Smart content decisions in publishing

Hello everyone and welcome. Thank you for joining us today. We're here to talk about something that almost every publisher or brand is dealing with right now, and that is the explosion of audience data.

Today, data is everywhere. You can see what performs, what doesn't perform, how readers engage, where they drop off, etc. But having access to that data is not the same as actually using it effectively. That's where the real challenge begins, and that's exactly what we'll explore in today's session: how to embed audience intelligence directly into publishing workflows to support smarter and faster publishing decisions.

Without further ado, let's check out the agenda for today. We're going to speak about audience intelligence in the modern newsroom and the role of data within editorial workflows. You'll be able to see an integration demo between Smartocto and WoodWing Studio, as this is our joint webinar with Smartocto and WoodWing. At the very end, we have a fireside discussion together with the speakers and a small slot dedicated to questions and answers from the audience and the speakers.

The speakers today: we are joined by Dani Leyhue, our product manager for WoodWing Studio. She will share how editorial workflows are evolving and how integrated insights really matter. And guest speaker Erik van Heeswijk. He is the co-founder and CEO of Smartocto, and he brings deep expertise in audience analytics and data for publishers.

Before we get started, I would just like to remind you that you can ask any questions that you might have throughout the session, and we will be addressing those questions at the appointed slot at the end of the webinar.

Without further ado, I would like to bring up our guest speaker Erik on stage for him to talk about audience intelligence in the modern newsroom. Erik, welcome.

Thank you very much, Martina. Let me tell you a bit about numbers. Of course, every newsroom in the world now has audience intelligence in some way or form. Metrics have been used for quite a number of years to produce more content, optimize content, etc. It's been very normal to use metrics in a way.

This is the old paradigm. I just want to compare it to a car. The traditional paradigm of analytics is: you do all the work. You in the newsroom, you in a marketing team, you in a journalistic newsroom. You write the stories, you optimize the headlines, you pick and choose, basically, and there is an analytics program that gives you all of the dashboard numbers: how fast you're going, how much of the distance there still is, etc.

There is that split between the actions on the one hand and what you see in the dashboards on the other hand. For 20 years, this has also been the paradigm within editorial analytics: dashboarding, reporting, looking at the numbers, taking some decisions as you go along.

But there's also a new paradigm coming. This will not surprise you, because AI is coming to media in a very strong way. I think we're one of the first industries to really be affected in a very large way by AI. I can see a lot of people arguing that everything should be automated. You're reading your newspaper in the car, the car does the driving. You don't have to look through the window. Everything has been done.

The problem with this, however, and of course we are quite used to autonomous driving in a sense, is that the car has no clue what you want. You still have to give it a route and a prediction of where you want to go. Do you want to take the touristic route? Do you want to go fast? Do you want to go slow? You really have to tell the system what you want to achieve in your car.

Maybe for cars it's very simple because there's a lane and there's an ultimate destination. But we all know that story strategy and story tactics are way more complex and complicated than autonomous driving.

That's of course very much seen today in a lot of newsrooms. Let me take an example. They use LLMs, ChatGPT, Claude, and all these kinds of LLMs to produce new headlines for a story. You can run an article into an LLM and you'll get new headlines. Again, here ChatGPT has no clue what you're optimizing against. Do you want more page views? Do you want more conversions? Do you want more attention time or loyalty? Do you really want to please the sponsors or something like that? All these goals are very different, and LLMs basically have no clue about that at all.

In a sense, it's still very hybrid driving. That's what we suggest. We are still doing most of the actions in the newsroom, and I think there's a fair case to be made for that. But you're also getting data that is much more actionable. Again, here we're very used to having navigation software in your car that says where you have to go left and right. That's a very different paradigm than just looking at the meter and trying to see what is the best route with maps and paper maps and all these kinds of things.

You're in charge, but the car actively helps you out because you know what you want. You've told it, basically, this is the end of the line. And it says, okay, you need to go left here, you need to go right there. Even if there's a traffic jam, it is rerouting you. That should also be the paradigm for analytics in the newsroom. It should actively help you out there as well.

