TRANSCRIPT

[00:00:00] Aubrey Bergauer: Hey everyone, it's Aubrey Bergauer here. This is a bonus episode I am super happy to be able to bring to you. That is because we are talking about A topic that is near and dear to my heart today, which is data, specifically, though, the problem with data, the problem with your patron base, CRM, data management that I see pretty much everywhere in arts management.

And as we get into this, before we can talk about data and the problem I've come to see, you have to understand how I got there. So I'm going to share a bit of a history of my own journey learning about. patron retention. So this goes all the way back to my very first job. I worked in development at the Seattle Symphony and right away, my first job out of college, I noticed two main things.

One is that the organization was not Great. With their own data. This is not throwing shade or throwing them under the bus. Uh, it's happened so many [00:01:00] places. And to give an example, my predecessor was literally keeping a separate database of all the donors and the information that was tracked. I mean, she had their giving levels, all these kinds of things in a separate database because she didn't trust the main database, which was test.

Torah. The second thing I noticed is that development and marketing were not talking to each other very much. There was very little coordination on efforts between the two departments, and at best, that's just that, lack of coordination. Messages getting jumbled or overlapped for the, for the patron that is.

And at worst though, it was really costing them revenue. My next job after that was in marketing. I was brought over to the Seattle Opera to be their audience development manager. I was there for about six years, so several more learnings here. The first is part of my job was to manage the young patrons club.

It's called the Bravo Club. We [00:02:00] grew that to the largest young professionals group of its kind in the country at the time. Opera companies from all over the country, my counterparts all over the country, were calling, asking, how did we do it? How did we see such growth? So much learning just in this alone.

So to set it up, The Bravo Club had a discount model. Maybe other young patrons groups have this kind of model too, where when you first join your first year in the club, you pay your membership fee and then you get 50 percent off, but in that single tickets or subscriptions, but then your second year, that discount goes to 40 percent off.

Your third year, it goes down to 30 percent off and on and on. So basically we're kind of weaning you off the discount over the years as you continue to be a member. As I was in the job, I started looking at the data and saw that a lot of people would join for the first year, the biggest discount, and then not renew.

On the surface, I think this makes a lot of sense. Take your deal and then don't come back. But I realized if my job was to grow this organization, [00:03:00] really the best analogy I could think of was that this is a leaky bucket. I realized that people were coming in, we're losing them, we're not keeping them, and therefore retention was going to be key.

If we were going to grow, we were going to have to plug the holes so that we were losing fewer people. In other words, keeping more people. And there's a whole bunch of things we did to help with that, that really became the foundation for how I think about audience retention today. A whole other separate conversation, but working with that board, even a junior board, really, and engaging them and activating them was really a big key to the success there too.

Simultaneous to all that, still same job, same organization, the Seattle Opera received. a Wallace Foundation grant. Now, if you know anything about Wallace Foundation grants, that's a big windfall of money to the tune of, at the time, 750, 000 over four years to fund all kinds of audience development work.

We did all kinds of projects for that grant. Lots of data and analysis work along the way that the [00:04:00] grant provided funding for. Just so much learning in all of that work as well, of course. What I noticed though is that one off projects were cool, even more cool when they were totally funded, but not very effective in the long run.

Like I was literally the person in the office pulling a bunch of the data on this and I could see it. Not effective in the long run at retaining and growing those audiences. So, on one hand, we had one group, the Young Patrons, Young Professionals group, where we were growing and had all of these other experiences on the other hands to quote unquote grow audiences per the grant award that actually weren't really working to grow.

Or we would see a spike in audiences because of the initiative and the money we were putting toward it, and then we would go back to the baseline after that. So all of these things were happening in parallel. Along the way, sort of also simultaneous to all of that, I started discovering research from all kinds of sources that was [00:05:00] backing up what I was seeing at my own organization at the time, but on a larger scale.

And so that's research from places like TRG Arts, the League of American Orchestras, Patron Growth Initiative, the CHURN Study, Opera America Research, SMU Data Arts started producing research somewhere around that time, or at least I came across. That's their research somewhere around that time. Arts journals.

Anybody read or used to read arts journal? It's been around for so long and I used to read every article I could every day in arts journal to try to understand the arts landscape. And when you put all these things together, all these different sources of information together, you start seeing patterns.

Some of the stats that are still largely true to this day are that of first time attendees, up to 90 percent don't come back. That's from the CHURN study I mentioned. 50 percent of new subscribers and new members, if you're at a museum, do not renew. That comes from a study Oliver Wyman did with the [00:06:00] League of American Orchestras.

Also on the museum side, Impacts Experience reports about this data. For fundraising, about 80 percent of first year donors. Don't give again. Don't renew the gift. That's from the Association of Fundraising Professionals. All of these types of stats and data have just stuck with me. Wallace, just to put another data point on this, just earlier this year, confirmed what I noticed back, maybe 2012, I would say, They confirmed this, and they basically said, Wallace Foundation, this year, that one off initiatives don't work, that focusing on demographic segmentation had virtually no bearing on long term results.

We all say we want younger audiences. They say focusing on demographic segmentation doesn't necessarily work. We say we want diverse audiences. It's not to say we can't achieve younger and more diverse audiences. It's saying the way they went about it. was not having a bearing on long term results. All of this is from there.

If you need to look up the article, it's called Results from the Building Audiences for Sustainability [00:07:00] Initiative. Their report summarizing all of this. Just to give one quote here, they said, unexamined and unfounded assumptions often hindered organizations ability to connect with audiences they hoped to reach.

Using data to Helped organizations uncover these assumptions. All of this was pointing to the same thing in my mind, a leaky bucket, a leaky bucket in our audiences, whether it was the young patrons group, whether it was the audiences on the whole, whether it was our donor base. And to solve it, we needed to get better at accessing and using our own data.

Everyone in the field has been so preoccupied with new audiences for as long as I have been in this business, which now is going on 20 years. If you keep filling a leaky bucket with water, what happens? it continues to leak. So are you tracking with me? The answer is not more new audiences. That's the equivalent of just adding more water to a leaky bucket.

The answer is plug the holes. And that is what audience retention [00:08:00] is all about. How do we keep more people with us for longer? How do we keep more water in the bucket? Knowing your data helps you do that. It helps you identify the holes where they are, then you can work to plug them. What did I do with all this?

Around 2012, I went on to become the marketing director at the Bombershoot Music and Arts Festival. So my first marketing director job. I said I'm going to apply all of this to our work here, and now we're talking about a festival model. We would say things or strategize for things like if somebody bought a one day pass last year, how do we get them to do that again and come back this year?

If they've come a few years in a row, how can we get them to buy a three day pass this time instead of a one day pass? And we saw success with this, really focusing on retention and when is it time to invite them to the next level of engagement? I was there for three festival cycles, so just around three years, and we saw through focusing on retention, plugging those holes, a 20 percent increase in attendance.[00:09:00] 

44 percent increase in earned revenue and the largest net profit in the festival company history at the time. Side note, this was still a non profit organization. All that to say, this strategy worked even in the context of a festival. 2014, I get my first executive director job, moved to California to go run the orchestra.

