In this episode, we talk to Adrian Mitchell, the founder of Brijj, a data management platform for data teams. We discuss the importance of taking a research-led approach to validating the business idea and how that can help get early adopters on board.
Hi. My name is Matthew Todd, and welcome to Inside the Scale Up. This is the podcast for founders, and executives in tech, looking to make an impact and learn from their peers within the tech business, we lift the lid on tech businesses, interview leaders, and follow their journey from startup to scale up and beyond; covering everything from developing product market fit, funding and fundraising models to value proposition structure and growth marketing. We learn from their journey so that you can understand how they really work, the failures, the success the lessons along the way, so that you can take their learnings and apply them within your own startup or scale up and join the ever-growing list of high growth UK SaaS businesses.
Hi, and welcome back to Inside the Scale Up podcast today. I’m here with Adrian Mitchell, great to have you here Adrian, morning.
Lovely to be here.
And yeah, as always, I always like the guests to introduce themselves and introduce their own business. I’d like to kind of have our audience here as they would kind of give it an elevator pitch. So over to you tell us about yourself and your business.
Brilliant, and no problem at all. So, I am Adrian Mitchell, and I am the co-founder and CEO of Brijj.io. So, what Brijj.io is, it’s a work management platform for data and analytics teams. You know, the simple way I like to let people know about it is that it’s effectively just like JIRA is for software developers, Brijj is for data and analytics teams.
So the problem we’re trying to solve is, is that in the UK alone, 24 billion pounds is spent on data projects. And you may have seen this statistic bandied around all over LinkedIn, 80% of data projects actually failed. And that’s a staggering level of failure. And it’s, you know, a staggering level of potential waste.
Now, why these projects fail, we believe in our research shows that the primary reason these projects fail is because the way that data creators, so data analysts, bi developers, data scientists work is not efficient when working with business users. So your marketing managers, your sales directors, and value in data analytics projects completely depends on that connection between data creators, and data users. You know, a simple chart has no intrinsic value, it only has value when it’s used by a business person to make a decision, create an action, or create an outcome.
Now, our platform, make sure that every step of a data project works for both data creators, and data consumers make sure they are connected, and operate and work effectively together. As effectively my business. I see fantastic, I think, yeah, I’d love to learn more about what that looks like in practice, you know, some of the benefits that it can bring.
But before we kind of get into that, you know, I always like to start and, and figure out kind of what led you to co-found this business in the first place, you know, what was your background? Where were you coming from? And you know, what led to the idea, and then the decision to actually try and take this forward?
No problem. So I actually, funnily enough, I fell into data analytics, I did criminal justice and sociology University. And obviously, in when you do sociology, there’s an aspect of research in there as an aspect of data analysis. And I always found that quite simple, quite
My early career, I was actually in recruitment. So I was, I was recruiting students, actually international students into universities. And just by sheer luck, and just coincidence, I was working for a business called Hobsons, which is part of the Daily Mail and general trust. And they needed a data analyst. They asked around the office, does anybody know how to use Excel, Microsoft Excel,
I put my hand up. And then from there, you know, fell in love with it, and worked my way up to becoming Head of Research and reporting in that business.
And then obviously, worked for a number of different corporations like Allied Irish Banks in Ireland, there’s Trust Bank in Northern Ireland. I’ve worked for capita in the UK, which was an experience. I’ve worked for students, and
I’ve worked in every single area of data analytics. I’ve been a bi developer, Data Manager, been responsible data governance. I’ve, as I mentioned, being a head of research and reporting, and I’ve been an analytics development manager. So I’ve seen it all. Yeah, actively dying all over the 1516 years, I’ve been in the business. And so what that’s done for me is it’s helped me understand that experience firsthand how difficult it is, firstly, to do the job, and secondly, to add value to a given business. I mean, I can’t tell you how many times I or one of my team members has created what we would class as you know, a beautiful piece of animal analysis.
You have a really intelligent model, let’s say a really effective piece of code. Yeah, an example. And worked. You know, what that phrase our butts off on this, only to find out the business haven’t used it didn’t understand it, or simply shrug their shoulders and says, So what?
Now this happens this, this has happened, it does happen. In every business I’ve been in. You know, I’ve got a lot of contacts and a lot of friends in this industry, and they have the exact same frustrations. So I know that this is a huge personal problem for data analysts and analytics professionals, which leads into that level of failure across, you know, different businesses.
Yeah, I see. And what are some of the reasons that, you know, those other stakeholders in the business, don’t manage to extract value or, as you say, just shrug their shoulders and say, so what?
Yeah. So it fundamentally comes down to the way that a project is started. And the way that a project is managed, so too often, a business user will ask for a specific output. So they’ll say, I need a chart showing sales by region over the last four years. Let’s keep it simple and obvious. Usually, it’s much more complicated than that. Yeah, let’s keep it simple. The problem with that is, is often the data or in information they ask for, is not actually the best piece of data, or the right question to actually create an outcome.
So, what tends to happen is data teams often quite transactional. They’re like a, they work like a service desk, right? A request comes in, they do a git question for a, they answer, they deliver, and then they move on. Yeah, right. When the truth is, you shouldn’t even really be asking business users what the question is, you should be asking them, what is the outcome they want to achieve? You know, what’s the business decision or the thing they need to do? At the end of this project? Okay. Because they are data users, these are people in the business often have absolutely no idea, the sheer breadth of data that business itself holds. Yeah. You may have experienced in your past, but you know, it’s the they either don’t understand what the business what data business holds, they often ask for data that the business doesn’t have, as well as what generally happens and then they look at you like, well, I need that data. So give me that specific data.
Adrian MitchellThe reality is if you find out what a person needs, what the business outcome is, and you work well with business users, your chances of success and actually creating a piece of analysis, which has intrinsic and tangible value for that business user, massively skyrockets.
And so that that there sounds incredibly simple. But trust me on this, so many businesses fail at doing that very simple thing. We’ve created, a methodology and their platform, and a piece of software that makes that incredibly easy to do. And makes it a basically, part of the process. You may have heard of opinionated software before they’ve ever heard of that.
So yeah, so. So just for listeners, opinionated software is software that actually has some rigidity built in, you know, how software often wants to create a lot, you know, you can create your own workflows, you can create your own way of working. opinionated software actually does the opposite. It has a lot of flexibility, but it has some opinionated workflows, which you cannot deviate from. That’s exactly what we’ve done with Brijj, because we know that asking questions in the right way, delivering the project in a structured way, eliminates those mistakes that so often made. So I had to ask the question I
Yeah, that makes a lot of a lot of sense about the Yeah, the problems that those businesses see, and the reasons that they’re not able to extract the value that they’re looking for. Because they as you said, they lack the knowledge, the experience to approach that in the right way to have the right processes, methodology, and thought process behind it. So yeah, obviously, then your background, you know, enabled you to see that from every angle, really appreciate that problem personally, how did you then decide that this was a problem worth solving with your own platform? What was it that kind of led you to try to start that as a business with confidence?