Let me give you a couple of really bold statements here. I think we've talked for a long time about the gap between data and insights. You have numbers, behavioral statistics, people click on a button or they view an article and you have a page view. For a long time, it was really challenging, and in a way it still is, to make a real-time dashboard out of that or to make a really good report out of that. Even knowing which dashboards are more attuned to which business case is quite hard work, I would say.

But my bold statement here is that the gap between data and insights has been solved. We know which data is very much attuned to which business case, or which content strategy is using which metrics at what time. For example, in the last 10 to 15 years, there have been a couple of really good tools that solve that problem. I don't think that's actually a real challenge anymore.

Again, building real-time dashboards is not that easy. Technically, there are all kinds of hurdles there, but it's not the main problem anymore in the newsroom. The real challenge is the gap between insights and action.

If you're fair and you look at your own newsroom, you can probably notice that. They have a lot of numbers. You have some kind of dashboards on the wall. Maybe it's even Smartocto. But they tell themselves, okay, I understand what is happening with these stories, but what is my next action? What am I going to do based on all these nifty graphs and metrics?

That part, that real challenge, is a cultural challenge. It's not a technical challenge at all. Basically, the conclusion is, for us at least, editorial analytics is not about numbers at all. It's about decision-making. Of course, you use numbers and all kinds of different metadata in order to facilitate that. But numbers are just the beginning. If you have a lot of numbers that don't mean anything in terms of action-taking, you're basically doing it wrong, and those numbers are worthless.

Let's take a look at what actually needs to happen in a cultural way to have a very strong presence of numbers in your newsroom. First of all, and it sounds really logical, but again I want you to think about your own newsroom, your own organization, and really think through: did I manage this right? Did I succeed in this?

There needs to be a clear, explainable bridge between the strategy, the content tactics, and the metrics. What is the ultimate goal for your company? What tactics are aligned with that? If A happens, do I do B, C, or D? And which metrics are you looking at in order to solve that problem?

What we see in organizations a lot of the time is that there are multiple strategies. If you ask person A, they will explain strategy one. If you talk to person B, they will have a totally different view on the strategy. But even worse, we see that there's a big gap between strategy and content tactics.

For example, you have a media company that's all about conversion strategies. They sell subscriptions to newspapers or digital newspapers, and in a sense they're always looking at page views in the dashboard. Which is not nothing. You have to have visitors in order to get conversions, but it's not the main metric that you should be looking at. You have a lot of flybys, for example from Google Discover. They don't mean anything for your conversion strategy, and we see a lot of that.

Taking a look at those numbers, again, we solve the problem, but it's more of a question of really attuning your culture in the right way. Not less important, the newsroom and the organization need to agree on: if this happens, that needs to happen. The marketing department, all of the management, the editors, they really have to have a clear sense of the next course of action. If a graph goes a certain way, if a metric goes a certain way, what do we do? Do we make extra content or do we make less? Do we get on the socials a bit more? Do we do less of that?

If you ask the newsroom a couple of these scenarios, let's say A, B, or C, what is the next course of action, you'll be surprised about how chaotic the answers will be in the long run. They shouldn't be. There's a clear path to that.

The most important one for this webinar is the reason for Smartocto existing: data should be actionable and forward-looking. A lot of people use metrics in terms of reports, and they just have really clear conclusions about what happened. But data should be about what is happening, what needs to happen, or what will likely be happening.

This is a very strange example maybe, from the philosopher William James in the United States, pragmatism. He basically said people take umbrellas outside because it will likely rain, not because it rained yesterday. A lot of the actions and decisions that people take are based on the future and not on history.

Of course, it sounds hard to have metrics on the future. But actually, a good analytic system takes that into account. It does projections, it does lines, and gets a sense of what an action from your side will in the end invoke.

