Georgios Grigoriadis, Founder and CEO of Baresquare, an AI-driven analytics platform, discusses his career journey from data scientist to founder and the development of Baresquare. He shares how the tool leverages AI-powered insights for marketing analytics, the challenges and opportunities in B2B marketing, and the potential of AI to empower individuals in marketing rather than replace them.

About Baresquare

Baresquare redefines data analytics by transitioning from traditional dashboards to proactive, AI-powered insights delivered directly to the right person at the right time. Baresquare pioneers a new approach where manual dashboard analysis and human intervention are unnecessary for identifying crucial business events and their underlying causes. This frees marketers, strategists and analysts to focus on creative endeavors and expanding business opportunities while providing insight that no other data set can provide.

About Georgios

Georgios Grigoriadis is a data advocate and the founder and CEO of Baresquare, a tech startup turning data analytics on its head by shifting from traditional dashboard-based analysis to proactive AI-powered insights, delivered directly to the right person at the right time. Fueled by the belief that data should empower, not overwhelm, Georgios built Baresquare to transform complex analytics into clear, useful answers for anyone to understand.

 Time Stamps

[00:46.1] – Georgios discusses what led him to build Baresquare.

[06:11.9] – How can marketeers use Baresqaure?

[06:48.8] – Georgios shares if he thinks the B2C industry is further ahead in using analytics.

[19:47.1] – Georgios offers some marketing advice.

[17:52.4] – Should young people embark on a marketing career? Georgios shares his opinions.

[24:51.9] – Georgios and Mike talk about the future of AI and its impact on the industry.

[31:48.9] – Georgios’s contact details

Quotes

“Baresquare today is turning data analytics on its head. And, we are not talking in terms of tables and numbers, but rather in terms of words and paragraphs.” Georgios Grigoriadis, Founder and CEO at Baresqaure.

“It’s very frustrating when those insights, they don’t find themselves driving action. But action, it’s more a matter of communication. It’s bringing people together and aligning their understanding.”  Georgios Grigoriadis, Founder and CEO at Baresqaure.

Follow Georgios:

Georgios Grigoriadis on LinkedIn: https://www.linkedin.com/in/georgiosbaresquare/

Baresquare website: https://baresquare.com/

Baresquare on LinkedIn: https://www.linkedin.com/company/baresquare/

Follow Mike:

Mike Maynard on LinkedIn: https://www.linkedin.com/in/mikemaynard/

Napier website: https://www.napierb2b.com/

Napier LinkedIn: https://www.linkedin.com/company/napier-partnership-limited/

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Want more? Check out Napier’s other podcast – The Marketing Automation Moment: https://podcasts.apple.com/ua/podcast/the-marketing-automation-moment-podcast/id1659211547

Transcript: Interview with Georgios Grigoriadis – Baresquare

Speakers: Mike Maynard, Georgios Grigoriadis

Mike: Thanks for listening to Marketing B2B Tech, the podcast from Napier, where you can find out what really works in B2B marketing today. Welcome to Marketing B2B Technology, the podcast from Napier. Today I’m joined by Georgios Grigoriadis. Georgios is the CEO and co-founder of Baresquare.

Georgios: Very happy to be here. Thanks for the invite.

Mike: Great having the podcast Georgios. So just to start off with, can you give us a bit of background about your career, and in particular, I’m interested to know when you move to America and why you decided to make that move?

Georgios: Absolutely. My background is in computer science and math, not an electrical engineer, which I know that it’s your background, but still, it’s rooted very well in in math and science. But I quickly realised that the most compelling puzzle for me to show was not about gold and algorithms, but rather what makes a product a success. So I have always been fascinated by data, information, and drawing conclusions and the power that those insights and information can have. In hindsight, I think that I was naturally drawn to the stories that data can tell. And as I told you, and the power that they contain, but at that time, my understanding my interpretation of this fascination was on what was possible and not possible with the data that I was available. So as right now we are having this artificiality in intelligence crop yard, where people are putting together some solutions that work and some others that don’t. Back in the day, it was exactly the same with data driven tools, approaches and processes. So I was working very much on now, that part of the industry, where today we know these technologies are Adobe analytics, or Google Analytics. And the challenge was how to put together data visualisations that they can drive results. I advocated for verbalising data, it was rather a radical approach where everyone was trying to visualise data. But I did so because I thought that communicating insights with natural language was more impactful. So I moved to London from Greece, and establish that an AI software company and in London, I found out what our positioning was. Now, this is the the point that I moved to New York, while I was trying to discover what should be our go to market approach. And I mean, New York has a very unique vibe, as you know, in a sense, performance under pressure of the melting pot that Manhattan has it’s it’s more about human skills and creation. And that is making it totally different from the West Coast startup centre. Now that the go to market team has been established in the US, I’m enjoying the Mediterranean climate and cuisine. I am right now. I’m based in Athens, Greece, but this will probably change very soon again.