Yeah, so for me, I think I think my background actually helped me with this because as I mentioned, I’ve been heavily involved in research obviously, my whole career is based around data so I was never going to start something without making sure I was gonna collect the data to make sure that what that my assumptions and my my experiences tallied with the wider reality.
I mean, don’t get me wrong. I had an awful lot of anecdotal evidence so that this problem existed. But I’m just effectively one data point. And my contemporaries and my colleagues and people I know in the industry are a few other data points. So what I want to do is I wanted to bring in more data. Yeah, only to bring a lot more data. So the first thing that we did to validate whether or not this problem existed, was we ran a piece of research with 700, what we termed white-collar workers, so office-based workers. Yeah. So we. So it was to say, we got 700 individuals. And we didn’t actually decide to split that 700. Between data creators and data consumers, what we did was we randomize 700 individuals because we wanted to find out what proportion of people in the industry classed themselves as data creators and data users, that was the first thing we needed to do.
Because, again, we had an assumption that there was a certain number of analysts in a given business. Yeah. But we needed to find out what the truth actually was. So that was the first thing we did, we started asking individuals, you know, a set of questions that were not specifically, you know, stuff that we absolutely needed to make sure we didn’t lead them down a path that we wanted, you know, we wanted to take bias out of our assumptions. So we asked them simple things such as, firstly, I said, Are you a data creator? Or a data consumer? Do you class yourself as such?
Secondly, if you are a data consumer, what systems do your data creators in your business use to manage their projects? How often? Do you feel that your business gives you the data that you need? When it doesn’t? Why doesn’t it? You know, all these different questions around their experience. And then with data creators, we asked them, what systems do you use? What problems do you have? Do you have any problems? How successful are your projects? How often are they successful? How often are your projects actually used and utilized by the business? So this gave us an understanding of whether or not our assumptions were correct about the industry as a whole?
I see them. And where did you source those kind of 700 people from? Was that a personal network?
Yeah, so at first, we thought we could get that sort of n number just through, you know, distributing it to people we knew, yeah. And using, you know, LinkedIn as a social platform to try and get those submissions. Yeah, now. And that wasn’t successful in terms of getting the sheer number that we needed. And, you know, during the process, we realized that actually, we were instilling some level of bias into our sample anyway, because these were people that were either directly connected with us or second connections, so to speak, to use LinkedIn terminology, which means therefore, that we’re going to put the group of individuals that have given our assumptions into that data set, I say, there is a mistake.
So we shouldn’t have done that. So what we did was, we went out to an IC went to an external source. And, you know, let them know, the makeup of the group that we wanted, told them, we need to be randomized. And then they went out and sources individuals. So we didn’t know any of these people. Personally, I see, which I think was a was, in hindsight, the obvious and smarter choice to validate in the first instance.
Yeah, I think that’s a good point about bias. You know, I think a lot of startups and even at the scale stage here, we always instill the importance of doing that kind of research on an ongoing basis. But I think you can assume that LinkedIn might seem like an easy platform to get in front of a number of people. And it certainly is a platform we recommend people do utilize, but I think you’ve got to consider what you’re trying to get out of it. Understand that, yeah, your connection pool and second-degree extended network is going to be made up of a particular audience, isn’t it?
It is absolutely going to reflect you and your opinions to some degree. And that’s, you know, just to forgetting my particular startup, but in startups and scale-ups in general, that’s a really big mistake to remember the name of the things with a mum test, right? Where you do not ask somebody you know, about your business?
Because they will, they will not and cannot give you an unbiased opinion. So I feel like that’s what we were initially going to inadvertently do. And I’m glad and I’m glad that we ended up not doing that. And again, that was a stroke of luck, because as I said, we just weren’t getting the results we needed. So we had to look for a different, different method. Yeah, but that wasn’t where we were. We stopped with the research though. We did that research because we wanted to see whether some of our underlying assumptions about the wider market were true.
But then what we need to do really is we needed to talk to our, you know, our potential buyers, our actual customers to see whether or not what we plan to build would have actual value to them. Yeah. So. So what we did in that instance was we sourced I think it was 20. I think it’s 22 Analytics leaders, so to speak CIOs, analytics managers, Director of analytics director of BI that sort of level across the English-speaking world. So if you’ve Australia, we’ve had some Canada, the United States, obviously, the UK, and Ireland.
And what we did with them was we asked them some similar questions to the test group of 700. But this was more around, you know, what larger strategic problems they had when they have failing data projects? What effect that has on their team’s morale?
You know, what platforms they use, or platforms that their teams feel, are useful session not useful? Yeah. And then what we did was we mocked up a effectively, what was a one minute, explainer video, really, that we that we procure was completely and utterly made up. But it had, it had what we perceive to be some of the potential features and attributes of our application. And you’d have the benefits more to the point that it was going to solve. And so after we asked these people, all of these questions about their experiences, we then the net, so they were doing this online, obviously, they clicked a button, and an explainer video came up.
And it was like, What do you and we set them? What do you think this product? Does? We simply ask that what do you think it does? Because we didn’t want to tell him what it does. We want to see whether or not the way that we’re articulating what it does made sense to them.
Yeah, that’s interesting is yeah, how are you getting across what you intended to get across? But I suppose also, you know, they’ll come with their own set of problems and challenges. So I guess you’d probably start to see a little bit of water there, perceptions and how that might be useful and extended to their particular situation.
Exactly. We did that we assess precisely at best, we did that. Because we wanted to firstly, detect the problems or absence of problems, actually, you know, some of these respondents, I’ve got any problems? Yeah. So get that into the forefront of their mind, show them something and see how they’ve interpreted what we’re showing, based on what they’ve just literally been considering? Yeah. Does that make sense? And I had different very different views of it. Some of them saw it as a pure project management platform.
Some of them saw it as a collaboration platform. You know, some of them, as I mentioned, saw no need for it. Yeah. And, you know, articulated why they thought this was a ridiculous idea, so to speak. And it was incredibly useful piece of like, qualitative information for us from leaders and people who held budget, people who make these decisions for their businesses. And before, before we did that piece of research, we decided that if we had a certain response rate of negatives, as in no interest in the platform, I don’t see how it solves any problems, wouldn’t theoretically buy it, we were going to cancel the whole project.