Maybe a last example of that, just to say it's not only the metrics, it's also the visualizations. Here you have our coordinate model, for example. You can do this yourself at home. It's not about our tooling in itself. It's about the way of thinking. Let's say the x-axis is about reach and the y-axis is about, in this case, loyalty. You get four quadrants if you do it like that.

You can imagine the top-right quadrant: all of the content is superb. We sort of prove again and again that that content really converts into any kind of subscription. Of course, it's your golden stuff. The top-left content is about the niche. It's not really great in reach, but it's very good in engagement or loyalty. It's your niche content. If that's really good, there are a lot of people that really like that content, but maybe not enough people.

The bottom-right quadrant is all about clickbaity stuff: a lot of reach, but not a lot of engagement or loyalty. The substandard bottom-left quadrant is content that maybe you should have a look at. Maybe you shouldn't even make it.

The concept of Smartocto, as I always say, is that 35% of all media content in any media company basically shouldn't be there if you take your own baseline and if you take your own metric and calculate which content shouldn't be there. It's always around 30 to 35%. Imagine if you could save that time.

To get to the action there as well: the top-left column, you should distribute that content more. It should be on your homepage, should be in your reports when you think the topic is very suitable for a bigger audience. The niche content deserves a bit bigger audience. That's very clear.

On the top-right corner, replicate that as much as you can. What that content is doing really well, pay a lot of attention to it. The bottom-right corner, you need to enrich it or follow it up with more engaging content. It has a lot of clicks and a lot of attention, but you should at least have a look at how to convert those people into other articles or into more attention time, for example.

And the bottom-left, as I said, evaluate. Take a look at whether that content really needs to be there. If a certain topic or certain segment is always in the bottom-left quadrant, ask yourself the question: is this content really important for me as a brand? And if the answer is no, I would suggest taking a very hard look at the production of that.

Like I said, these are all the cultural elements of this: the bridge between the strategy and the content, the newsroom and the organization agreeing on the tactics, and data being actionable and forward-looking.

You notice here also the word notifications. That's actually what Smartocto has been doing since its birth, like eight years ago. It calculates notifications based on the data. Our vision is that analytics should be more like navigation software in your car. It mentions where you should go left and right. That's what data in the newsroom should also do. It should mention when a story needs to go to Facebook. It should mention when you need to do a follow-up. All of these notifications, based on the business model and based on the real-time data, are in some way or form in Smartocto.

You will see them later on in WoodWing Studio as well. The last point, and this is a segue into the next speaker as well, is that editorial analytics should be where editors live. They didn't go to journalism school to just look at graphs the whole time. They need to be where they make the content. They need to be there when they really want to make optimizations. It needs to be full frontal and in their face all the time. It needs to be relevant in that phase. That's also why we made an integration with WoodWing, and Dani will tell you all about what is there in the workflow. She knows everything about that.

All right, thank you so much, Erik. I want to talk a little bit more about the editorial workflow and the role of data.

I'm lucky enough to work with a lot of different brands, publishers, and industries. The one thing that I hear most often nowadays is that we have to do more with less. It's a hard time to be in publishing and in this industry, right? Because the reality is that your workflow is actively changing. You have more things that you have to worry about. You have to worry about publishing. Your deadlines are likely increasing. There are so many different stakeholders that you're managing. You've got so many different updates that have to be done. You're worrying about 50 different decisions that all have to be done in the span of one day. And all of this has to be done and managed somehow.

If you've never heard of WoodWing Studio, I would highly recommend that you take a look at it because WoodWing Studio can be used as a content hub where a lot of this content can live and these decisions become transparent to other people.

Customers of WoodWing will know that inside Studio, you have the ability to write and create your content. This works out really well as you're creating the content. However, as Erik mentioned, all of that data that you need, all of those analytics, it sits in an external dashboard. Unfortunately, no one is spending their entire day sitting in a dashboard. That's why we need to bring them together.