All they kept saying during recruitment was how much attrition there was, both on the ticket sales subscription side of things, as well as with donors. So does this sound familiar? Do you talk about attrition at your organization? Let me tell you, attrition is another way to say leaky bucket, right? Losing people.

In 2014, I put together a plan of everything I learned so far, the combination of all those sources for the research, the data I had seen in my own work at a large organization, at a mid sized festival, the case studies of organizations published by all those sources over the years, all the articles I had read, and I made what I now call the long haul model.

Very quickly is [00:10:00] just a systematic patron journey, customer journey. that focuses on retention at every audience segment. How do we retain more first timers and get them to come back? That means they become multibuyers. Once somebody subscribes, how do we get them to renew that subscription? How do we get them to renew again?

Then when do we ask them for a donation? How do we get them to renew that gift? So all about that. Plugging the holes of the leaky bucket by the end of the first year as chief executive of the orchestra, we were already seeing results after we implemented what I now call the long haul model, this plan we saw after year one, 14 percent growth in ticket sales and a balanced budget for that organization for the first time in at least 10 years, it might have even been closer to 15 years and I call it the long haul model because The idea is having a plan for every step of the patron journey.

As I said, you don't go from newcomer to donor in one year. Just most people don't. Instead, it's really about building [00:11:00] relationships and building that revenue. over time. But you just heard me say we started making pretty good money from this right away in year one. So we kept it going, we kept working on this plan, getting better and better at plugging those holes.

Fast forward three years, that brings us to 2017, and now at that point we had had incredible growth. Audiences at that point were up 70%. Subscription revenue up 71%. Earned revenue total of 145%. And contributed revenue up 41%. So all of those numbers just continued to grow and grow and grow. By the time I left a few years later, they were even higher.

But in 2017, that's when I published the article about the long haul model, called it that publicly for the first time. That article took off. It became my most read blog. article of all time. It still is to this day. So I'll link to it in the show notes for anybody who wants it. The side note is that all of that Wallace Foundation conclusion I mentioned that was [00:12:00] saying focusing on demographic segmentation doesn't work.

Well, with the long haul model, we saw our average audience age getting younger. Why? Because it turns out, and this is likely true for your organization too, We had younger people coming, they were coming all along, they just weren't coming back. So the bucket was leaky, they were falling right out of that leaky bucket, whereas the long timers who were not leaking out kept getting older and older.

When we got better at plugging the holes, working on retention, that is to say, guess what? More people of all ages were not just coming, but they were staying with us, including younger folks. And that's when the averages in terms of age, the average age, started to drop. I knew I was on to something at that point.

I had had years of experience at all these different organizations seeing success with this kind of work in one form or another. By 2019, I had the long haul model trademarked and that was the next step for formalizing it, [00:13:00] professionalizing it. And in the early days of my business launching, that was a big part of the work I was doing with clients.

It still is. It's now what I teach in my run at like a business academy. A whole module is on the long haul model and how do you implement it exactly. And today here, now the point of this whole episode is to share more about really the next step in this model and being able to apply it. To bring us up to speed, last year, a company called Artalyze got in touch with me.

They were working on a way for arts organizations to better visualize their own patron retention data. They were like, are you interested? I was like, yes. Better visualization of your patron retention data? 100%? 1000%? Yes. So I said I was interested because through all those years, no matter if I was at a big organization on the biggest CRM or a regional orchestra, or now with all of these types of organizations I work with, and then some, always a big challenge has been, especially in terms of this patron retention leaky bucket work, [00:14:00] always a big challenge has been.

Managing and using the organization's own data. So, for example, the only way to know what your first time buyer retention rate is is to pull a report from the CRM. How many first timers came back over the last 12 months? And how many did not, right? That's how you determine your retention rate. How many first year subscribers renewed and how many did not?

First year donors, right? For an organization to be successful at fixing their leaky bucket, they had to be really good at understanding where the leaks were. As I've been saying, the only way to do this is to understand how to harness your data. It has really been a struggle for many organizations to get on top of that.

And particularly to get on top of that for every segment. Sometimes organizations know their subscriber data or their donor data, but maybe not the other segments or something like that. Some don't have much of a handle on most of it. It varies. But even for the organizations that were the most on top of their data, then the challenge became how time [00:15:00] consuming that can be.

And I know because I've been there, I have lived it, that stuff can take up so much time when you're trying to be on top of all the different segments and all the different data points. It's a challenge. So when Artalyze reached out, they said they were developing a product so that arts organizations didn't have to run all those reports, but instead just upload their data, like literally a spreadsheet, and then Artalyze's software would handle the sorting and the number crunching and everything.

in one place. One place. Like, not eight different reports. One place. This is what they were telling me. They said this would be in the form of a dashboard that you could then drill down into to get the specifics that you need. So the dashboard gives you the stats on the leaky bucket. Plus, you get to see when you're plugging the holes.

In other words, when you are retaining more people well at every step of the patron journey, every one of these key audience segments. They brought me in to help provide a little insight. They were developing the dashboard, making it work specifically for people and [00:16:00] organizations who are implementing my long haul model.

And now I'm here to tell you that service just launched publicly a few weeks ago. So if you are listening in real time, this is July 2024 when this episode drops and we've just recently announced it's available. So what I want to do for the rest of this episode is share more about that process with you and what they are doing to help you fix that leaky bucket.

I have to be honest, I am not technical. I'm very data driven but not technical. Honestly, even as we were working on this and rolling this out, I had questions on exactly how this is working on the back end. I brought in the founders of Artelize to give us some answers. Welcome to this special bonus episode of the Offstage Mike.

We are talking all things data so we can get better at fixing that darn leaky bucket of our patrons. I'm Aubrey Bergauer and welcome to my podcast. I'm known in the arts world for being customer [00:17:00] centric, data obsessed, and for growing revenue. The arts are my vehicle to make the change I want to see in this world, like creating places of belonging, pursuing gender and racial equality, developing high performing teams and leaders, and leveraging technology to elevate our work.

I've been called the Steve Jobs of classical music and the Sheryl Sandberg of the symphony. I've held Offstage roles managing millions of dollars in revenue at major institutions and as chief executive of an orchestra where we doubled the size of the audience and nearly quadrupled the donor base. And now I'm here to help you achieve that same kind of success.

In this podcast, we are sorting through the data inside and outside the arts, applying those findings to our work, leading out with our values. And bringing in some expert voices along the way. All to build the vibrant future we know is possible for our institutions and for ourselves as Offstage administrators and leaders.

This podcast is about optimizing the business around the art, not sacrificing it. [00:18:00] You're listening to the Offstage Mike.

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Yes, you heard that right arriving. I love that. The State of the Arts report shares exclusive data from the top class registration software company, [00:19:00] core Storm. I got to see trends discovered from core storm's analysis of more than 1 million class registrations. And you'll wanna see them too. So if you're ready to elevate your education program, don't miss out on the State of the Arts Report.