They were just not going to be we’re just not gonna go forward. Yeah. I remember the precise figure, but we decided that if a third of the people said they did not find it interesting, okay, then we would cancel. That’s less than half obviously. But the reason for that is because in our previous recession, and, you know, just in our talking to other entrepreneurs talking to other business owners, there is a big difference between somebody saying I would buy something, and somebody saying that and somebody actually paying for it.
Yeah. When was that? The question you asked? Would you buy this?
We said we said, Would Yes. So effectively, if this platform delivered what you believe it to deliver? Would you purchase a platform like this for your current business? I think that’s what we asked. Yeah. And so by asking that, for your current business, it gives us an understanding of firstly, what they buy. And secondly, is there a need in current businesses for next profit? Because again, if you ask somebody, would you buy this? Conceptually, I would buy it if I needed it.
Indeed, that’s very different to current situation relevant isn’t
Exactly, exactly, yes. So we need to find out. Would you buy it? And do you theoretically need it? Yeah, I mean, it’s very again, again, there’s always some ambiguity. There’s always some risk in these pieces of research because again, you don’t have a product because they’re not having to sign a check. And you cannot, it’s very difficult to eliminate that at such an early stage before you Just for you really start? Yeah, that’s what we did. And, you know, talking to you a part of incubators, were part of some accelerators, and talking to other business leaders and talking to the people who run these incubators, we did a higher level of research than most other businesses do.
But I, you know, again, like I say, my background meant that that just was an impossibility for me, I wouldn’t be able to do it without doing it. I’d have. I’d be too scared. I don’t know. I’d have been too nervous to get to go ahead. But I’m glad I did. And the vast majority of our experiences so far have tallied with that research, which has obviously been really, really encouraging.
Yeah. And in that piece of research, were there. You know, surprises, were there things that you weren’t expecting the, you know, their perception of the products, you know, where that differed to your assumptions.
So, I mean, the biggest, the biggest surprise for me actually was actually in the piece of best piece of research with the 700 individuals. Because if you split the two respondents between data creators and data users, yeah, the number of data creators who said they have a process and a platform in place to manage their projects, was close to say, 80%. Yeah, they said, Yeah, we have something, you know, actually Google Forms, it could be something really basic, like an Excel sheet. But they said they had something. Then when you asked data users, it was something like 10% said there was a process now. Wow. So so so we so what we saw there was okay, so it seems like lots of businesses have something from the customer’s perspective. They don’t.
So there’s, there’s a, there’s a huge problem there, on the customer side, actually not on the data creators. So they gave us some really insightful stuff. But with the, with the piece, the piece with the, you know, the data leaders, the 20 odd people a bit that the bit that surprised me the most about that was actually the ease at which they understood what it was we were trying to do. And how many of them said, we don’t have something like this. Okay. So the reason what the reason why that surprised me was because, again, I think that the research showed and indicated to me, that people on the ground data creators, the people doing the coding the people doing the actual work. Yeah, they do have processes in place.
But again, the people higher up, the leaders, the people at strategic level, are not aware of these processes, which means to me, right at these processes, as they are currently set up, are not solving strategic level problems. Because if they weren’t solving strategic level problems, those people at the CG level would be aware of them. Yeah, yeah. Give me that give me another piece of confidence and real insight. That’s the platforms that are currently being used or seen as tactical. As soon as they see this process. They’re not seen as solving the strategic problem. And as stated, promise, we define it is that relationship between the creator and data users? Because without it, you cannot deliver value.
Yeah. And why do you think that is? Do you think that’s because it’s relatively new field? Are the existing products on the market that address that, but they’re not just aware of them? You know, what does that kind of landscape look like?
I think it’s because the vast majority of analytical professionals, unfortunately, are quite tactical in their outlook. They’re quite, they’re quite process-driven. Yeah. So let me give you an example. Let’s talk about JIRA. So Atlassian Jira, which is probably the most well, certainly, in the past, and up to this point, the most popular project management tool for definitely software developers, and for the vast majority of analytics. They know of JIRA. They know it does a job. And they, so, therefore, the default position is okay, if we’re going to spend any money on this JIRA is JIRA is the choice? Yeah.
The problem with JIRA is like I say, it’s a very, it’s a very process-orientated piece of software, which predominantly provides huge amounts of value for the actual creators, you know, the software developers themselves. It doesn’t focus and consider the fact that the connection between technical and non-technical users is absolutely imperative in the data analytics space.
Okay. Okay. So, because data people often have software-type backgrounds, because they’re often mathematically minded, they did they defaulted. The folks at Jira, I don’t even consider the strategic level consideration. I still don’t consider the fact that their data users are never ever going to go into JIRA once I look at it.
Yeah, I can’t tell you how many times I’ve tried to implement JIRA or Jira, like piece of software into a business tried to get say a sales director to update it. get submitted ticket or to respond to a comment. They’ve opened it, seen all the options on it and users. I’m emailing Yeah, yeah. And what that does is effectively means that you have a system in place that you want to use to better manage your projects, but half of the individuals need to be an intricate part of a project for us to use.
So therefore, it has no, it loses a huge amount of its value. That’s, that’s the key problem that I that I find in this industry. The analytics, people love Jira, because it doesn’t real job for them. It does an incredible job for them. But it does a terrible job for their customers. And without that connection between them and their customers, they have such a higher chance of project failure and a lower chance of project success.
No, I think that’s, that’s really interesting. So you’ve got a market where the tooling decisions are being driven by like half of the users, as you say, and they’re, they’re not the strategic ones. They’re more tactical. And I guess from your business perspective, they’re also not the people with the purchasing power directly themselves as well.
Often yeah, that’s, that’s a real, that’s the real key. So the people who are going to be using our software and are the ones buying? Yeah, yeah, that’s, that is actually one of the really interesting aspects of our business, but also one of the huge challenges of our business. Because what you tend to have is, you tend to have almost an adversarial relationship between the user and the person buying.
So, for instance, if we talk to a director of the AI, or we talk to actually one of the leaders on the consumer side, so they’re not the technical teams, they’re not the creators, they get what we’re trying to build. Yeah. And they get what we’re trying to create, and they like this, this, we need this, we need this. And then you get to, let’s say, the head of BI, or the lead bi developer, and he’s like, I want to use JIRA. Yeah, you see what I’m saying?
So it’s a very key slash important conversation to be having early with both of those individuals that the strategic-minded person and the tactical person so that they both understand why we’re doing this. And the way that we tackle them is that we actually talk in practical terms to the strategic person we talked to, we talked to the tactical person in emotional terms.
And what I mean by this is, we talk to the BI developer, let’s say, we say how many times do you do a piece of work or create a BI dashboard, we’ll have to spend hours updating the dataset, only to find out and I was logged in for three months. Yeah. And they and they say it happens all the time. It makes me so angry. And I say, Do you know why that’s happening? Because it’s certainly not happening. Because you’re bad at your job. Is it? I mean, you’re fantastic at your job. You’re doing wonderful work. You’re working so hard. You’re delivering, exactly to spec as it’s been defined to you.
