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Noble Conversations
Stears: The Bloomberg of Africa | A Noble Conversation with Olabode Ogunlana
In this episode, we sat down with Olabode Ogunlana, Co-Founder of Stears, the most comprehensive financial data and research provider for global funds and corporates investing in Africa.
We cover:
- The Founding Story of Stears: How the company was conceived, its mission to bridge investment gaps in Africa, and its evolution since 2017.
- Data and Insights: The unique ways Stears collects and leverages data to provide real-time insights on key areas such as elections, markets, and industries in Africa.
- Overcoming Challenges: The biggest obstacles faced by the Stears team and how they’ve navigated Nigeria’s economic climate.
- Future Focus: Insights on sectors Stears plans to cover, including the potential growth of Nigeria's creative economy and other emerging areas.
- Building a Better Future: Thoughts on how we can create the society of our dreams, ways the audience can support Stears' mission, and personal reflections from the team.
Welcome to the Noble Conversations, a platform focused on building the society of our dreams by engaging in noble conversations with those who are making community and global impact, specifically in the areas of advocacy, creativity and or entrepreneurship. Now, today's guest is definitely making community and global impact in the area of entrepreneurship in Africa, specifically in Nigeria as well the area of entrepreneurship in Africa and specifically in Nigeria as well. Olabode Obunlana is the Chief Technology Officer at Steers Company, a market insights company for investing in Africa. Now, join us today as we have a noble conversation about the company shaping the future of investment in Africa, in Africa. Okay so, bo, it has come to my attention that you are not just chief technology officer at Steers, but you're also an amazing artist and can draw.
Speaker 2:Oh, interesting.
Speaker 1:Is that true?
Speaker 2:I feel like I could draw in a past life. But every time I try to do it these days, it comes out very disappointing.
Speaker 1:But I do draw. Okay, you did. When was the last time you attempted to draw?
Speaker 2:a few weeks ago. I have a 10 month old daughter, so I was trying to draw her. I haven't quite captured her. You know exact image yet okay, okay, okay.
Speaker 1:So was that, was that something that you feel came like naturally, or was it something that maybe you had to work on or develop, uh, the ability to, you know, put images to paper?
Speaker 2:It's a good question. I remember my sister and I both drew quite a bit when we were kids and I sort of picked it up from her. I saw her drawing and I thought that was really cool and so I wanted a copy and so I just started drawing. Because of that, both of us are pretty all right artists. Now I don't know if she still draws, but I still try from time to time.
Speaker 2:It's just something we have the whole. I remember we were in school and they would. They would the art teacher would try and get us to do more art and like, take it on year after year, but you eventually both, I think dropped it okay, understandable, understandable.
Speaker 1:I personally I'm not, like you know, artistically inclined in that way. I mean, I'd sickly inclined in other ways, but not like drawing. So whenever I see someone that can do that, I'm always impressed. Um, but who would you say olabode is you know because you draw or you drew in your past life? And your chief technology officer at steers and co-founder um, who? Who would you say is you know? Olapade is like? How do you view yourself in your work and outside of your work?
Speaker 2:that's very interesting question. Um, I feel like, um, after this, after this interview, I need to come back and ask you the same question.
Speaker 1:I mean, I'll give you my answers. I'll give you my answers.
Speaker 2:I'll give you. I don't hide, yeah, so I see myself as a few things. I'm a father I'm quite excited about that. I am a technologist at heart, so I love things, I love all things technology. I get quite embedded in deep tech not actually producing deep tech, maybe not yet, but in observing and speculating where it's going to go. Of course, I'm CTO at'm also. Um I I also focus on product, so my, my role sort of takes me into the realm of talking to customers, understanding their needs, and I think it's something I really like to do.
Speaker 2:I just like to understand what makes this person or this group of people different from each other and similar to each other, and what makes them delighted when they're trying to achieve a particular goal and they can't do it and all of a sudden they can in an interesting way. So that really interests me. It's, I think it's quite hard to achieve. I don't know if we'll see if we have. We have fully achieved that. Um, yeah, there, I don't know what. About me? Right, because I can tell you, yeah, I can tell you anything, but I guess you have to ask this part yeah, yeah, so okay.
Speaker 1:So you mentioned deep tech, right, and you're fascinated with. You know deep tech and where it's going. So, for you, what's something new that you're learning about deep tech and where it's going, or just in general, that you would like to share with someone?
Speaker 2:ones that most people are paying attention to is like um ai. So right, like, for instance, uh yes, let's get where it's gonna start. At another company, um raised uh 1 billion out the gate about without um, you know, without so much as I don't know, I don't know what they have at this point. They have, they have a small team and, of course, I was the previous CTO of OpenAI. So it's clear that what they're working on they're working on safe super intelligence, which I think is a worthy goal, and it clearly is something they were working on while they were at OpenAI as well. But apparently the project was dismantled when Ilya left, when they had the whole debacle of project was dismantled when Ilya left, when they had the whole debacle of Sam being fired by the board rehired and then lots of people jumping ship. Actually, I don't know if anyone jumped ship, but I know about Ilya that I think something I follow, that maybe a lot of people follow as well, is quantum computing.
Speaker 1:Okay.
Speaker 2:And also nuclear technology, not like arms, but um nuclear power generation energy. Yep, yeah, yeah, okay those two are quite big um. Are you interested in any of these?