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[00:19:58] Aubrey Bergauer: I am so happy to introduce you [00:20:00] to the founders of Artelize. First up, we have Suni Gerald, founder and CEO. Suni started his career as a freelance opera singer with an international career.

He self describes as a serial entrepreneur. He was the founder of Trulink, which was the opera talent marketplace, if anybody remembers that. And if you don't remember Trulinked, maybe you remember Operabase, which Trulinked later acquired. Today, Sunia is the founder and CEO of Artalyze, where they're on a mission to have the world of the performing arts in one location, and we're going to hear more about what that means when we talk to him.

Also joining us is Jan Pilgaard Karlsson, which I have to say, these are all Danish names by the way, so I am trying to do them justice as best I can. He is the co founder and chief product officer at Artelize. He has a master of musicology from the University of Copenhagen and a degree in business administration from the Copenhagen Business School.

And you'll hear me say in the interview, I didn't even know he was a musicologist by training because I know him [00:21:00] now so much as the tech wizard that I was happy to hear that both. Jan and Suni have musical backgrounds. He spent 10 years in the IT industry before joining Truelink as chief operating officer, and that's where he met Suni.

Both Suni and Jan live and work in Copenhagen, and Artelize, you should know, recently received a 1 million euro pre seed round of funding. That is a huge vote of confidence by venture capitalist investors, and they're just getting started. Suni and Jan, welcome! 

[00:21:32] Jan: Thank you, Aubrey. Amazing that you have us.

Thank you very much for having us. 

[00:21:36] Aubrey Bergauer: I'm so excited because I want to jump in with a topic I love very much, which is you and your website talk about becoming an audience growth hacker. And I love this idea. I love calling it that. I'm here in Silicon Valley. There's a lot of hackathons happening all over the place, but you guys are the first I've ever heard to say audience growth hacker.

And so immediately, like for me, it really [00:22:00] perked my interest. Can you first just explain what that means exactly? 

[00:22:05] Sune: Yeah, absolutely. We've been in this tech industry for many years, and as you told, it's something completely normal term within the tech area, but we really felt that in this underserved world of performing art, we need to sort of introduce this principle into of audience technology quite simply because, you know, as you also agitate a lot for being data driven and, uh, we think there's no other way to really know if your experiments are working, if what you are sending out there in the world has a response, positive response, or maybe a negative response.

And therefore you should not continue to do that. And the only way to run business, I think, and audience engagement in a serious way is to be data driven. 

[00:22:49] Jan: And my favorite example is the McDonald's example, but no McDonald's once decided to test a spaghetti bolognese. Normally when they introduced a new dish, it would cost some like [00:23:00] 20, 30 million dollars of recite development, uh, marketing development and take a year, a year or two years.

So, uh, they decided let's do it this way instead. Uh, I don't actually know why they decided that. They put up. At 10 McDonald's restaurants, they put up a sign. Would you like to try spaghetti bolognese at the counter? When people asked, they said, sorry, we ran out today. Would you like a burger? So they got confirmation if this was worth doing or not in sort of a 100 investment and in 10 days, instead of a 20 million investment.

That's growth hacking one on one or in a boiled down to a little dice. And that's what we want to do for the performing arts, you know. They should be able to do that McDonald's sign without going crazy in IT projects. 

[00:23:43] Aubrey Bergauer: Yeah, I really love this because so often, you can tell me if you've seen this, so many organizations want to go out with a big idea or we want to roll out this new opera production, new concert series, new whatever.

And it's like, these are very expensive, quote unquote, experiments to try. And instead, [00:24:00] I'm way more a proponent of, no, no, what's the way you can pilot test this? How do you serve Bolognese at the counter to follow this analogy? I really love this idea of just, yeah, small experiments, big time. That's what you all help facilitate.

Really, through one of your, I guess, first products. Is that accurate to say? These little experiments, is that true? One of your first products? 

[00:24:21] Sune: Well, I would say it's, it's one of our now many products. The ArtLize platform in itself is driving an audience towards the arts organization because we are aggregating data, as you know, using AI technology to simply create a platform where you can find everything about performing arts in one location.

And that product, of course, needs to be supported. by a retention mechanism. It's not enough just to get them there the first time. You need to be able to communicate with them and make them come back. And to make them come back, you actually got to know what they're doing. And therefore you can say segments.

So in our, audience engagement sort of [00:25:00] portfolio. One of the most important things, and therefore you can call it our first product, in a way, that is to make sure you can monitor your audience movements. The analytics tools that we bring is actually an audience analytics tool. And those segments, then you can play with them and you can see if your experiments are actually working on the groups that you are targeting on.

It could be a group of people who are about to turn into subscribers, or maybe even donors, but In this way, you can recognize which target groups you should engage with and in the right language, so to speak, or in the right fashion. So you are not just sending the same message to everybody, hoping for the best.

[00:25:39] Aubrey Bergauer: Oh, speaking my language, not sending the same message to everybody and hoping for the best. Oh my gosh. This is why we get along. So can you give, I want to, I want to poke on this though. Can you give an example or two, like what's a super cool experiment for lack of a better word, like an experiment that you've seen on the platform that was just really interesting.

So talk about that, kind of give us the setup [00:26:00] and then the follow up is going to be like, how does somebody use your software to do that? So kind of part one and part two of this question. 

[00:26:06] Jan: One of my absolute favorite cases, it's a mid sized symphony orchestra in the U. S. I think we're discussing that maybe.

We are actually a U. S. company. You know, we have 5, 000 symphony orchestras on the platform currently. They engaged with our analytics, dropped their data. We come back to that, I think, how that actually happens. But the system generated a suggestion because it looked at the data and figured out that they have a four series subscription and it's a small symphony orchestra.

The data showed that many engaged with only the three of the four events. They actually did not want the classical events. They wanted the Christmas concert, the pops concert, the Celtic celebration, or whatever St. Patrick's day you have, uh, stuff like that in that segment of, uh, let's say entertainment.

So the system identified a group of people, offer them a two to three series, a subscription instead of only take all or nothing. And that work wonders, you know, they were [00:27:00] able to get people on the subscription that would all. Would never have done it before. And you could say, it's not rocket science.

They could have sat down with some spreadsheets and done the exercise themselves, but actually doing that, that's super hard, you know? So that's one of my favorite exams. It's actually a standard method, Diceversify your subscription offering. And people have heard about it before, but here were the data to drive that decision and here were the experiment system to see if it worked.

That's one of my favorite examples. Actually, plain vanilla, standard stuff, not rocket science, but you needed someone to prompt you to start doing it. 

[00:27:32] Aubrey Bergauer: I think that's important. I think everything you're saying. Okay. So let me just make sure I understand those. So this is not saying everybody did not want the classical concert.

It was just a subset of people, right? Just so everybody doesn't get really freaked out that people didn't want the classical concert. 