So because we know you’re great. There must be a prompt somewhere else, right? It must actually be with the spec in the first place, it must be that you’re answering the wrong questions. That’s how we talk to them and try and turn their viewpoint around from using one of those as your DevOps or Jira, one of those big players. And that’s the real challenge. And it’s actually been one of the biggest challenges we have. But it’s also completely understandable in some way. Yeah, we’re still working on but luckily, doing quite well in turning that around.
Yeah, and I think that’s really interesting. And especially when selling b2b, you’ve got to bear in mind the different stakeholders within those businesses that are going to have an opinion. And I think you’re absolutely right, you do have to treat them differently and speak to them differently, because their perceptions about the challenges within the business and their role will be very different.
Yeah, no, I can imagine that you come to that situation with a lot of kind of strongly held assumptions and beliefs. But I think the good thing about the approach that you’ve described, and you know, because of your background, is that you’re willing to put that effort and actually not even willing, you kind of saw it as a necessity to put in terms of research to do.
I mean, I imagine if I’d have not done that and got over-excited with my idea, and then found out like it, I was completely and utterly wrong, I’d have been banging my head against the wall. So I just, I just couldn’t do that. And so that would be like, my biggest recommendation, actually, at this stage. Remember, though, bear in mind, I’m still a startup. Right? So I haven’t, you know, so to speak, made it or not, I’m not, you know, I can’t speak from that position. Well, I can speak from a position of, of being in in the storm, so to speak, I’m in that difficult stage, I mean, that one working, you know, 70-hour weeks. And that would have been significantly harder and significantly more stressful than it is if I didn’t have that research to back it up.
Yeah, you know, that that research often comforts me. Because what because, you know, there’s, there’s many, I’ve experienced it and everyone I speak to my incubators, experiences, where you, when you start sweating you thinking, have I made a terrible mistake here, this is incredibly difficult to do. And you need anything you can find to get you back on that path and to keep you motivated, and keep you solely focused on your vision and mission. And for me, that research was that piece. So yeah, that would be a massive piece of them recommend record. That’s a recommendation of mine. For anybody who’s looking to get into this,
I think that’s a great recommendation, one that I would echo and I think, as you say, you know, finding that from, you know, the right kind of scale and type of research versus just asking a couple of close connections, and to get one of them to say, oh, that sounds nice, you know, a lot different in terms of quality of research, isn’t it?
Yeah, it’s, it’s almost, it’s almost like you kind of want to avoid people who are going to be nice to you, when it comes to finding out about your business. I think I’m maybe articulating that incorrectly. But that it’s a real mistake to talk to people like you, or who like you, when you’re starting a business because they will tell you they’re being honest and truth. And sometimes they are and sometimes they actually spot on, I’m sure. For many massive successful businesses were like, that sounds like a fantastic idea.
For instance, Facebook, I’d argue that probably people went incredible. And they were correct. But you just can’t, you just can’t, you can never know. So ask the people who have no vested interest in you. Yeah. Who will look at it completely, from a selfish perspective.
Because every single one of your potential customers is going to do that when it comes to the day of selling this thing. And, and, yeah, so that’s, that’s why I’m really good about that research. If, let’s say, you know, 510 years down the line, I’m starting a new business, and I’m doing a new venture or something. That’s, that’s the one thing I always do is research to, to a huge extent.
Yeah, absolutely. So, you know, then with that second piece of research done, you kind of got that confidence that you roughly know that it’s worth building something and what that should look like, how did you then kind of manage that initial build, and then acquisition of those, those early customers?
So the next stage after we finished a recession, we you know, we called it our go decision, we said in the beginning, we’re going to go for this, we’re gonna we’re going to do this effectively involved, understanding exactly how we were going to develop a piece of software like every wants to build. Now, as I, as I mention, I’ve got huge background on the technical side in data analytics, but I’m not a software developer. I don’t have the ability to build a SaaS app. Yeah. So to speak.
So we knew from the very beginning. And again, this was this is part of the equation in our mind whether or not this is worth it, that we were going to have to source an agency at the very least, or try and bring on some kind of technical talent. super early. Yeah. Now. Luckily, luckily for me, and this is something that other people some of your listeners may not be lucky enough is that my co founder is independently wealthy.
Okay, so I didn’t have to what I didn’t have to consider, like other people do. Funding rounds in the early stages. Obviously, I had to, I had to convince him, yes, that the business and had to pitch to that one individual. And I didn’t have that as a consideration. But what it also meant was, is that I knew I had a certain amount of money from the very beginning so we could afford an agency as an example.
So we decided to go down the agency route because we we did not want to have to deal with things like you know, bringing on a CTO as a co founder, negotiating on, you know, their share of the bill. Business yeah, having to deal having to deal with them on a personal basis, managing their expectations, all that sort of stuff. We don’t get distracted by that. So we went for an agency to do that.
So what we did was we, I think it was 15 Different agencies we reached out to well. And before before we’ve done that, again, this is based on my background, I’d already created an entire spec platform on confluence, JIRA, Confluence. Yeah. So I’d written out everything that we wanted to potentially achieve. And I split that out between, you know, must have should have could have, you know, nice to haves, that sort of thing.
And so effectively what this was, this was a gigantic repository of what we want to build, including, logic diagrams, all sorts of stuff like that. And now, obviously, when you’re building a product, you want to go down the MVP route, you want to you want to build the minimum, the minimum thing that’s smallest product you can because it saves money, saves time, helps you validate as this the lean, lean methodology, so to speak.
But the reason why we wanted to put everything on there is firstly, because it was useful to scope out what our grand vision, yeah. But secondly, it helped us understand which agencies would read it. And which of them were strategic and business minded in their focus. Some of them would just say, yeah, we can we can deliver that. Here’s a here’s a piece of costing for the entire thing. There you go. Yeah, didn’t consider that. Didn’t even consider the idea of an MVP didn’t didn’t consider the idea of, of doing this in a modular, you know, agile method.
They just were simply out for our cash. Yeah. And so so that helped us validate them. And some of them were the polar opposite. And they said, Listen, we don’t think that you should be doing everything here. I think you need to prioritize that’s useful help seven discussions. But then some of them told us, we understand your business vision. We believe this is the first bit you should lead, develop, do this in the first month, do this in the next three months, and then work from there. Those are the ones that we move to the next Conversations.
Does that make sense? So we almost wanted to create a test for the agencies because their businesses, they want to maximize their revenues, we understand that there’s nothing wrong with that. But we need a we need a way of validating whether or not that agencies firstly going to do the job. And secondly, whether they’ve got the right mindset.