In reality, people are under pressure to make these decisions, but they need better data where they're making the decision. What we're talking about is that data is living outside of where the decisions are getting made. If you're using a workflow tool like Studio, you need that data to live closer to you so that as people are creating that content, they have it at their fingertips. They don't have to do that extra step of: wait, where do I need to go log in? What was that tool that I was using?

In a publishing area, as you create content, there are a few moments that truly matter for the content creators. We know that you need to plan content, whether that's inside Studio, inside a tool like Monday or Wrike, or wherever it may be. Someone is deciding something needs to be written. When you're making that decision, that's when those analytics really come in and matter. What could I be looking at that is trending really well? What insights do I have that suggest that this piece of content is going to outperform other content that I've already created?

You're creating the content. This is when people are writing. You're editing the content, likely in something like a content hub. You need to know: okay, this is why I need to write a headline in a specific way, because it's more SEO friendly. Or this specific topic has been trending, and this is why it's so important.

Lastly, when you're publishing the content, when it's about to go live, this is when you're taking a final big-picture glance and looking: okay, this is really going to do well because I have the story, I have my tags, I have my metadata, and everything is lined up to match exactly what the data suggests. This is when it's most valuable.

From the product perspective, there are a couple of different pieces here. Your workflows are becoming more and more integrated with all of the different tools that you use. It's becoming less work to go and reach out to that third party. Where did I leave this dashboard? You want it to be as seamless as possible for the people who have to make these decisions so that they can produce more content and better content. It's about doing more with less.

The editors, bless them, are at the front end of this. They're on the front lines. They need better guidance, and it's about helping those editors get the guidance that they need at those high-value points.

Lastly, context is everything. Making sure that your metadata matters, your tags, your value, all of these pieces. The reason why some articles may not perform as well is because your adoption fails, because the data is not in the right place at the right time. Having those analytics at the right moment is truly key.

If I look at this from the data access to the decision area, we're talking about shifting a mindset. In the old model, it was: well, I just need to do this and then I go and send it out. If I look 20 years ago, back when print was still more dominant and still the most important channel that most publishers were managing, you would send out a print newsletter, a print magazine, or a booklet, and then you would have no idea which articles were people's favorites.

But now that we're seeing the rise of web and the fact that digital is outperforming print in many industries, you can start to see which articles matter. Why are those articles getting the highest traction? All of that information becomes so much more interesting to the people who are creating the content, because now you're more likely to be able to create a new similar story that continues to attract your audience.

This is revolutionary for the people who are creating the content. It's all about bringing those insights back to the editors at those high-value moments.

If I hone in on my one big key takeaway for today, it's that insights are really only valuable when they're delivered in context right next to your workflow. To me, that's one of the reasons why Smartocto and Studio can fit so well together, because Smartocto brings you all of those insights, and WoodWing Studio brings you that editorial workflow. The two can really exist beautifully hand in hand.

To show you a little bit more about that, I'm going to invite Erik back to the stage to give you a brief walkthrough.

Thanks, Dani. Like you said, there's something really powerful if a good CMS, a good publishing system, and a good analytic system team up. You get the right data at the right time in the place that you really want it. That's of course the ultimate goal.

Maybe it seems really weird for me, as the CEO of a data company, to say: listen, less is sometimes more. You really have to be attuned to what data is actually valuable for you. It's so easy to overwhelm newsrooms with a huge pile of numbers. They need to live where you make a decision, and they need to be accurate, and they need to be the right metric. That's of course what we have been doing all that time.

Let's do a bit of a demo. We'll show you what we've built together. For the people here in the webinar, a lot of you have probably been used to using WoodWing Studio. You recognize what the system is all about. Basically, you make stories in WoodWing Studio. You edit them, you visualize them, and lay them out as well.

At the top right of WoodWing Studio, there's a button called Apps. You can click that and go into the Smartocto app. Initially, that will go into something really interesting but also very simple: it's a dashboard. You will have that dashboard where you want it to live. It's a dashboard with page views and all kinds of other metrics. You'll see your impact throughout the day. There's engagement in that part, and with the colors you see where all of the visitors came from. You have a sense of the impact of your story strategy.