[00:27:48] Jan: No. They shouldn't be freaked out. This was increasing the revenues. It was increasing the locked in revenue early season from the subscriptions. It was not detracting from any revenue at all.

[00:28:00] And exactly like soon, it just said that there might actually be a customer seeing you in two different ways. They might actually see this and that symphony orchestra as a totally different than their neighbor, and that's fine. You don't have them have the same impression of you as long as they love you and come back.

And our system makes it possible to work with more segments so it doesn't become overpowering for a, as you always talk about, a already lean organization. 

[00:28:24] Aubrey Bergauer: Oh, this is so great. Okay, so then if somebody was using your software, then like in this example of this experiment, like what's happening, like help us understand, like the platform is prompting the user, the client organization and saying like, here's a segment we think we might want to try this three performance offer instead of the whole four concert series.

Is that really what the prompt that the client's given? 

[00:28:45] Jan: Exactly. It identifies, for instance, it, in this case, it had, there are multi ticket buyers here, but in a sub segment of those, there are seven, it was concretely 75 people who consistently never buy the classical, but consistently come to the orders, even buys [00:29:00] many tickets.

There were people who bought 14 tickets. Why not get them on a subscription? You know, some of these guys then all of a sudden bought 50, you know, 50 tickets, maybe 14 subscriptions from one person. That you locked those thousands of revenue dollars early on. So the system said, there are these people, then the orchestra started the experiment and then they uploaded new data to the system.

And then it said 19 percent converted, then they got very factual data on how this worked. And then they can try again next year, you know, and see, can we get that to 60 percent instead of 19, now we already know a lot about it. That's in our system. That's the next step of our development. When it sees successful experiments, it will suggest how you can evolve and do even smarter things.

And you can also do your own experiments. We're not locking it down, but we have suggestions generated by AI already coming up. 

[00:29:47] Aubrey Bergauer: Okay, people can't see me right now, but I feel like I'm that star eyed emoji. Like, I'm like, Oh my gosh, this is so good. Now my gears are really spinning because multibuyers, I think, are sometimes an enigma.

And like, this kind of experiment [00:30:00] is so helpful to illuminate like, Oh, as you just said, this segment, they've been buying multiple single tickets, and yet the organization had not been able to convert them to a subscription for whatever reason. And now the software is saying, try this instead. And then lo and behold, there was a 19 percent take rate on that.

So that's really incredible. This is a good segue. I want us to talk about this new dashboard that just launched and the part that we worked on together before I dive into the dashboard itself. And how are we tracking multibuyers and all these different segments? I want to first talk about kind of the history behind it.

back way up. Tell us your story, really. When did you get started? When you were first thinking about founding Artalyze, like what problem or problems were you setting out to solve that you were seeing in the performing arts? So start there, start at the beginning. What were the problems? What's kind of the origin story?

[00:30:51] Sune: Yeah, so we both come from a background of a previous, started back in 2011, Ken and I, there was something called True Link that was the company that [00:31:00] acquired Operabase that is perhaps more well known. But that that taught us the power of having data and having a lot of data in one sector. The thing when you have a lot of information in one place is that you get a lot of attention both from the audience but also from the professionals.

And in 2020, after some years in tooling and uppercase, where we sort of felt maybe this was a little bit too narrow, and we felt we needed to do more for the audience. There was a lot of things going on, especially then when the pandeMic came, it became 100% evidence that we need to do something for the audience, do something to make sure that the professionals had also other means of getting revenue.

Not just, you know, because everything closed down. The artists all of a sudden lost all their revenue. The arts organizations had to close down. We need to find a way to be visible in the market and also be able to approach our fans in new ways and make sure [00:32:00] that we have many channels of revenue. So the whole idea to start with was about creating a marketplace where we could have a closer connection to the fans, find a new audience, make sure the whole industry was super visible and accessible.

And then of course, it developed into this, getting the audience back and let's say, develop a new audience. audience, a more young and diverse audience. That's the background for Artslice, which is, of course, not just for opera, you know, this is for the whole performing arts space. 

[00:32:31] Jan: And you know, what we often hear, the fayetist fate has said that performing arts lovers are getting gray haired and it's all over.

It's all over. Yes. But you know, we have data showing there are audiences sitting around the corner from a symphony hall that didn't know that the artists they love were on tonight. They literally live across from it, but because they did not look out the window and see the banner and the building that Lang Lang was coming, they missed out on it.

No, that happens all the time. [00:33:00] There are tons of audiences out there. You just need to get them. And there are so, and people are spending more and more on performing arts after the pandeMic. It's not going down, it's going up. So it's just about taking it seriously and not being a pessimistic about your future.

And there's a lot to do. So we set out to. Let's make that platform where all those optimists can work from and where even the pessimists can see, oh, oh my God, new audiences are coming automatically from idolize. Let me take a look at it. 

[00:33:27] Aubrey Bergauer: Oh, wow. This is really great. A place for the optimist and the pessimist.

So just so everybody listening is clear, there's basically multiple entry points to your product because you have this public consumer facing. part of your platform that you just described. People can go buy tickets, shop around, that kind of thing. And then you also have this part that we were talking about before that's organization facing and organizations, that's where they can run the experiments and put their data.

So I just wanted to be clear that both direct to consumer and then B2B, right? Am I capturing this correctly? 

[00:33:57] Sune: Yeah. And may I add from the very [00:34:00] beginning, we saw that there's a huge power in and we believe that that power. is actually what can give the world a new renaissance. A renaissance that is not powered by the kings and princes like the last one, but by the people for the people.

Because if we connect artists, arts organizations with the fan directly, then the transaction will also happen directly. And that basically leads to a new renaissance of the performing arts. And we actually believe that is going to happen. 

[00:34:29] Aubrey Bergauer: Say, when I first learned that you all had this portal for merch, at first I was kind of scratching my head because I was like, Oh, it's not that big of a revenue stream.

But then I heard you say what you just said in another session or conversation we had. And I was like, right. It's an indicator. Right. Only super fans. or at least somebody who has some kind of affinity enough that they want that souvenir or whatever. I think that's really brilliant. Like, I don't know, suddenly here's star eyed emoji again, Aubrey, because suddenly I was like, right, it's not just about the revenue stream.

It's about the indicator and the potential of [00:35:00] that patron. And anyways, I got super, super jazzed one day. 

[00:35:03] Sune: Yes, absolutely. Exactly. And also even in a merge, we are giving the arts organizations the opportunity to have their own workshop directly on Artalize and The reason for that, of course, is okay, nice with the revenue.

The artists can do the same, by the way. And it's nice with the revenue, but it's even nicer to know who your superfans are. So that becomes a data point in itself. One of the things to develop your audience with and incentivize your audience with. So therefore it's a natural part of our portfolio, so to speak.