Mike: Well, having experienced winter in New York, I think you’ve made a good decision going back to Greece. So certainly for the winter. That’s a great move. So lucky. So talk a little bit. I mean, you said you were interested in really explaining data and information in words, how did that come about? And how did that lead to Baresquare becoming a product.

Georgios: So it’s very frustrating for people that are putting with a long hours to really understand what they’re what data have to say. It’s very frustrating when those insights they don’t know find themselves into driving action. But action, it’s more a matter of communication. It’s bringing people together and aligning their understanding. Now, every team every company is having people with different skills, and data and deep data science knowledge is not really a common language. Natural Language is a common language. So at the end of the day, we always had to rewrite what we’re finding and take out the detailed data science out of the insight for our misunderstanding. So, at the end of the day, no matter what was the analytical algorithm used to drive the result, the end result was always a couple of sentences, that the more that they communicate that the true meaning of the finding in business terms, the better. So we said, why not just go to the last step, and trying to recreate a new language that is successfully communicating those insights.

Mike: So it sounds like what you’re trying to do is almost cut out that data scientist, and let people who need to understand how to use the data, get information delivered in the language they use is that we are trying to do,

Georgios: I would say, this is how we are framing it today. Back then it was more about not having those pressures, insights slip somehow, and a natural language filled, the best way to communicate.

Mike: That’s great. So I can see how people would benefit, can you give us some examples of where bare square would be used, and how people particularly marketers might use it to help them in their jobs.

Georgios: Today’s turning data analytics on its head. And now we are not talking in terms of tables and numbers, but rather in terms of words and paragraphs, we take their traditional analysis that is done on dashboards, which most of the times is reactive. And we are using these AI powered insights so we can deliver to the right version, on the right moment in time, on the wrong way of understanding. The insight. Shown wereBaresquare has been particularly successful is in companies that are having a lot of data volume and complexity. You see, the more complex the data set, the more we thrive as a platform within that complexity, because that’s the pain that we eat is taking out that complicated and bit.

Mike: And so would a good example would be companies that are running large Google Ads campaigns and then trying to track the performance across the website is that the kind of thing people are doing.

Georgios: So typically, marketing analytics is the starting point. But if it is to look at experience from the user side, imagine that one morning you receive a Beresford notification on your phone before you go to work. And the platform is informing you that there was a sudden drop on the conversion rate, let’s say 20% on a particular product. And that has a reference point, let’s say that the reference point is, since the day before, our AI has investigated the issue and determined that the root cause is a recent price chains on one of your main competitors. That is what have made the product offering less compelling. Now, our platform not only can identify the problem, but also provide recommendations on how to address it. And an easy way of solving this problem is to enable a promotion, that is bringing down the price and making the product compelling again. So that’s how an analyst or a subject matter expert, a marketer, if you like they can leapfrog into action in the beginning of the day.

Mike: So that’s interesting, because, you know, you’re not only integrating over the company’s analytics, but you’re also looking at competitors pricing. I mean, are you having to integrate with a lot of different packages to pull that data? How does that work?

Georgios: So first of all, it’s important to highlight that Baresquare is not collecting data, we don’t have a tag, in other words, similar to what Google ads or Google Analytics is doing. And that is because we think that collecting data, it’s more or less a solved problem. What we are doing instead is to quickly understand what are their behaviours, see them in a digital journey that otherwise would have been blind spots within the dashboard. And then once we have that, that’s our starting point, then we can interrogate many different datasets. For example, we can pull in experience analytics, those datasets that really show where people are having a problem during their digital journey. Also, we have product analytics, or CRM data, which is more about if people are keep on engaging with the digital experience or the product and so on. Now, at some point, and especially with the latest technologies of autonomous, dedicated AI agents, we can really use those little guys to go and gather more contextual information, go and visit the website, see how the website is being rendered on a specific browser. And also, if the product availability of a competitor or the price, or even the weather, a public holiday, a promotional calendar, could also be part of the explanation of the root cause. And that is what is compiling the why as very much people in the analytics world, like to call it to really understand why. But what’s very exciting is that now we have a new era that is taking out the subject matter expertise. So even people who are adjusting the beginning of their career or in the beginning of this job engagement, just during the onboarding period of their lives, perhaps they don’t really know how they can influence the business goals or the revenue. And this is where an LLM can also provide very detailed personalised next steps on how to cross check the validity of the results, and how really, they can act.