Yeah. Are they capable? Are they just transactional in nature? Or are they actually going to help support you with that vision and see the bigger picture and value have a longer term relationship?
Precisely. And that was essential for us, right? Because I was going to operate with some somewhat of a project and Product Manager during this during this build. And so I needed to be sure that when I say something, they’re going to be able to interpret that not just as a technical specification, but correlate it with my business need. Yeah, that makes sense. So you know, that when I’m talking about how the data creators and data consumers need to work together, in this relationship, I’m like a consumer.
And if I ask them to do something, like build this module for me, and they just build it without considering its, you know, it’s actual practical application to my business problem, I am going to build things that make no sense, I’m going to make mistakes. So I need those technical individuals to have a business mind. So that they can effectively work like a CTO within my business, and deliver for me. So ultimately, you know, it took us two and a half months to choose our agency as a long time in the startup world.
But, you know, there’s some decisions that you absolutely must do everything to get right. You know, that your essay hire slow fire fire fast, right? People say that. So we, we hired slow, because we wanted to try and get it right. And so often loads of conversations, loads of back and forth loads and negotiations on cost. And that’s, that’s how we chose our MC. That’s how we developed the product and got the technical expertise in to develop that product. So that was, that’s the journey that we took on the technical build side.
Yeah. And I think that’s a good a good approach of taking on like, you know, you’re kind of thinking behind, you know, giving those agencies a test and, you know, evaluating them, you know, as to how they’re actually going to work and support your business. Because as you say, Yeah, many of them will just either not be capable or Saviola not will just be interested in that. Cash. And I think, you know, once you get to wanting to build something, you know, I’ve seen it quite a lot that, you know, founders will be so keen to start something they say yes to the first thing they think they can afford, but they wouldn’t really appreciate the cost of that taking longer than it should or the wrong thing being built and then having to do a lot of rework on that that. So I think, you know, it’s important to take the right strategic approach to selecting those partners that you’re going to work with in your business.
Absolutely. I think I think a lot of founders are optimistic by by nature, right? You know, we’re and We, we visualize a future where things are better, right? And you’ve got to be optimistic by to do that. But that optimism can sometimes cause you to make mistakes, like you’ve just said there, right? So you find the first one you can afford it, like you’re optimistic about it. Yeah, they can do it. And then you quickly quickly make a decision because you know, speed is of the essence, you need time time. Don’t have time you don’t have money, let’s get this going.
There’s some things which, if you get it wrong, are just going to destroy you. And and in the SaaS space. And in the software space, often, it can be the people who build your product, because there’s one thing I can guarantee right, is, they will make mistakes. Yeah, you will build you will build bad features. You will they will not deliver on time. and on budget. Like, yeah, I’ve I’ve experienced that as as a creator, I will almost always either be rushing towards the end, or deliver it a little bit late because these things metaphysics stakes, you, you’ve probably heard it, you know, what is it?
Even in like project management in general? What is it like 85% of projects? underestimate the time it’s going to take? Yes, yeah. Yes, it’s something that something like that, right. So software, it’s exactly the same. So you want to be optimistic, but you’ve also got to be kind of pessimistic, right? You’ve got to be, I got called a realist, a realist, rather than a pessimist. These things are going to be delayed, etc. And you’ve got to make the right choice. And so again, I would spend time on on choosing your agency or choosing your CTO, no one, no one, no one would argue that you need to choose the right CTO, if that’s the route you go down, that we would argue we need to hire, and really be careful about the best developers we can hire. Yeah. So if you’re going to do that, make sure you do the same thing for an agency. Because it’s exactly the same problem.
Yeah, and I think just because you reach a particular stage in that business, no matter what that is, I think a lot of founders Den is, you know, have this sense of urgency, and they worry, oh, I’ve got to this stage. Now. Therefore, there must be other people almost ready to overtake me. Therefore, I need to get this out as quickly as possible when that isn’t true. And you know, you shouldn’t be scared of sharing your idea. No one is going to take that and run with it and execute the way that you do. And it’s that execution, that is the important thing.
Yeah, execute, execute, and everything. Everyone’s got ideas. Execution is where it’s at. And obviously, like I said, like I said earlier, I’m not at the stage where I can sit here and profess to you that I’m some sort of, you know, execution genius I’m executing right now, or what, but when I when I, when I can say to you is it’s incredibly tough. And it’s way tougher to do this than it was to, for instance, write that entire spec document in Confluence. It was way tougher to do this than it was to convince my co founder to part with his money to try and help you know, deliver this vision of mine. Yeah. It. It can’t be under arrest. It can’t be understated how difficult it is to execute. But I can also say, it can’t be understated, how good it feels when things go right. When when you when you get when you get those wins. So yeah, I certainly wouldn’t want to put anybody off. Because because it’s it’s absolutely amazing to be doing this, even though like I say, I haven’t made it all the way.
No, it certainly sounds like you made a lot of very smart decisions along the way. And are you heading in the right direction, which is backed up by the results that you’re you’re seeing?
So, you asked earlier about the results? How are we going about trying to acquire customers? This, this here, in my, in my opinion, is actually one of the major mistakes we made. Okay, so one of the major that you just mentioned, we made some good decisions. This is one, this is one of the bad decisions that we made. So we had the view that because we were going into a space that had quite a few perceptual competitors. Now, let me explain what I mean by that. So we don’t believe we have any actual competitors, because we are, you know, dedicated data analytics, project management collaboration tool, right?
But from the perception of our customers, many of them see us as the same as JIRA. Okay, right, as the same as monday.com. They’re perceptually, we’re going into a space where there’s lots of competitors, right? And once we start talking to people, we can explain the differences, but they’re not going to, they’re not going to, they’re not going to put the effort into see the differences when we make when they see an advertisement of ours or see our social posts. They’re just gonna make assumptions, right?
Because we were perceptually in a very competitive landscape, we knew we thought that we needed a really incredibly polished product. Before we even told anyone we existed. Okay, before we before before we ever did any significant work about marketing our business aggressively. Yeah. And that was a mistake. Because what it meant was, what it meant was is that when I say we had We have funding, that no one’s got unlimited funds. And so we spent more money in the early days on the product, when we should have, you know, routed some of that money to early stage marketing testing, commercial testing, and commercial activities.
So that was one of the mistakes we made. Right. So our initial, you know, in the early stages, I would say that 90% of my time, you know, in the in the business was spent on product development. 10% was on marketing slash commercial activities, right. And so, because 10% of our time was dedicated on this, all we were doing, were Google ad, Reddit ad LinkedIn ad experiments. Okay. Yeah. So spending very, very little money on these platforms, but trying to work out what the best keywords were, what the best channels what the best customer profiles were, yeah, all of that sort of stuff, just trying to go and collect data to be ready for the time that we that we fit the switch. Okay. Now, during that period, during that period, we still managed to get 70, you know, early adopters, on to the platform, which is a really good number.