[00:35:34] Jan: Yeah, the real cool thing is because we use AI again, and it's totally no hands, no touch, you know, it's automated. You can even make a collector series for your season. So there's a mock per performance, a mock per artist that performs, you know, there's actually some guys over at Columbus Symphony. They're even doing these, you know, embroidered Badges like for the college jacket with works, you know, the planets by Holst, the Mahler [00:36:00] five, you can get a badge for your jacket, you know, I think that's amazing what they're doing up there.

And that is, I don't know why they did it. Actually. I don't know them personally, but I think they will get some traction that they didn't even believe in out of that. 

[00:36:12] Aubrey Bergauer: Yeah. Oh, I like this a lot. I was going to say earlier. Yeah. We know the person who buys the Mahler. Embroidered patch for their jacket. We know that person is a super fan.

Like that's a real specific type of person Yes, 

[00:36:24] Jan: oh just kidding. 

[00:36:25] Aubrey Bergauer: Let me just clarify but you guys so you're based in copenhagen both of you. Yep. Yet you serve Primarily u. s organizations true or false That 

[00:36:35] Jan: is absolutely true and we just came back from the league of american orchestras conference in houston where we Met a lot of interesting fun people that love what we do also So that was nice running a business On us soil from Denmark, India, Ukraine, uh, Nigeria, UK, Italy.

You know, our team is all over the place. That's super cool. We decided to go for the us first because it's simply the biggest [00:37:00] market with maybe another kind of issue. You know, there is, I don't want to say complacency in Europe. The funding situation is just different as you know. The pandeMic was not a real problem for many European arts organizations.

The funding kept coming, you know, they couldn't produce anything, but the funding kept coming. That was not like that in the US. So we wanted to go where we could really make a huge difference. Then we'll go back to Europe. We'll shortly be doing that. We have started with the UK now, and we will come to Europe.

If you want a success with a tech product, you should start where it's hardest. So the old Navy SEAL saying. Take a real hard problem and go hard at it. Then you'll get a big success, you know? So that's also why we did it. That's a Silicon Valley motto, but it works. 

[00:37:41] Sune: Yeah. Especially if they sell it online.

So we can actually see who bought those badges because that's the whole thing here. We know already big organizations have that little corner shop with the merge. They don't know who buys it. Therefore, it's actually a little bit useless in my view. It's nice. It gives a little revenue and. If someone is wearing a t shirt, [00:38:00] you also get some awareness out there on the streets.

But what if you just sold it online? And also we are doing it in a fashion where they don't need to have a storage or anything because it's made on demand as close to the buyer as possible. So it's also in that way a little on the green agenda. 

[00:38:16] Aubrey Bergauer: Oh, I like, yeah, I like it. That's awesome. So then, okay. So then your background, we kind of talked about this briefly, but you're both have an arts background, data background, tech background, or is it different for each of you?

Kind of talk about. that for both of you. 

[00:38:30] Sune: It is a little different. I have an artistic background as an opera singer and Jan, he's a musicologist, if I may take it on your behalf, a musicologist with a tech background, because his first job after university was actually in a tech company. So I thought he was the perfect guy to ask about Joining my project back in the day.

Now we call each other the ying and yang of performing arts tech because I would say we're a rock solid team. 

[00:38:57] Aubrey Bergauer: Amazing. Sunni, I knew you had a background as a [00:39:00] singer. Yana did not know you were a musicologist. 

[00:39:03] Jan: Yeah, I do. Uh, practice to say that Sunni created beautiful music. I took it apart, . And, uh, I, I'm also an avid jazz piano player, but, uh, only for myself.

[00:39:14] Aubrey Bergauer: All right. I'm totally saying like, yeah, the tech, the data and the artistic piece come together. But then where along the way did experimentation come into the fold? Like, when did you become obsessed with data and experimentation? When did that happen for you? Like, these are things I talk about all the time.

And I think there are words more and more people at orchestras, opera companies, theaters, even museums are using and saying, but. I think it wasn't until I was getting to know you all last year when we first met that I realized like you really actually walk that talk. You're not just saying these things and you are really trying to help arts organizations be able to be data obsessed, iteration obsessed, and experimentation obsessed.

So, When did that happen? How did you become this way? I know my answer to this, but I want to hear your answer. 

[00:39:57] Jan: I can give it a go. The funny thing is it actually [00:40:00] miMics our own journey. You know, soon and I, when we started our first startup, we didn't even know the term startup. That's true. We just started a company because we wanted to help some people solve a problem.

And. And we didn't know about experimentation back then. So our development cycle was actually also slow back in the day. Then it got faster and faster. Then you get to know what you're actually doing. And then, you know, and then when we started Artalyze here, we knew that this is going to be a crazy fast cycle of fail fast, as they call it in Silicon Valley.

Do experiments, figure out what works and make sure to measure it. Because there is a good deal, even in startup, because it's hard to get right this data driven experimentation. There's a good deal of guesswork going on and it's super dangerous. We had on our panel in January, Bart van der Roost. I love his story.

You know, that's from the orchestra world, but that they wanted to change the programming of one of the events, but they figured out it was the timing that was wrong because families couldn't attend the family event that Friday afternoon. Again, you can make so many big errors. You wouldn't [00:41:00] believe the amount of data driven tools we have running in our own company.

It's amazing as a startup, there's so much tooling you can get off the shelf. It still needs a little bit of work, but for performing arts, there's nothing you can't become data driven before we were there. I actually would be so cocky to say we're the only ones who have this. Because we found out that really works.

[00:41:20] Sune: Yeah. And I would say for me as a, an artist, that there are so many tenor jokes out there, but you know, one of the jokes that I usually, and this is on me. I wanted to create a system that is data driven, that even a tenor can operate. It has to be so easy that a tenor can become a data genius. I truly believe we have reached that point that it's not part of my education.

I'm not some data nerd or something like that, but I do know how important it is. to get verification about everything you do. And if you don't get that, you work in the blind. And I know if this applies to building a company, it also applies to a career. [00:42:00] And it certainly also applies to a nonprofit trying to grow an audience and all the experimentation that follows that process.

[00:42:07] Aubrey Bergauer: Yeah, I love that. I agree with everything you're saying. And It's necessary not just to make good business decisions, but I just want to mention the element of getting buy in from others. I feel like this has been so true for my own career, and then now so many people I talk to and who reach out to me You know, they'll come to me saying, okay, Aubrey, I totally understand what you're saying.

I'm like bought in probably a lot of people listening feel this way. And they're like, but I've got to get my fill in the blank board, artistic director, manager, whoever to buy into this and these ideas. And so for me, that's where data became so helpful. It was not just Aubrey earlier in my career, the kid in the office.

Promoting a certain strategy or idea, but oh no, here's what the data says. Here's what the research says. Here's how we can test it. Going back to that, test it, measure it, and actually [00:43:00] know with certainty if this performed well for us or not, whatever. This is the digital ad, the other online experiment of some sort, that just became so important.

[00:43:10] Jan: Yeah, and operate. If you expected friction on this podcast, we are unfortunately not going to give it to you. 

[00:43:17] Aubrey Bergauer: I know we're, we are pretty aligned. 