Mike: That’s interesting. One of the things I feel compelled to ask is a lot of the examples you you’re giving, they feel quite orientated towards the business consumer market rather than B2B market. I mean, do you think the consumer market is further ahead in terms of using analytics? Or do you think it’s in a position where, you know, maybe it’s easier to get data because there’s a lot of online transactions?

Georgios: I think it’s because of the second, the B2B marketing still has a lot of ambiguous touch points. And many of these are within the physical world. Let’s think of an exhibition, a conference. Now, that part of the experience is not very much digital, or at least not yet. And therefore, there are many of these data points that somehow are being not captured. However, on the other side, what is unique with B2B is the longer sales cycle that happens many of the times, and these touch points, they can very much exist within a CRM database, where in the consumer market, of course, you can have CRM and look into the lifetime value of a customer. But it’s less of a complex problem. While in the B2B world, it is very much so. And also, I would say that the element of personalization, it’s way more important on B2B Cause the offering needs to align with the narrative of the business, the pain of the business, work with the ambiguity of all of the different data points that we’ve just talked about.

Mike: That’s interesting. So I mean, from that, do you think that actually B2B is got an opportunity in the future, to actually make better use of analytics understand data more, and therefore generate better campaigns? Or do you think that B2B is always going to struggle because of the longer sales cycle and the physical touchpoints?

Georgios: No, on the contrary, I think that companies that are employing B2B marketing will have way more of room to manoeuvre and provide new innovative ways to engage with customers. Taking that back to the example of the conference, think how underutilised The stand can be within a conference, think about how difficult it is to match someone who is interested for technology and a technology vendor and finding about correct person within the organisation that can explain the solution. Exactly for the needs of the customer. This is where personalization can go totally on that on the next level.

Mike: It sounds great. I mean, the one thing I’m wondering now is bare square is actually quite complex. It’s pulling in data from a lot of different places and using AI to produce conclusions in natural language. I mean, is this something that that’s a really very expensive product? Or is it perhaps more accessible to businesses?

Georgios: Better square is a company about is a startup which means that we are working on our go to market and we have found the best way for us to reach out To the market right now. And it happens to be that the best approach for us is to go for enterprise customers show also our licence and our business model is better optimised for enterprises. However, this is just a go to market approach. And it means that this is where we can prove our value in a better way. It’s not to say that it’s only that enterprises that they need this type of solution, because at the end of the day, it’s also a matter of their reference for an enterprise has an efference point of many analysts, many people who are working with dashboards or complex datasets on a daily basis. However, if we think about smaller organisations, those teams, perhaps they don’t have dedicated resources. So in proportion, with the complexity for them that they have to deal every day, it’s as Ben, I would say that the application of AI powered insights go across all different types of businesses. And even for us as consumers or people by ourselves, I can totally imagine solutions that are operating exactly with the same logic, and they are applying to our everyday lives.

Mike: I love that that sounds like a bit of a hint as to where you know, possibly in the future, you might go moving from the enterprise level down to more mass market. I’m interested there any other features that you’re thinking of you’d like to add to Baresquare, to you know, either help your customers or improve the performance,

Georgios: I would like to take you straight into what excites me the most. And that is, for some reason, session replays. I don’t know what kind of beef I unconsciously have with session replay. But perhaps is the facts about where it comes to data collection. And how we are utilising these datasets. Perhaps these technologies that are capturing absolutely every move of the cursor is where I see that we are under utilising the dataset. In a way, I think that is more like a graveyard of data points that are being utilised only as much as people have time to stand in front of a screen. And looking into session replays. Of course, you have already some solutions. The best one that people might know is Microsoft clarity, and their experience analytics platform of rds using GPT technologies to write a quick summary of what happens into that session. However, what I’m flirting with, and I’m talking very strongly with my product team is whether we need to proactively record absolutely every session, or is it something that we just need to do on an ad hoc basis, or even more just employee agents to go and we create a session with all the different paths that a user could take, and then use these NLM technologies just to summarise where exactly that problem became a hurdle for the user.

Mike: That’s fascinating. That sounds like not only you’re going to save some poor marketers from watching session replay after session replay, which is not the most fun job in marketing. But also you’re actually going to be able to preempt those potential problems. If you have the AI navigating around, then presumably you can see problems before even your customers encounter them. Absolutely.