Yeah, it’s nothing, it’s nothing to, to, you know, turn your nose up at. But I believe that if we’d have flipped the amount of money slightly from product into marketing, we could have significantly increased that number right. Now, that would have had huge benefits for us, right? Because we’d have had more data coming in about what the product should be as we developed it. Yes. Yeah. Does that make sense? So, you know, don’t get me wrong, we have, we have that input. But more data is always better in this in this sort of in this sort of space.
And so I think we’d have, firstly had more information on what value the actual product itself as we were building it could provide, what features we should be building out, we’d have been able to understand our customers significantly better than we otherwise would have done at the point that we started our commercial activities. And we’d have been in it, we’ve been a bit of space. And so what that what that’s meant with men and what what that stake is, because we’ve only just really, in the last few months started our commercial activities.
So we spent six months trying to hire individuals into the business. Yeah, we’ve set up our how our strategy on terms of marketing, but we we need more information than we currently have that. So we don’t we don’t believe that we’re taken advantage of what we have to the greatest extent that we otherwise could could have done. And that’s a real shame, because we’re not, we’re not adding customers at the speed at which we ideally say particularly wants to be if we’d have flipped that spend soon, I’m saying, yeah, that model would how much we have much faster, we’d be great. We are growing fast. But we’d have been growing much, much faster, I think it would have flipped it. And that’s effectively what I what I think could have happened. But it’s hindsight. Right? You make decisions over time?
I think that’s a really good point. And I think a lot of people, you know, see marketing, unfortunately, still in a very transactional way, you know, it’s part of a marketing funnel, to feed people at the top to kind of get them into a sales process to convert them further down. But, you know, so many companies of all sizes really miss out on the fact that those marketing activities are part of that research. And it helps you to polish the messaging around your products, which, you know, feeds back into marketing itself, it also feeds into sales. And it also feeds into development, because you get you develop your kind of strong opinion and message that you want to take to that market, and then you understand their wants, desires, their values at a deeper level, which then does feed into the development of the product as well. So it can, you know, almost kind of preempt a lot of their, you know, problems challenges and helps you to position it in a very particular way, doesn’t it?
Because it does. And I think, again, because it was this is my first startup. I don’t think I was aware. And maybe other founders may have experienced the same thing of how much time marketing takes Yeah, you know, brand awareness, trust, social proof is so much time. So you should be starting day one. You should be starting day one on developing those plans, on bringing in talent and help in the marketing and commercial space. I mentioned earlier, we decided not to bring on a CTO because we didn’t believe that we need to do that. Again. In hindsight, what I would have 100% done is bring on a commercially minded marketing based But slash sales based founder, a one? Okay. That’s that’s, that’s, that’s something that I, I will or would do if I could do that all again.
Because, again, you also sometimes I think and I’ve seen this with other people I speak to because again, you’re a founder, you’re quite ambitious you Why not every founder is confident, so to speak, or is an extrovert, you have a certain level of belief, right? You have a certain type of belief in yourself, and and your idea. So what you do is you convince yourself as I’ll be able to sell this no problem. Yeah. Because I because I believe in it, right.
But the the skill set for a marketing and sales person is incredibly difficult to find and incredibly difficult to do. And a massively valuable, I used to ask myself, why do the sales guys earn the most money, because like, when I used to work for capital, for instance, why these guy get so much money, like, they’re not, you know, they’re not that special. But they are, because it’s, it’s one of the toughest jobs in there isn’t business.
And so that’s what I would learn, I brought in somebody a much, much more rarely, I’d spent much more money on it in the early stages of this business. But like I said, hindsight is a wonderful thing. You make you make a decision, at that moment, you make it based on the best available information. And so you can’t beat yourself up when you make those mistakes, because as long as you made the right decision at the time, that’s just the way it is that it’s weighted sometimes then hindsight sometimes finds that you did have all the information, but you would never know that.
Absolutely. I think the key thing is to actually make a decision actively strategically based on that information that you do have, rather than, you know, just kind of sit on that not make a decision either way, or hedge your bets, do a little bit of this, a little bit of that, and that can really harm a business more than, you know, making the wrong decision.
Exactly. I mean, we we experienced that with our experiments, right? Like, in effect, we were doing experimentation for the right reason. Yeah. But actually, what that meant was some of our experiments, were probably wasting money. Because we weren’t experts in that space. We were just we thought we’re collecting data, so that we’re ready. So we’re ready for when it matters if or when it matters, when if we’d have brought in the right talent, they’d have quickly identified. Right, that’s, that’s a red herring, you know, so that’s a dead end. Yeah, let’s eliminate that experiment. And let’s try a new experiment. It’s tried this new route. Most of times very hard to acquire. And at the time, because of the perceptually competitive landscape that we were we were dealing with, or we believe we were dealing with, that’s the decision we made. But, you know, there you go. That’s what being a startup or scale-up is all about. Right?
Absolutely. Absolutely. It’s, it’s always a learning process, they’ll always be something to learn. And you know, that that may be a skill or a capability that you don’t have, and yet you obviously you can look at outsourcing that and getting support, you know, with some of those areas as well.
But there will be you know, there’s always going to be new things that you’re going to learn from your markets as that landscape changes as well. And that’s why that research process, I think, is, you know, inherent part of what good marketing looks like. It should be heavily researched based.
But I think it should be a continual process as well, because, you know, new tooling will arrive new best practices, and even as people, you know, more people use your platform, you will kind of help them level up what they are now capable of doing. So there’ll be things that they need that cannot be thought of at the moment because it wouldn’t make sense.
Exactly. Yeah. I mean, that that that point there about, you know, them identifying things that they need, that you, you would have no idea that that happens. That has happened. And it happens very, very quickly. And then you have to make, again, strategic and tactical decisions about how your roadmap is going to completely flip. Yeah. But again, you’ve also gotta be careful about that.
Because you can you can hear some ideas or some things that are absolutely essential that you need to validate whether or not that’s essential to a lot of people. Yeah, yeah. Because there’s no point in developing that feature. If it’s just one person saying, I absolutely need this. Everyone needs this. Why is this not here? It’s crazy, what you know, but this feature in everything takes time.
And so that’s why you need to be very methodical and, and think very carefully about how you play this out, especially when you’re a small business like ours, right. Like, there’s only a few of us. Yeah, and we’ve only got meant to have eight hours in the day, but most time we have 12 Yeah, but but, you know, that’s the way it is and that’s why it makes it so challenging, but that’s also why it makes those those those winds rewarding. And hopefully, obviously when we IPO or exit super rewarding.