[00:43:19] Jan: We agree amazingly on, on everything. 

[00:43:23] Aubrey Bergauer: This is why we get along. This is a kind of a good segue because so then when I think about boards, then the more I have brought data to boards in particular, every board I've worked with, I think probably ever, whether I was earlier in my career, Seattle Symphony, Seattle Opera, so these bigger organizations at the Bumbersheet Music and Arts Festival, and then Later, my own board at California Symphony and then clients now.

So I'm just like naming all these different types of organizations. Basically, every single board almost ever has asked about once we do kind of get them on the data track and using that to buy in, then it's what are the KPIs like data brings out a thirst for more data I've discovered, especially in boards, I [00:44:00] think.

Every board is like, but can we have a dashboard? Okay. If you are listening, wherever you are right now, like somebody raise your hand. If you've been through this with your board, they want a dashboard. This stuff takes so much work. And this eventually gets into the next thing I want us to talk about, but to just bring together some of these threads.

I've always seen a need for organizations to get a better handle on their data. We joke about even a tenor could do it. That's what you want for your products. But also, joking aside, there's a real need for that type of empowerment to help organizations get the best handle on their data as possible. For all those reasons, whether it's board wanting a dashboard or organizations just trying to get a better handle of their own data, we want things to be easy for the staff.

Like the dashboard becomes so necessary and bringing it all together. I'm now thinking back like I hate it when I have had to ask from my team over the years, you know, we need this, this and this for the board meeting next week. And it's so much work. And I know the data's there, but it's so time consuming to extract it back out of the CRM.

Where I'm [00:45:00] going with all of this is Speaking of all these challenges and speaking of all this data, I'm dying for us to talk about this new audience engagement dashboard, because I think it just really brings together so many of these things we're talking about. So I just want to rip off the band aid.

I mentioned, like, all these different challenges. I guess my first question in all of this, you know, we want to quickly and effectively track all these things. We want key patron retention metrics, but when did this first occur to you? Because this was way before we ever met. So, when was that? 

[00:45:29] Jan: It's not ages ago.

Is a year ago or something we also know from our own experience this requirement and when there's one thing that's dangerous for board work it is that you create data for the board because then what happens is the board tells you what was the development since last now want to work on you yeah but the data did actually not come from our systems it's something we had to sit and collate for this meeting.

We also knew that from our own experience and from also from our source we have talked to it has to be automated [00:46:00] as it becomes actually a big risk and liability even for you the work with your board where you can actually ruin trust and it can be dangerous if you can't manage it super straightforward so it's around a year ago that we started working on this because we have so much data.

We knew analytics a bit, you know, we talked about our background, we knew having a lot of data. There's insights to be had. And one thing is that we also are planning a casting solution for opera companies and stuff, because we have data on thousands and thousands of artists and, and for symphony orchestras.

But what about this whole audience idea? And then we sat down and thought, but we don't have audience data. You know, we don't have ticketing data in our system. Can't we somehow make it easy for orgs to still get this insight that we can deliver? And maybe I'm jumping to conclusions, but what we decided, let's just make sure that As our marketing tool, we'll just click a button and get a suggestion for a show me post in 30 seconds based on performing arts data.

What about being able to get a dashboard in 30 seconds? That was [00:47:00] the goal we set and put down for ourselves. We are now at, I think, 45 seconds, so unfortunately people have to wait 15 seconds extra for the dashboard. We hope that's okay. 

[00:47:09] Aubrey Bergauer: I cannot, I just have to interject. I cannot even, like, for me, it's like almost unfathomable that I could take, like, I've never spent 45 seconds preparing, you know, Right.

Exactly. Nobody has like, it almost sounds too good to be true. So I'm sorry to interrupt. I just had to interject and be like, this is like amazing. 

[00:47:26] Jan: We want to present simple, understandable things. It's not with the 82 percent significance. This is probably going to happen. No, it says how many first time buyers you have, et cetera, et cetera.

How many donors, how many major donors you have, what's the risk of losing big donors and all that classic stuff. It's based on your own data that you just drop in the system, so you can get them out, you can get them in, and you are good to go. And we use AI for this, that's why we can read any data source.

I want to say you can even drop a PDF, but I have never heard about anyone who wanted to drop a PDF. We still do get CSVs and Excel [00:48:00] spreadsheets from our customers, but I think they could drop a PDF, it would still work. 

[00:48:04] Aubrey Bergauer: Wow, okay, I'm going to come back to that, because I want to come back to the mechanics, but first, For everyone listening, though, I just want to say can we just list out the patron segments we're tracking with this dashboard.

So maybe like describe kind of the overview of we've talked about the why and the challenges and what we're trying to solve. So now talk about the solution. So we've created this dashboard. You brought me in along the way to come be a part of this with you all list out if you would give that overview and then list out like what are the segments this dashboard is helping folks track.

[00:48:34] Jan: Yes, it looks a lot like your long haul model. It has a little bit of extra bells and whistles. The first newcomers, we call them that, but first time buyers, first time buyers, you know, those, that's also one of the big challenges customers have when you get into a definition project. What is first time? Is it okay if they came seven years ago and now again, are they then a first time?

Yes, they are first time because you can't use that for anything. It's a totally different organization. Maybe they [00:49:00] went and visited. So they are the first time buyers. They are the multi buyers, which is actually also a little hard to define. But those are the ones who then came back. Came the first time and came back within a given time period.

There are the subscribers and there are the repeat subscribers, those who renewed the subscription, and then we have the donors, which we also break down in different segments, donors, major donors, repeat donors, you know, and because the board will be asking you, what, who are the lapsed donors? Who are the lapsed subscribers?

So we also have that, although you could say that's not fun to look at. You lost them, but you will lose some people. Maybe you can have some churn programs like the telecom companies have to try and get them back. You will always lose some people at some point. Maybe they move away, then you can't do anything, but then they can become a donor.

Actually. There are always another way to engage people. If they were subscribed for five years and move up to Michigan, down from Florida, then maybe they want to support their home orchestra with a donation. Never give up on anything. Now I answered your question in a confusing way. I hope it made sense.

[00:49:58] Aubrey Bergauer: Yeah. One of the [00:50:00] things I see a lot, I'll put it this way, is the challenges with segmenting our data. So a lot of these metrics you just named, yes, this is definitely the exact segments I talk about in my long haul model. These are KPIs that I recommend for any board, going back to dashboard metrics.

Aubrey, what are the key metrics we should be tracking? For me, for anybody who works with me, this is what it is. It's what's our first time buyer retention rate. What is the pool of multibuyers? Because that is our top prospect for subscriptions. And then once we get into subscriptions, it's breaking out first year subscribers from second year and higher.

And I just to give an example, just recently this happened. I was working with an organization. You know, we're looking at some of these metrics. They said, Aubrey, our subscription renewal rate is 80%. They were pretty happy with that. Not a bad renewal rate. And they said, But we got to get it higher. Of course, we all want to hire.