If you go into different packages with Smartocto and WoodWing, you have multiple dashboards, you can make your own dashboard. There are a lot of different versions of that when it comes also to different content strategies. The default is a very interesting, very simple, very attuned dashboard.

But of course, I promised it's always more than dashboards. Because what happens if you make a story in WoodWing Studio and you have the Smartocto app? Let's say you have a story about the Olympics and you publish that story. What basically happens here is that at the top and in the bottom right, you see that the WoodWing story ID is connected to the Smartocto story ID.

It sounds a bit technical, but basically that means that Smartocto understands what you publish via WoodWing as well, and vice versa. All of the analytics that Smartocto has, WoodWing Studio understands which story those metrics belong to. Of course, then we can do all kinds of nifty stuff outside of the dashboarding as well.

For example, and again this is just the start, we made something really special. In WoodWing Studio, there's your inbox. In the inbox, you get all kinds of messages about workflow, your colleagues, and how to produce an even better story, for example. But in those inboxes, you also have the Smartocto notifications.

Automatically, based on your business model and based on your real-time data, it will say something like this: this story or this piece has few views, but readers are highly engaged. Consider promoting it more or using it as a template for similar content. Again, if you remember the product model, this is content that is in the top-left quadrant. It's niche content, but it's really good, so you'll get notified about that as well.

Smartocto has over 300 very interesting algorithms that help you on a daily basis and even on a minute-by-minute basis. What content needs to go to Facebook? Which content drives conversions or has conversions? What is actually happening on social? All of these different things, attention time, scroll depth, they're all very much in the notifications of Smartocto. Even user needs, if you have a newsroom that is into the user needs commissioning system, which of course Smartocto is the basis of and the founder of.

Here you see a lot of interesting notifications, and we can have them in your inbox. Even more spectacular, we can also send them to you when you're in the social team. You only get the social notifications. When you are on the homepage team, you only get the A/B testing notifications or something like that. There are a lot of different possibilities. Here, you're actually looking at navigation software in your car. But in this case, the car is WoodWing, and Smartocto is the navigation software. Wouldn't that be nice if you have that as well?

Of course, there's the dashboard, but you can also click on one of the stories in the dashboard and then go into the real-time system of Smartocto. You can click here to the story page. You have real-time views on what the impact is from a view perspective: page views or something like that. But there's also a graph on engagement in social, for example. You have all the app data. You have attention time, if we implement that as well. It's all about that specific story.

Everybody in your newsroom is very happy to click on that, click on the notification, click on the dashboard, and immediately know what the basis is for the advice that Smartocto gives. We are always giving you tips and advice very actively, but we're also giving you the why of the advice. Why are you doing that?

In this sense, we can help you create different workflows. We can help you in the changes that media is going through. We know, of course, that people in the newsrooms are creatures of habit. It's very difficult to change the way they think sometimes about story production. Newsrooms are almost a military operation. Everything is busy and it needs to be very precise.

That's also why it's so important to have it very convenient in your publishing system, in WoodWing Studio, and to be connected to the next action in the workflow, for example.

This is what we built initially. It's very much ready. You can sign up to it. But I'm more discussing here the view that both WoodWing and Smartocto have, that numbers need to be there where you are, so to say. Together, we made that possible. I'm pretty sure in the next weeks and months or so, we'll take that to another level, and that's a real promise.

Let's maybe get the other ones back, because this is more like a fireside discussion that we can now have on all of the stuff that we did. We're very happy about it.

Yes. Perfect. Thank you so much for the presentation, Dani and Erik. I think the car analogy and the navigation system were very great to bring us closer to what you're trying to get across and the message you have for publishers.

I believe they can download the brochure that we have by this screen. You can all see it. It's a bit more information on the integration that Erik just showcased.

If we move on to the fireside discussion, I would like to ask a question for Erik. Can you probably share with us any mistakes that publishers make when they adopt audience analytics?