Yes. Yeah, absolutely. That’s, that’s the future though. Yes. And yeah, I want to kind of, you know, jump into kind of what you know, the next steps and the vision for Brijj Looks like in a sec. But before we do one thing, you know, we discussed, you know, before this, this interview that I just love you to kind of explain this that we were talking about the reason people make decisions that aren’t based on data.
And you said that a lot of people make assumptions based on opinions that they treat as fact. So I’d love to kind of hear your perspective on that, and the use of data, especially as we’ve talked so much about the value of research.
Well, I don’t think it’s, you know, a controversial thing to say that a lot of people make decisions based on erroneous information. Right, we see it, we see it, we see it all the time. Yeah, right. But you’ve got it, you’ve got to see that from the human perspective. And the reason I talk about this is because I often used to explain this to my analysts, when I was managing. People make decisions, as I mentioned earlier on all available data, right?
All available data as a concept. Yeah. Now, unfortunately, thoughts humans are hardwired to believe that their personal experience and what they see with their own eyes, is more valuable as data, than anything else. Yeah. Right. So, you know, people, people always say, oh, making decisions on assumptions is wrong. But unfortunately, making decision on assumptions is our natural way of making decisions.
Because for hundreds of 1000s of years of our history, we had to make quick decisions, right? You hear you’re walking through the woods, and a piece of wood cracks, you’re gonna run, yeah, and you got to make quick decisions based on what you hear and feel around you. So we’re hardwired to do that. Obviously, that has huge repercussions in business today. Because what the sheer amount of data points of information that you have no chance of experiencing personally want to say, personally, I mean, you as an individual, but also your business, or your department, or your team, you know, is so vast, and if you make decisions based on your personal experience, again, with teams past experience, your business experience, you are missing 99% of the true reality of your market, or of your space that you’re working.
Adrian MitchellSo yeah, being in data and research has taught me right, that you need to firstly collect every bit of data that you know about every piece of information within your business or within your sphere, you also need to spend some time thinking about the possible data that you have no idea even exists. Right?
They have ideas. Yeah, it’s like give an example. Right? So I used to work for a business called unite students. Okay. And we used I used to be a manager there. And so they have a responsibility to look after their students.
For instance, as an example, if students live in their buildings, they are contractually and morally obligated to make sure these people are safe. Yeah, yeah. So they do an incredible amount of work, trying to make sure that the well-being of their students is looked after they have huge amounts of data that they collect, which students know about, but about, you know, who they are situations, they’re in, you know, their limit their history in order to make sure that that they keep them safe and keep them well. And it would be very, very easy just to collect that data and keep that keep that data as is and just work down that process.
But we discovered that there were ways to use other completely unrelated datasets yet to ascertain whether or not a given student or a given group of students is at risk. A good example. So in United buildings, there are sound sensors, which are there to make sure that a particular corridor, maybe be loud or you know, maybe caught maybe causing a sound disturbance, right. It doesn’t record any like words doesn’t record him. He just kept record a sound level. Yeah, that’s what he does. It’s like a 10 decibels right now. It’s whatever. There’s not really it’s not very interesting data.
But we thought to ourselves, okay. What if we find that a particular corridor or a particular room is noisy, but what about the opposite? What if it’s super quiet with the time? What if nothing’s happening in that particular corridor? And what this data gives you is it doesn’t give you a hard and fast bit of knowledge. It doesn’t tell you anything for certain, but it gives you an indication of whether a given corridor or group of rooms.
I’ll get you along. If they’re establishing relationships, yeah, yep. Interesting. And as you now know, to a certain level, whether or not students in that particular corridor or in that particular group of flats, may potentially to a higher degree, have some level of loneliness issues or may need some support with the involving clubs and stuff like this. Yeah, I see. So so the reason I mentioned that is because you’re not had a mission to look after the well being of their students. Yeah, they have assumptions about what students want, what students care about.
But as an organization, and as a team as a division, we collected additional data, which helps us make better decisions to ultimately deliver on that mission. So I’m saying so so so not only do you have the ability to eliminate mistakes by collecting more data, you have the ability to make better decisions and make more benefit more value by collecting more data. And so that’s that’s what I’m that’s what I believe in. That’s what I that’s what I’ve always focused on with my staff members, like done throughout this given if this particular business and anything I’ve ever done before. Yeah, and I think more data is almost always better.
I think that’s massively interesting. And I think that’s, you know, a really good example that you gave of, you know, aligning to that mission-aligned to the outcome. And then, using that data to uncover problems that you didn’t know existed, that can be solved that that all mean, and move you further towards that outcome.
Exactly. And I mean, they, you know, that data, for instance, it wasn’t, you know, at the time I was there, I don’t know about now that they could have negative thoughts on that. It wasn’t being like, consistently and methodically collected, that it was been analyzed, right?
Yeah, we wanted to see if quiet corridors tend to have more issues. And it’s just, it’s just, it’s just you’ve always got to be intuitive, you always got to be thinking about how you can use data to better advance people’s lives and your business’s bottom line, and you’ve got to have an intuitive and inquisitive sorry, an inquisitive mindset at all times, not only to work in data, but I think just to do anything, startup or scared up related. Yeah. Because you’ll you’ll find information and ideas that you’d had no idea existed before.
Yeah, I think that’s a really, really great and, and certainly valid point. So yeah, I guess then, in terms of, of Brijj, itself, and what are the, you know, you mentioned before, you know, in the startup phase, you’re starting to get, you know, really good kind of clarity on that value proposition and customer acquisition, and, you know, those lessons learned on my hosting, but, you know, there’s still a very, very solid sounding, you know, research-driven approach to this. So yeah, what are the, you know, what does the future immediate future look like for Greyston? What are your, your kind of key things that you’re focusing on at the moment?
So, the immediate future is rectifying that strategic error that we made in the beginning on the conceptual side. So laser-focus on further refining our marketing and commercial messaging? Yeah. Try trying our best to improve the team’s capabilities in that space. Making some very, I say quick, so how would have like phases? short timeframe decisions on how much we plan to spend on the commercial side? Yeah. So that’s, that’s a funding and spend question. And really, our intention is to go from a very, very small business that’s at the, you know, the nascent stage to becoming a hyper-growth business. And that that requires, like, laser focus on the commercial side.
So we’re right at the beginning of that journey, you know, full disclosure, right beginning of that decision, and that and that’s to step. But that’s a short-term focus, we need, and want to grow as fast as we can. And there’s two reasons for that. The first is obviously because that’s, that’s how stocks work. Yeah, crisis, growth is king. Right? We need to do that for me for my co-founders benefit and any future funding that we want to bring in. The second reason is because it correlates with our ultimate vision. And, and our ultimate vision is to build a platform that can tell you the best decision, action, and outcome that you should take based on any business question.