And that goes to this retention idea. And I said, Okay, but Overall renewal rate 80%. What is your first time, first year subscriber renewal rate? They said, we don't know. I said, okay, please run these numbers. Cause I think it'll tell us something. [00:51:00] They came back. They said, Aubrey, our first year subscriber renewal rate is 20%.

And I said, okay, that's a really big difference from 80 percent overall. That means you're once they're in, and this matches the broader data set. You could tell me if this is what you're seeing as well, but. When somebody's in for a second year, they're really in. And by the time they're in for a third year of season tickets, they are so loyal.

This is like 90 percent or higher often is the case, right? Okay, I see you nodding your head. So I was like, you want higher subscription renewal rates. You work on those first timers because that, and that's true for any subscription membership model in any sector. The first renewal period is the biggest drop off, but it's like, you want to improve that metric, your overall retention rate.

then your first time subscribers are rare to focus. And they were like, okay. So suddenly it actually brought clarity to our scope of work together. Oh, we're going to work on renewing first year subscribers. So anyways, I just wanted to put a pin in that because I think it's important for people to see like breaking out these metrics in a clear way.

And same thing for donors. First year donor renewal rates are pretty [00:52:00] abysmal nationwide. Being able to separate that from the longer, more loyal folks is important. And then just as you said, Jan, being able to see their ticket history. Oh, they've been subscribing for five years. They're not yet a donor.

Clearly we need to be soliciting them at that point. And so that's really what my long haul model is all about is yeah. How do we move people along this loyalty journey and take them on that journey? 

[00:52:24] Sune: And it really starts extremely early in our model, you can say it starts even before the first transaction, because the platform itself, the Artelize platform, you can say when we bring people to click the ticket link for the first time, you can assume they are not regulars, because Otherwise they would have gone directly to their own website and not through our slides.

But how do we already then and there greet them and say, thank you for actually choosing to be interested in our offering and clicking this ticket link. So what we do is we give them a chance to have a little screen coming up where they say, thank you so much [00:53:00] for your interest. Here's a 10 percent discount on your very first buy.

So we already start that conversation. as early as humanly possible. And we got to get that retention rate up. It's now, I think it's between 10 and 15 percent in average, depending on the organization, which is appalling. Uh, sorry to say that. So 90 percent never comes back. That's not good enough. We all, as an industry, we need to do something about this.

So the earlier you can start, the better. And then of course, if you then don't know that These people are new. If they ever come back, then you're working totally in the blind. So that's why such a dashboard is super important. 

[00:53:39] Jan: And if I can add a little thing, you know, the thing with arts organizations, many people did not come to work there.

And like soon it's a tenor exam. They did not work in arts organizations to sit and analyze their customer acquisition cost and newcomer retention rate. It's actually hard to get it to be intrinsic. So it has to be easy, or else it will be something you are driven by. [00:54:00] Away from because then all the fun begins.

Oh, let's do the programming now. Oh, that's fun. Which guest artists should we have next year, etc, etc. And you know, what we have seen is that newcomer retention or first timer retention drives customer acquisition cost. It drives if you looked at the organization from all the classic financial metrics, everything comes out of that.

If you only work on that and nothing else, then everything improves in the organization over time. And I think that's also what you are working on, but you can actually, if you don't have no bandwidth to work on anything else, then just do that. You will see amazing results on all your metrics. And if some expert came in and measured you from head to toe, is that an expression?

Then you would see everything going up. 

[00:54:44] Aubrey Bergauer: Okay, no, I love this so much. So very tactically then, if people are hearing this and they're like, yeah, Make it easier. Sounds great. I want help with my data. I want this easy thing you're talking about. Like, how does it actually work? Like, we said upload a spreadsheet.

Upload not a PDF. Maybe, we don't know. But, jokes [00:55:00] aside, like, spreadsheet CSV, like, it's that easy? Or, like, what needs to happen? 

[00:55:04] Jan: Yeah. That is how easy it is. The more data we have, the better, and we don't care about the formatting. There's only one requirement, but that's always there of some reason, because that's a unique identifier.

We do need to know that it's Aubrey Bergauer that bought a ticket or became a donor or was a subscriber, but you don't have to worry about it. And we have organizations, you know, that donor thing is a manual spreadsheet that they update on the side. Then they have an audience view running the ticket.

And they use MailChimp for the emailing or some other awesome real CRM system, but they simply are able to export it as is and drop it in our system. And I don't even have to care about time periods and scaling, just take whatever they can get and drop it. And next month they take whatever they can get and drop it.

We clean it and make sure to consolidate that all three is the same. And we have actually examples, even where they didn't have an email. Because the donor spreadsheet was not meant for mailing people. It was just to keep track of it. So we only had names or [00:56:00] household names, but we still match them because we can figure out who it is in the other.

[00:56:04] Aubrey Bergauer: So you're using, let me make sure, I know this is in the weeds, but I just want everybody listening to understand generally email address is the unique identifier. Okay. And that makes sense. Of course, it's unique to everybody. And then, but if not, you're saying, then you're trying to match on other fields is what I'm hearing.

[00:56:21] Jan: Yep. Exactly just like like the name and stuff and of course it's not an 100 percent guarantee that is that can kill any data project and that's what we've seen so often killing and also in my previous history working with banks and stuff you know spending seven years on identifying what is a customer you know then you don't get anywhere you should not.

Sweat the details too much if you lose one customer in the data it does not matter compared to moving forward that's a little bit the growth hacking mindset move fast and break things. I'm not sorry I don't mean it like that because we actually made sure that you move fast without breaking things we have taken sort of that risk.

Off your shoulders [00:57:00] by at least making it 99. 9 percent precise. 

[00:57:02] Aubrey Bergauer: I say all the time, yeah, progress, not perfection. Like if we don't have an email on every single patron, of course we want that. But if we don't like fine, take 99 percent of my data that does have emails. Yeah. 

[00:57:12] Jan: That's a very good one. Yes. 

[00:57:14] Aubrey Bergauer: I mean, I guess we kind of talked about that, but I just hear this a lot from organizations.

They don't have faith in their own data. We know we have some bad data and you're saying that's okay. We're going to work with what we've got, right? Am I hearing that? 

[00:57:26] Jan: Yeah, because the cool thing is if somebody bought a ticket, then they bought a ticket. I have actually not seen very often that that's wrong.

That is very rare that they bought a ticket and then they didn't actually buy a ticket and since if you buy a ticket let's say audience view you buy a ticket and later get a refund that it's also present in the data so we get two transactions we can know it away and say but it's still an interesting data point.

They bought a ticket and canceled, so they're not totally uninterested. That's not a disaster. Why? Maybe they got sick or they needed to fly to Birmingham that day. So something happened that did the job. 

[00:57:59] Aubrey Bergauer: So then [00:58:00] that can be used, that like data point then could be used in a future experiment recommendation from the platform of like, there are people who have, I don't know, I'm kind of making this up.