Yeah, that's a good question. One I already mentioned. Basically, the main mistake is that you just put a dashboard on your wall or just a report in your email and you think: okay, that's case solved. We have data now. That's of course a bit of a lazy approach to editorial analytics, and it doesn't work, to be honest. Because it starts there. The technical challenge is just the start of it, and it's not the end result.

I also feel that editors want more context with their data. A number doesn't say a lot unless you know to which tactic, to which strategy, that number really belongs. If it's a number and you cannot get any action on that, that's also a mistake. It's just a number for you.

Maybe just one funny anecdote. I always did it with chief editors. They say, I have a lot of data. I said, okay, let's do it. Let's print out every data point, every data report that you have in the last month or so. We printed it out. I did it a couple of times. So you have a huge stack of reports that they consume.

Then I said, okay, listen. On the left part of your desk, put all the reports that you didn't read and didn't take any action on. On the right side of your desk, put a report, a piece of paper that you really did something with, that was the core of an action. I guarantee you if you do that, the right side of your desk is pretty empty.

Most of the data is not very useful. It's just fetish. It's just numbers, vanity metrics and stuff like that. Of course, there is a lot of knowledge nowadays in 2026 on how to do that better.

I want to jump onto that too because when I was rolling out a customer, we were talking about the same thing. They were recently moving from print to digital, and we were talking about analytics, working with their web team and showing them: okay, how does this work? What does this look like?

The print editor, who has seen these analytics for the very first time, looks up and just says, I have no idea what these analytics mean. This doesn't connect to anything that I'm writing. So you have to translate all of this back into a language that speaks to the editors, that speaks to the writers. I think that's really what this integration showcases: we're speaking the same language as the people who have to make these decisions.

Exactly. True. If you just throw numbers at them, you expect them to be the slave of the numbers. I'm not sure how many people in the webinar today have experience in journalism, but I'm pretty sure everybody knows journalists are not like that. They don't do that. And rightly so, to be honest.

That being said, what changes do teams need to make to adopt this data-informed publishing, for both speakers?

You can go ahead, Dani, if you want, or maybe Erik.

There are a lot of different cultural changes that you have to go through. First of all, it's a cultural willingness to accept that the way that you may or may not have been working is adapting. We're in a very rapidly developing world, between AI, analytics, and things shifting to digital. It's the fact that we're constantly reinventing ourselves to try and be better for our audiences, to try and be better for our own newsrooms.

It's the age of curiosity, I think. How could I do this better? Why did this article do as well as it did? As long as you have that curiosity, that willingness to keep exploring, to keep trying, to keep questioning why am I even doing the articles that I'm doing, I think that helps people succeed. Once you have that curiosity, you're going to grow, you're going to learn, and you're going to serve both your own brand and your customers better.

Yeah, I think that's totally true. I also think a lot of newsrooms expect it to come naturally. They don't want to go through the pain of changing workflows and the way they approach data. Of course, I'm not advocating for pain, because journalism is a lot of fun, but you have to go through the motions. You have to take this seriously.

In the end, you have to. It comes to you, whatever you're going to do, and there's no escaping really improving the quality of your decision-making. That means taking a hard look at your metrics, taking a look at the way analytics is placed into your workflow, taking a look at your publishing system and saying: listen, how is this system helping me respond to metrics?

The main challenge here is, I think, that newsrooms tend to be so busy that they don't find the time for exploration or even some internal feedback. I think you have to make time for that. If there's one piece of advice you take away from this: really make time for strategy. It's worthwhile. There are a lot of things changing in AI and media, and in the next 5 to 10 years, things like that will be crucial in the end.

Thank you. Any lessons learned from publishers that are adopting data-driven workflows that you could share, Erik?

I think that strikes something that is quite striking. In the end, there are two things that mainly happen when you do it right. Let's also talk about what happens when you do it right. It's a lot of fun, actually. I talk about pain, but if it really works and it's worthwhile, you can see people saying: listen, it doesn't diminish the quality of journalism, or it doesn't diminish my sense of writing. It's actually journalism done better. There's a lot of satisfaction around that as well.

