromising the donor by manipulating them through PII like I mentioned earlier, right? And if you use PII as a way of biasing your solicitation amounts, then you are corrupting the process in another manner.


Julia Campbell  24:56  

PII personally identifiable information, just in case someone missed it.


Michael Gorriarán  24:59  

So, yes, thank you. I appreciate that, right. So if you're using someone's surname, and you can infer something from that about how much to ask them, you just corrupted and minimized your, your fundraising or you don't you don't optimize it across the entire group. Same thing with someone's address, if you assume wealth or not, or lack of wealth, because of their location, for instance, that can be very misleading, misguided, and ineffective as you can just inherently imagine, right. So we try to make sure those two factors are nullified. And as we developed our solution, we want it to work without personally identifiable information, they wanted to develop a solution that sustains the donor over a lifetime of giving. So that automatically eliminates the unethical use of AI if you're trying to sustain the relationship. So we


Julia Campbell  25:49  

know that today's fundraising environment is fundamentally changed, because of the global pandemic and the state of unrest and uncertain times, whatever you want to call it unprecedented times. So why is right now the right time to optimize this kind of technology.


Michael Gorriarán  26:12  

So there's two sides to that equation, and one is the nonprofit's operating side. And the other one is the donor behavior. So traditionally, as you've been inferring from my comments we've always focused on lift is increasing donations and giving a compelling return on investment. What we are observing across the portfolio for all of this current year 2022 Is that we find that donors below $75, in annual giving are quite inelastic. And it's almost intuitive. We just proved that out with all of our solicitations. So it's a statistically accurate observation, not just conjecture. And we believe as you would imagine that as the economy came back to life, after the COVID started, subsiding, and vaccinations became available, there was just competition for your discretionary income. And so you traveled, you went to restaurants, etc, etc. And then your ability to make the same level of donations both in scope and scale, across the different nonprofits you cared about was inhibited based on your spending money on different things that didn't occur during COVID. The second thing is that we are all being subjected now to $6 Gas gallon per get gallon of gasoline price, right. And so all the inflation that's out there in the system right now is really a tangible, having a tangible in impact on my discretionary spending, as well. So between inflation, and the circumstances around changing your spending habits, we're seeing that the $75 and below donations are quite inelastic this year. So we had developed this separate AI tool that we have had now for a couple of years, we just were not pushing it with our clients to take a look at it. So we didn't know if the moment was right. So the first tool about creating lift is exact ask, I love this branding, by the way, exact asked right? How much to ask of this donor at this moment in time to optimize their giving. So if you know in today's environment, you're getting instead of 10%, lift, 12% lift, maybe you're getting 5% lift, and you can see that then the second algorithm exact donor, it says to you, if I've calculated Julia's amount at this moment in time, should I mail her or not today is a gift going to come as a high probability event from this direct mail piece. And what we are seeing when we run that second algorithm exact donor, we take the exact tasks as input, and then we run the exact donor to determine that nail decision in a very precise way. Our clients are able to reduce their male volume by 10% to 20%. No problem we did one yesterday with a reduction for the month, it's a monthly decision is not a blanket decision. The average number of pieces was going down 30% at about $1 a clip per piece of mail, that is a big expense reduction. And this gets to the operating side of the nonprofits. The nonprofits are facing inflation as well. That paper is up there envelope cost is up there postage is up the labor costs are up the average received from our clients is about a 22% increase. So we can help them not blanket cut, you know, some blunt instrument 10% of their mail volume, but knowing whom to solicit at this point in time, and then not solicit the ones who don't have a high propensity of making a gift. We think we can cut operating costs 20 to 30% Based on the proposals that we have been working on over the last three or four months now.


Julia Campbell  29:41  

So yes, I talked with Beth Kanter and Alison Fein earlier this year. I've talked to them a couple of times they wrote a book The Smart nonprofit. It's all about how to use technology to cut operating costs. And to make things more efficient and more effective. It's not about replacing humans. Not about replacing people. It's not about replacing that coffee with the major donor or the gala or the golf tournament. It's about making these kinds of things more intuitive, and more profitable and just more efficient because someone could literally sit there and run the data and crunch the numbers on every donor in the database. But that's definitely not the best use of the development directors time. So I think that's, I think that's a great point. And how long does it take usually to kind of see results if you're running our Joonas technology if you're running this kind of AI,


Michael Gorriarán  30:37  

so the first one in the lift the exact task and by the way, we love Beth's work just for the record, great stuff. Really great stuff. Yeah, it's phenomenal. But getting to your question, exact task takes us anywhere from nine to 12 months to get to full optimization on lift, we need three learning cycles. And we deliver this I should have mentioned this, we don't provide the technology to anyone. We decided several years ago after trying to provide the technology that it was better delivered as a simple service and our customers like that it takes us about two weeks to get them up and running. We do all the work in terms of the analytics and providing the gift array values, and we put it into their fundraising processes. So it's in their system. So very, very simple to adopt. And that's been overwhelmingly positive for us. So exact ask, as I mentioned, to get the algorithm to full flight is three learning cycles. It's about nine to 12 months to get full flight, you get returned during that time, but you get optimization after nine to 12 months, and then it continues there on on the exact donor side. It's a different equation, because then you're determining simply whom do I mail this month, and it only takes about three to four months to show that we're just picking the right people to get the gifts from once you get it in full flight.


Julia Campbell  31:47  

Fantastic. So I know a lot of people are going to be interested in learning more. Where can people go to learn about you read your blog, I know you've got a lot of white papers on your website and learn more about the work you do.


Michael Gorriarán  32:01  

So Arjuna Solutions .com A R J U N A solutions.com and Michael Gorriarán is MCG.arjunasolution.com, you can contact me via email, take a look at the website, we'd be delighted to provide a briefing.


Julia Campbell  32:16  

Fantastic. Thank you so much for being here. Michael, this is really interesting. I learned a lot. And I'm always excited to present new opportunities and new technologies and ways to work more efficiently and effectively to my listeners. So thank you again for being on


Michael Gorriarán  32:34  

Julia, it's been our pleasure. It's a pleasure having the opportunity to participate in the podcast today.


Julia Campbell  32:46  

Well, hey there, I wanted to say thank you for tuning into my show, and for listening all the way to the end. If you really enjoyed today's conversation, make sure to subscribe to the show in your favorite podcast app, and you'll get new episodes downloaded as soon as they come out. I would love if you left me a rating or review because this tells other people that my podcast is worth listening to. And then me and my guests can reach even more earbuds and create even more impact. So that's pretty much it. I'll be back soon with a brand new episode. But until then, you can find me on Instagram at Julia Campbell seven, seven. Keep changing the world you nonprofit unicorn


Transcribed by https://otter.ai