You can tell me if I'm right or wrong, but like, yeah, there's a group of people who may be their first time buyers, or maybe they were no shows. Send them an offer, send them a really steep offers. 

[00:58:16] Jan: Exactly. I would say that you shouldn't be so worried about bad data. We haven't yet seen that we can't get results out of any of our customers data.

And then it's a very different system. And most use these big, you know, standard tessitura, audience view, you name it. And they have data in nice order, you know, because it's about tickets and revenue and it normally works really well. So you can trust that. I would not be worried. 

[00:58:39] Aubrey Bergauer: Yeah, this is great. And just that you map the fields like people don't have to do their own data mapping on the other fields.

Yeah, that's I think a really big one. Okay. I know I'm in the weeds on all this stuff, but it's just one. It's so important. I think for the people doing this work to hear this, but I have to ask about one more problem that I see everywhere. And I want to ask how you dealt with it for some reason. And I'm not technical, so I don't know [00:59:00] why this is, but Almost every single CRM I have worked with, the segment that is the hardest to track is multibuyers.

So I said we would come back to multibuyers. Here we are. And I don't know why, but like to pull a report from CRMs that says, I need to know which attendees from this performance, any given performance, Which group of attendees, this was their second visit within 12 months, like for any performance, what are the multibuyers?

So we know these are the first timers who came back, like that for some reason is such a challenging report to pull. I think Tessitura is the best at this, but none of these databases are perfect in my opinion. All of them struggle, and I feel like this segment in particular of multibuyers. for like almost every client I've worked with has been the most challenging.

Can you tell me like, why is this so challenging? And what has Artelis done to be able to combat this challenge? 

[00:59:51] Sune: I think at least we can say that we have solved this problem. I would also say that's again where the new technology comes in. We are almost helped by AI that is [01:00:00] extremely good at overviewing huge data sets.

and getting the thin red line or the red thread that goes through that whole data set. It's a mixture of that you actually need to program it. You cannot just ask chat dbt to do this than having really strong artificial intelligence working with the data. 

[01:00:18] Jan: In software, there's something called a data engineer.

Those are experts in using machine learning and AI models. We have those guys solving that issue because it is not easy. It's actually super hard, and it's especially hard to do when we get arbitrary data in all kinds of formats. The reason why it's hard, it's not like a filter in your data. Show me the ones who bought more than two tickets.

Show me the ones that bought five tickets in this time period. That all CRMs can normally do. Very well, but when you start adding those sets, you know, and, and this, but not, uh, then it gets super complex. And the reason why most CMs don't have it is because it's a very, very bespoke requirement that's super hard to develop.

It will actually return wrong results in many [01:01:00] cases because it gets very technical. It's about joining sets and. Behind the scenes runs something called SQL normally and set joining in SQL is just super hard to get right. Yes, that's the technical reason for this. So we have data engineers working on it.

That's you know, it's not something Although it's easy to use what we built. It's not something we built overnight 

[01:01:20] Aubrey Bergauer: Well, thank you for going there because I made you talk about data sets and overlapping and sets and all these things. So I appreciate it because, yeah, it's just it's a problem that I see and you all have really been working hard under the surface to address it.

Easy for the user, difficult for you guys. Great. Love it. 

[01:01:39] Sune: We are doing the heavy lifting. That's, that's for sure. 

[01:01:42] Aubrey Bergauer: And then, okay, and then we talked about this integrates with any CRM, just upload your spreadsheets. But then how often, that's my next question, how often should people be like adding new data, like after every performance is that, or what's a best practice that you would recommend?

[01:01:54] Sune: Yeah, well, we recommend at least once a month, that's fine. Then you can see the [01:02:00] development going through your audience movement. But I would say in principle, you could do it. After each event that you have and get an extra bonus you can save from seeing development from wheat. I would say that's really up to you.

The system will chew it and give you the results. 

[01:02:13] Jan: By the way, just to say we are planning to build integrations with different systems, so I know many of, uh, the listeners here are using just in their private life, all kinds of apps, you know. Connect with Google. Connect with this. Connect with that. So we are planning to do, you know, connect with MailChimp, and then you don't have to think about then connect with Tessitura, then you don't have to think about it anymore.

Then it just runs automatically. That's the next natural step. But we wanted to build this easy, drop your data, step toward, yeah, there's no, nothing stopping you here in the beginning. 

[01:02:41] Aubrey Bergauer: Oh, that's great that they'll automatically be connected in that way. Even better. Okay. I know I've taken up so much of your time.

I want to wrap this up. I want everybody to know. Artelize has been so generous to offer listeners of the Offstage Mic a very nice discount for the product. I feel so honored. I was [01:03:00] able to be a very small part of coming, intersecting this project. And they said, we really want to extend this to your listeners.

So everybody listening, you get 20 percent off if you are interested in checking out the, not just the audience engagement dashboard, that's only one of their product lines. Artilize, as we talked about at the top, also offers the growth hacker, the experiments, you get that ticket sales support, just everything we talked about.

All of that is included when somebody joins as a user. They've been so generous. Go to my website if you want to learn more. It's aubreybergauer. com slash dashboard, 20 percent off for listeners. And if you're in my run at like a business academy, at least through the end of this year, you get 50 percent off.

So again, go Aubrey Bergauer dot com slash dashboard. I'm just really blown away. I have to say how you all are rolling this out in a way to invite organizations in, make it as easy as possible and meet just such a big need. You guys, thank you for doing that. I'm really, I'm really grateful on behalf of everybody.

[01:03:57] Jan: Thank you. And thank you for inviting us to share, [01:04:00] share all the weeds. 

[01:04:00] Aubrey Bergauer: I'll just mention if anybody wants a free break. resource. We did, the three of us plus some other experts that Jan and Sun brought in, we did a webinar back in January and that's all up on your website. Is that right? The rethinking audience engagement?

[01:04:18] Jan: Absolutely. It can be found in the footer. 

[01:04:20] Aubrey Bergauer: Amazing. Okay. If you send me the link, I'll put it in the show notes for everybody. And yeah, one more time, Sun, Jan, thank you so much. Thank you so much for being here. It has just been a real pleasure and honor working with you. Like I said, having our worlds intersect just briefly for this project and a real joy having you here on the podcast to get nerdy a little bit and talk about it.

So thank you guys. 

[01:04:41] Jan: Yeah, thank you, Aubrey. Thank you. 

[01:04:45] Aubrey Bergauer: That's all for today, folks. Thanks so much for listening. And if you like what you heard here, hit that button to follow or subscribe to this podcast. If you're new, welcome. I am so glad you made it. And if you've been listening for a [01:05:00] while, I loved so much that you were getting value from this.

So if that's you, please take just two seconds to leave a quick one tap rating, full on review. Isn't even required if you're short on time to all of you. Once more, thanks again. I'll see you next time, right here on the Offstage Mic. The Offstage Mic was produced by me, Aubrey Bergauer, and edited by Novo music, an audio production company of all women, audio engineers, and musicians.

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