Also, when you do a good data strategy and when you do a good workflow strategy, the first thing that happens is that people in your newsroom start drinking coffee together where they previously didn't. There's a lot of synergy. There's a lot of cooperation that metrics can force onto a newsroom in a good way.

Previously, social teams didn't discuss that much the stories that went out to Facebook with the chief commissioning editor. But now, in a world where all these channels have to be served in some way or form, and the metrics help with that, a cup of coffee is just the first result of a good data publishing system strategy, so to say.

Sure. I remember doing a rollout with one brand where their print and their digital team were talking together for the first time. I will be honest, the print editors were concerned, because the second you start saying workflow changes, to those of us like Erik and I, it's exciting, because it's something new, something different. But to the editors, the creatives, they come in and they're scared. They're thinking that AI is coming to take their jobs.

We had to sit down and really explain to them: it's not about taking your jobs away. It's about trying to help you and empower you to do what you already do today better. For the first time, all of these new insights are available. This is like a gift. We want to give you this great opportunity to learn more about the people who want to learn from you, that are working with you. It's about trying to help you more than replace you.

Exactly. In the end, if the data doesn't work for you, you end up working for the data, and that's not great.

I think there are so many possibilities there as well. Let me rephrase also a question that Dmitry Shishkin, hi Dmitry, asked in the webinar. The question is for both of us, Dani, if you're okay with that. It concerns a specific decision-making process regarding multimodality. How does a CMS or a publishing system and an analytic system play out in delivering multimodal content?

I think what you're referring to, Dmitry, is how all these formats in different channels work in a publishing system and analytic system. Basically, we've always advocated for making a rough story, a rough narrative, and then in the end translating it into different channels, with channel managers and stuff like that.

There is a half-draft story that the newsroom makes, and then the translation into channels comes later. Of course, you can use all of these metrics and all of these channels to make follow-ups and other channels, for example, to really see if that story resonates on all of these channels in the same way. How you use user needs or tone of voice or formats on these different channels. There are a lot of feedback loops around that as well, and I think that will also count for how you do that in a publishing system like WoodWing Studio.

Right. Sure. I'm going to jump on top of this now because you're speaking my language. When we talk about all of the different platforms that have to be managed, whether it's your digital platform, your web platform, your print platform, there's a lot of different content. With the rise of publishers, what we're seeing is that people needed a place to store all of this content. That's where WoodWing comes in.

What WoodWing created is something called Studio, which acts as a content hub for all of the content that's actively being created. You create your content once, you publish your content to wherever it needs to go. You could start print, you could start digital, you could start with an image, you could start with just a rough draft of an idea. All of that content being stored in one centralized place allows it to be transparent to all of the different stakeholders that need to be involved as that story continues to evolve.

The benefit of having that content hub, WoodWing Studio, is that let's say, for example, I am creating a story. I am doing a great job, and then maybe something happens. I go on vacation. I'm out for a few weeks. I wouldn't want Erik to be waiting on me to finish that story. I want him to be able to continue the story in my absence. That's why having that content hub and having the analytics attached to that content hub could help him. He can take the story, see where other trends are around that story, and take it across the finish line.

Storing all of your content in one place helps you create a structured path for your content and really helps unlock your potential there.

Exactly. One of those stories can be, and it's a terrible word in journalism, but let's use it anyway, a semi-finished product. It can be a narrative that you translate into the other ones. Of course, you need a good publishing system to do so, and you need good data to make those decisions as well. That would be our explanation, Dmitry, to your question.

Thank you both. We kind of transitioned from the fireside discussion. Sorry, that was probably my mistake.

Magdalena, no worries.

We have another interesting question from Danielo. How does the system calculate the lift of a suggestion if an editor changes a headline based on a notification and clicks go up? How does the system distinguish between the quality of the tip and natural social media velocity or breaking news timing, for example?