So let me expand on that a little bit. Is our vision for the product? Yeah. Every business out there has data analysts, nearly every single business out there has been around this and they have answered hundreds of questions over many Many years. So what that means is, is that a lot of the time when a business question is asked, it has already been answered. Yep. Yeah. It’s either been answered directly, or the code is there for it to be answered very quickly, a chart is there to be updated.
Or, you know, a report could inform that particular question. Yeah. But I cannot tell you how many businesses out there have no record of what they have previously created? You know, I created a lot of analysis for dmg T and Hobsons PLC. Back when I was a data analyst. If that question is asked, now, I can guarantee you no one has a clue where that report is stored, or where that piece of code is executed. Our ultimate aim is to create a system where every question in a business is asked. And so, therefore, every requirement, every piece of code, every decision, every action, and every outcome is also recorded, almost a central point of truth, for a business’s decision-making process.
Now, in order to do that, what do you need? You need data? And lots of it? Yes. So we say so, therefore, in order to build this grand vision for our product, which will have immeasurable value, to just SMEs, who we’re targeting right now, but enterprises, ultimately, we need to accumulate the amount of data, we need to build our ML models to build a predictive models to actually, you know, map out the way that our platform will deliver on that promise.
So that’s why the commercials are so important because we need to grow aggressively in order to start to building that and to be able to prove it as a proof of concept, which will help us once we finalized or proven the theory behind what we’re trying to build, there will be staggering, staggering, valuable. So that’s, that’s why we’re focusing on the commercials and the marketing side right now.
Awesome. No, that sounds like a very compelling vision and a very powerful product. As a result of that.
We hope we hope so. I mean, imagine, imagine if a Sales Director types in a question. Yeah. And perhaps the last 12 pieces of insight or data that that organization itself, has created? Yeah. You know, very rare. Is that available? In most organizations? I mean, you’ve worked for some big companies in the past, and you know, you’ve worked for yourself, I bet you’ve asked that question of a data analyst and said, Why can’t you answer this straight away? Yeah. Why is it? Why is it gonna take you so long? To answer this question? Surely somebody has asked this before. And the data answers like maybe, but that can be six years ago, and I don’t know who the guy was.
Yeah. They forget, even if they know, what was done before. Yeah, exactly.
Exactly. And so few amount of time, it was a huge amount of labor. And yeah, massively valuable. And there are platforms out there that tried to do this, or tried to do it in different ways. But I think if you create an ecosystem whereby naturally the question is asked in the platform, and naturally, the question is, the question is answered in the same platform, the natural consequence of that is that all of your questions and answers are in the same place, rather than doing your work in JIRA, storing and storing the outputs in SharePoint by asking the questions in Google Forms, chatting about it in Slack, asked another question in email, phoning somebody, you see them saying you can’t you can’t create this interlinked network?
Well, you can. But most businesses don’t have the technical knowledge or the money to do it. Yeah. So therefore, I think, solves that problem. We need to get that we need to get there. And the way that we get there is collecting more data and collecting more customers. So that’s that’s the focus right now.
Now, that sounds great. And yeah, I wish you the best of luck with that process. And I’m sure it will be very, you know, research-driven, given your background and approaches so far, and I think it’s, it’s good to see that that growth is not just I say just but it’s not purely about booting or attempting to boost customer numbers in it.
There’s no vanity metrics in there as what I mean, you’re looking for genuine sustainable growth, but and then there’s growth that’s going to obviously, you know, accelerate the business make the business more valuable, but also it is something that then feeds into a, a bigger vision and bigger product vision, which I think is is really, really good.
Yeah, I mean, I’m glad to say that I mean, I am like most founders in that I want to make something of myself, both, you know, I want to make an impact and I want to, I want to reap the rewards of that, you know, financially whichever way you want it, whichever way you want to look at it.
Yeah, but ultimately, ultimately, I loved being a data analyst and I have met some of the most intelligent and dedicated professionals of my life to in doing this work, you know, I’ve managed them work with them, I’ve spoken to them at conferences, and it’s a really, really valuable and imperative discipline. And I’m gonna be doing this the rest of my life, regardless, I’m gonna be working in data analytics in one way, one way or another.
So I personally want to make an impact on this industry that lasts longer than the 34 years that I plan to work in it. So that’s, that’s what I’m ultimately trying to achieve with Brijj. And obviously, I want to also create value for my co-founder want to create value for the shareholders that we have in the future? And, and we’ll see, we’ll see how it plays out one way or another, this problem that I’m trying to solve will get solved will either be us, fingers crossed, it will be Yeah. But you know, but it’s gonna get sold one day. And so we’re on the journey to doing that ourselves. I wholeheartedly believe that.
Cool. No, I think that’s, that’s an excellent vision, and I think is the right way to go about building. You know, I start out transitioning into that, you know, higher growth, scale-up, you’re doing it for the right reasons, and, and in the right way, which I think is fantastic. And, you know, for anyone listening, that’s interested in finding out more about Brijj, obviously, we’ll be sharing the links to the website in the, in the notes as well. So people can take a look at the platform, see if it’s something they might be able to get on board with us, as well. But before we kind of close out, are there any kind of closing thoughts that you have, or any other bits of information you’d like to get across?
Not really, other than I hope that I’ve, it’s been useful for you to chat with me, and I hope your listeners find even the smallest bit of information that I provided that they’re useful, you know, not all data is useful, but I hope some some of this data that I’ve given you will be valuable, and you can you can use it to, to either make the decision to start your own business or to, to benefit your own in some kind of positive way.
No, absolutely. No, thank you, again, for taking the time. And I think there is a lot of data in this conversation that I think people should take away, like the value of that research, and you’re trying to break down some of the bias that that may exist in no matter the size or scale of your business, you know, the way you’ve broken down your problem, validation, you know, validating the problem exists.
But then secondly, is one worth solving. You know, before you get to validating the solution is the right solution to that problem and feeding into the marketing. The messaging is all related to serving that core audience. I think there’s a lot that people should listen to, in this conversation about the importance of that research and making decisions based on data. So no, thank you for sharing that much appreciated. And, yeah, I look forward to another conversation and see how that growth has gone.
Absolutely. Before to it. So thank you so much for having me. Appreciate it. I’ve enjoyed it.
Good. No, me too. I think it’s been a really good conversation. So thanks again and take care.
Thank you for joining me on this episode of Inside the Scale Up. Remember for the show notes and in-depth resources from today’s guest. You can find these on the website insidethescaleup.com. You can also leave feedback on today’s episode, as well as suggest guests and companies you’d like to hear from. Thank you for listening