Georgios: I would say that it’s definitely something that exists within our roadmap. But then again, going back to the state or for where we are as a technology and what we need to introduce first, I’m afraid that it’s not the absolutely next feature that we are going to bring to life. But definitely it’s it’s the one that could generate this biggest change when it comes to when and how we collect data.

Mike: I love that Georgios. So love the the dreaming about what might be possible in the future, rather than just telling us about an incremental feature enhancements. So that was a great answer. There’s a couple of questions we always like to ask people say, one of the things I’m really interested in Georgios says, What’s the best bit of marketing advice you’ve ever been given?

Georgios: That’s a good one. That’s an excellent question. Well, first of all, I think that it makes sense to do a little bit of definition of what marketing is. And for some people Will like myself that I was taught marketing in the beginning of early 2000s. Marketing was all about Kotler and the four P’s, price, promotion, placement, and product, of course. And then we have the more contemporary definition of marketing, where people these days, they think of marketing as the overall go to market most. So finding the exactly the needs, and matching them with solutions. And this is where tools like the business canvas, that approach and all other contemporary ways that we have to understand the business, they’ll notion that I really love the most when it comes to marketing, is thinking of marketing being all about finding ways for people to find him. So I think it applies because both has consumers, buyers or working on on behalf of a company, we really like to have the sense of freewill. Now we can debate on a philosophical level, whether free will exist or not. But we all love that sense, especially in the in the Western world. And always we operate within the limits of our understanding of our current pains and interests. And now we would like to have the sense that we are driving the show, we would like to solve a problem whenever we feel that that problem deserves our time and resources to be sold. And it’s only natural if you think about it that in the past few years, we have seen this rise of self serve product lead approaches. So marketing, it’s all about discovering where to leave breadcrumbs for the right people to discover you. It’s a more natural, organic, perhaps even a respectful and fun way of contacting marketing.

Mike: I love that I love that approach of it’s not about pushing it to people, it’s about helping them come to you. And I think, you know, that probably reflects a lot of the sort of theories around inbound marketing, for example, that, you know, has been very effective. Certainly, you know, a lot of HubSpot users are really into it. Of course. My other question, George is around advice for young people in marketing. You know, we’re very keen at Napier, to get young people into their marketing career. But what would be your advice to a young person if they were wondering whether they should go into marketing or not as their career?

Georgios: This is very relevant. And I’m thinking a lot about it. Just because I have a nephew, who just decided that he would like to go and become a developer, he would like to study computer science. And in the same week that He’s declaring that we have Devin AI, just coming in and claiming that something like one out of six different types of tasks that an engineer needs to solve with gold today, perhaps there is an end to end automation for these weights, poses the question, Will marketing still be relevant for people that are young right now and they are kicking off their career? So I was thinking that a lot. And I think that people should absolutely pursue a career in marketing. And let me tell you why. In essence, I think perhaps it’s not the popular opinion. I mean, I would love to hear yours. But I think that marketing, it’s all about competing over limited resources, it’s always going to be there. And people are always going to be competing for finding a better way compared to everyone else, to reach to that audience. So in a way, marketing will never go out of fashion. Perhaps people should be informed about what they think that marketing practices are today, the most certain thing is that those practices will not be the same in the future. But the overall idea, the fact that we try to appeal to words of the limited time and attention that people are having and be better than everyone else, this is going to stay.

Mike: I love that. And I personally think you know, marketing could become a lot more interesting as a career, particularly as AI takes out a lot of less exciting jobs, like sitting and watching session replays a great example of something that I don’t think any marketer can say they enjoy, but sometimes you have to do it. So I agree. I think there’s huge opportunities in marketing for a career. It’s going to be tough. I think it’s going to be more competitive, but so well, everything else as AI comes in and impacts it.

Georgios: How do you understand AI and marketing these days, Mike?

Mike: It’s really interesting. So our business is actually quite niche. So we focus on deep tech business to business clients. And AI is less effective in very specialist areas like that, presumably, because the training data is much less than in more general areas. So we’re using AI in all sorts of different ways. And I think the reality is, is that actually everybody’s using AI in marketing, if you’re using Google ads, there’s AI tools in there that Google have implemented. And I think that’s quite a good model. You know, people, when they go to Google ads, don’t think about using the AI, they think about using the tool and the AI is kind of hidden away. And it just does good things, it just optimises your ad, and you can be at home sleeping, and Google is still optimising your ad and still using AI. And I think that’s a very interesting model going forward that more and more, you know, my belief is we’ll see AI as a separate standalone thing disappear. And I actually think what we’ll see more and more AI just get buried into tools. And so when you’re using a tool, more will be done for you automatically. But you won’t think of it as using AI. So definitely, that there’s gonna be issue as to how many marketers or computer programmes or anyone else we need in the future. But I don’t think AI strength is actually replacing people completely. I think it’s replacing people on the very repetitive, the straightforward, the less creative jobs, but I have no idea whether that’s you know, 10% of marketers 50% of marketers or 90% of marketers will be replaced, it will be interesting to see.