And then there is another question. Does the integration support native A/B testing or any other solution? For example, can we serve the Smartocto-recommended headline to 50% of the audience and a human-choice headline to the other 50% to mathematically prove the value of the AI's decision?

Good question, actually. Let me begin with the last one. Yes, we have our own A/B testing, split-testing algorithms. They can be used via your own website in just an overlay that you get automatically there. But those algorithms can also be connected to WoodWing Studio and then used there as well. Whenever you have those questions, please come in and I think we can really make that business case if that's very important. But yes, the answer is we do all of that.

Your first question is mostly about how you can evaluate the power of the notification when all these things are happening at the same time. Basically, because we also have all of the historical data, Smartocto is not only a real-time data analytics system, but it's also historical analytics. We know what's happening across months and even years. Sometimes we know what's normal. We know how many people click on a teaser, for example, when that's related to a topic or an image or something like that.

We can benchmark all of these things quite rapidly and quite well. Our notifications know when it's a niche story that deserves more audience. We have all these listeners on engagement and attention time. We know all of that, and we can compare it to the same story on the same day, in the same place, in the same hour. All of these benchmarking calculations are going on on the back end of this.

I think we have around 65,000 calculations per second across all of these different metrics and all of these different stories. There's a lot of machinery going on on the back end, but we know quite well how to benchmark all of these different metrics. Again, if you want to know more and you want to get really nerdy, feel free to just reach out.

Thanks. One last question from the fireside chat that we pre-planned, of course. What is your opinion? What will happen to analytics in the age of AI?

I think that's a really hard one to answer, but I'm going to jump in first. The only certainty is that we're uncertain, right? Things are going to change. AI is rapidly evolving. As it rapidly evolves, it's also going to rapidly change and evolve the newsroom. It's going to change how people consume content, how we create content, because more and more people are wanting and hoping for specialized content on these niche markets.

How can I create more specialized content on these niche markets? As AI evolves, it will change the reader, which will also change and develop the editor. I think it's just a cycle that's going to feed itself. Erik, I'll pass it to you.

Thanks. Such a difficult question. Of course, there are going to be a lot of different things going on at the same time. There will be more content. That's for sure. There will be an avalanche of content, and not everything will be great. I'm not saying in the newsrooms, I'm saying outside of that as well.

There's a real chance for journalism to stand out, and that's of course a given, but it needs to be picture perfect. Every piece of content needs to be spot on, because otherwise you won't survive in that avalanche, that sea of content that will be there in the years to come.

Secondly, and we already noticed that, it enables us to give more context to the data automatically. We've built systems that automatically give you strategic advice on the data and give you notifications that are very dynamic to a specific person. The why of the data will become much more pronounced, and LLMs of course enable us like that as well.

Lastly, so many things will happen with the UX in the years of LLMs. There are a lot of things being summarized in other tools, so probably SEO will be replaced by optimizing content for LLMs as well. But I'm pretty sure data will help with that in some way or form. I will also stick with Dani's statement: who knows?

Yes, it's an uncertain future indeed. That being said, I'd really like to wrap up the webinar because we pre-planned a 45-minute to an hour slot. I think it's concerning that we don't have any more questions. Those were the couple of questions from the audience plus the questions I had for the speakers.

I think we really unraveled an interesting topic for publishers and brands, of course, concerning getting data insights closer to your workflows and the decision-making you're doing with the content that's to follow.

Again, you had the chance to download the brochure. After we end the live webinar, you'll also be forwarded to a URL where you can download the brochure in case you missed it throughout the conversation.

Again, big thanks to Dani and Erik for today and their time, and for sharing their expertise and knowledge on this topic. I do hope that we will meet again soon and discuss the updates within the integration and the Smartocto features that you're planning to add, Erik.

Thank you so much.

Thank you both, and thank you all.

Yes. Until next time, enjoy your day and see you soon. Bye bye.

Note: this transcript has been auto-corrected using AI, it may contain mistakes