Georgios: And that is quite a lot to unpack. But if it was to, to offer one thing that I found very compelling in what you said, it’s also the the zeitgeist of these days, we think of AI as an entity by itself. And it’s, it’s crazy, it’s like thinking knives as an entity by themselves. It’s like if people versus tools or people versus knives is a thing, which is not. At the end of the day, the value is created by people. And it’s being accepted, endorsed by other people shown. AI is just just another tool. And of course, this is something that all of us believe, and we rationalise it like this. But when it comes to the most emotional responses, we still respond as if we are talking about a replacement. So I was thinking quite a lot, perhaps we need to start rebranding AI. And thinking it less about technology, and more about a vertical, perhaps, where we unpack the human needs in a very deep way.

Mike: I think that’s interesting and probably more reflective of the truth of AI Georgios but I do wonder whether there’s so many companies that are trying to sell this dream of AI replacing, you know, as many workers as possible, that actually perhaps overhyping it is in their interest at the moment.

Georgios: And also, I wonder how this could also become a self fulfilling prophecy, the more that we are thinking AI as a replacement. And the more that we start marketing, AI as a replacement, the more it can become a replacement. So a few years back around 2018 2019, I joined a conference when it was all about policymaking in AI. And the discussion was all over the place with the famous, dynamic or far or whatever an autonomous car should do in case of absolutely having only two paths forward killing one person or killing a group of people. There was a lot of debate when it comes to that. But at the same time, Cambridge Analytica was operating, and almost publicly announcing the way that they are breaking down and creating micro segments based on the Big Five ocean cycle metric classification. So those guys were already talking about their methods. And at another point in the world, we are having a discourse when it comes to all what an autonomous vehicle should do. Now, my point here is that there is a matter of sequencing. The next problem that will we’ll try to solve or understand or perhaps create a blueprint. And a few years back, we had the option to start thinking about data Schumann Nolan’s watch going on with intellectual property. And of course, should be earning some sort of commissions or having at least intellectual property owned or what has been written. And we didn’t do that, we still like to be talking about the autonomous car dilemma. And it feels to me that we are doing the same thing right now, we can be just thinking about how to apply AI in such a way that we can enable more people to pursue their dreams, even if they don’t have the capabilities. Data driven marketing is a thing, but not all people understand data. And we have people that really would like to change the world. And they have an idea that they would like to communicate to the world. And AI for them can be the absolute tool to overcome their lack of competence. But if we don’t think as such, if we don’t think about how to roll out these solutions, in a way that we can empower people, and we create more and more narratives of replacing people, then eventually, the solutions that we will create, will be replacing people and making them obsolete. So I think that there is something to be said about that self fulfilling prophecy of AI. If we think it in a different way, the future can be different.

Mike: I think that’s super positive and a great way to end the podcast, the thought that, you know, what we should be doing with AI is helping people achieve their dreams, not trying to replace them. And that’s what’s going to generate the best product. So thank you very much, Georgios, it’s been amazing if people would like to, you know, learn more about Baresquare, or, you know, indeed ask you about some of the things you’ve said on AI, what’s the best way they could contact you.

Georgios: Go on the website, baresquare.com. This is where we are going to be keeping people up to date with what we are doing. And I’m trying to become more and more active on LinkedIn. With the time that is being allowed to me it focus on that extra version, while at the same time, we are preparing for a really big product launch of our new platform. As I told you, it’s trying to enable as many people to go out and explain to the world what they are doing and why they think that there is a solution, product or idea. It’s worth the attention.

Mike: That’s fantastic. And I certainly look forward to the launch of the new platform. And I’m sure a lot of people that don’t enjoy data, but love marketing are looking forward to it. So, Georgios Thank you very much for being on the podcast.

Georgios: Thank you.

Mike: Thanks so much for listening to Marketing B2B Tech. We hope you enjoyed the episode. And if you did, please make sure you subscribe on iTunes, or on your favourite podcast application. If you’d like to know more, please visit our website at Napier B2B dot com or contact me directly on LinkedIn.

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