Speaker 1:as well. You just mentioned nuclear energy and, yes, that's one that I'm extremely interested in. Reason is I just made it a transition from working in the healthcare sector to working in the renewables sector, so I'm learning a lot about different sources of renewable energy, whether that's biofuel, nuclear coal, all of that. Nuclear is supposed to be one of the safest right, but it's, and cleanest, it's just how do you? Well, one, it's very expensive and two, like, how do you get people to actually like basically it's, it's an education thing to see the benefits of nuclear? Yeah, um, you know, I, I don't. I wonder if it's scalable, or at least if it would ever be scalable or commercial. Uh, that's something I've been thinking about too, but no, I think I think okay. So you mentioned quantum, um, you know ai, obviously, and then nuclear energy, um, and then, of course, you know, the common theme between all three of those things is data, right, which is where Is it?
Speaker 2:I don't know, I don't know, I wouldn't draw those lines.
Speaker 1:Oh, you don't draw those lines?
Speaker 2:Why? Why do you say that? Don't get me wrong. I think data is a very interesting space. It's actually where I am right now. I'm super excited about data and big data and the connection between that, and actually I would draw the line of data between AI and between quantum computing. Even in between there's research going in the direction of quantum machine learning to remove the hard problems in machine learning by fundamentally changing how the computation actually works. Because quantum computing is good for solving optimization problems and machine learning is basically an optimization problem. But it just can't really be solved very easily because you have to check everything one by one. The computer has to check all the steps in order to optimize. But nuclear, I would say. Is there a lot of data in nuclear? Maybe there is, I didn't know it well enough.
Speaker 1:I would say, okay. Well, here's the idea. Quantum tends to go over my head. I'm still like, you know, maybe.
Speaker 2:I need to invest more time.
Speaker 1:Yeah, yeah, I just need to invest more time in understanding, like quantum computing and you know all that, but I think I think you're right.
Speaker 1:I think I mean mostly quantum, ai, um nuclear, I mean obviously, like there's still some data that's being used to, you know, but I don't know exactly how, how that data is used and to what extent. Um stairs you mentioned, you work in the founding, you know, in the um data space. So I'm curious about stairs and its founding story. You know, I've seen a few things online and I've read and watched videos. I think I had a brief chat with preston is it a year ago or two years ago? So I'm kind of familiar with the company From my understanding. You know, sears makes it easier for organizations to invest and operate in Africa by providing insights on the market, while also making it easier for African countries and businesses to receive capital. So basically, sears is steering us into the future of data and insights in Africa.
Speaker 1:But when you're thinking about data right at STEERS, like, one of the things I'm always curious about is like how does STEERS like get that data to be able to tell whether it is? Oh, this is what you should. You know, maybe these are, these are areas you could invest in or not. These are where opportunities exist or not. I even saw, with the new um steers election tracker for last year's elections, like being able to update those in real time. So I'm curious like how does steers gather that information and present it to the public? Obviously, there are some things that are proprietary, but for the things you can share, I'd love to hear.
Speaker 2:Yeah, no, of course. So yeah, of course. First of all, actually, if you've spoken to Preston, you've captured a lot of stares that I won't be able to say as well as he did.
Speaker 1:It was a 10-minute call 10-minute call, so I don't know if I yeah, but it was a 10-minute call. I got something.
Speaker 2:Yeah, but yeah, so we collect data, and there are many different methods to doing so. There's I mean, people like to break it down into first-party and third-party data collection and then there's the enrichment process, so there's data at rest like. And then there's the enrichment process, so there's data at rest like raw data. Ie, if you send out a survey and you're able to collect all the sentiments of many individuals in a particular country towards a particular brand or towards their current situation as it pertains to electricity or fuel, for instance, fuel prices have gone up, um, you could send out a survey finding out something about, um, what the average fuel cost per household is like, and that would be useful for, maybe, a government, for, maybe, um, people that are doing startups, or for people who actually are selling fuel, um okay yeah, so that information can be collected directly through survey, and that's first-party.
Speaker 2:But you can also have third-party data collection where there are other data providers and, of course, third-party then goes into free and paid data. Well, I'm simplifying, but that's basically it. You find data for free if there are organizations putting out that data, and there are a lot of these organizations government organizations, banks. There are a lot of survey organizations that are putting out information because they are regulated to put out that information, because people need to know those things, like national statistics organizations, for example.
Speaker 2:And you can take that information, regardless of whatever form it's in, and turn it into a form that is valuable for people to access. It might be a PDF, and you take it and structure it into an Excel or a screen that is a table of rows, I mean. It makes it easy for people to understand what's going on. And then there's other data providers maybe a bit like us, but maybe sometimes not, that we can pay for data, and so in that case we either pay them to give us their data directly or pay them to plug into their system, and that would mean, like, access data via API and then present it.
Speaker 2:All of these different types of data collection work for different types of data, for different types of customers, and when you bring that together, you have something unique, because now you have the ability to see things that each of the individual data providers might not be able to see with their own data. Putting it together gives you a new story, and then you can also enrich it by doing your own analytics. For instance, we have forecasts, we have indices, we have analytics, what we call analytics, which is basically data that has been created from the previous data. That is useful in itself because of the expertise that's then been embedded into it to provide some kind of value. For instance, we forecast on FX, which is a very hot topic in a lot of African countries today. Henry Suryawirawan.
Speaker 1:Okay, okay. So out of these three sources of data, right, which one, would you say, tends to be well? One more reliable, and then two, um, also like, more like, used for insights and analytics it depends on the project.
Speaker 2:Actually, it depends on what we're trying to achieve. So, in 2023, in 2023, we had the elections and, um, we wanted to elections and we wanted to predict what the likely outcomes would be under certain scenarios. And in order to do that, we worked backwards and said, hey, if we want to find this out, we need to create an analytical model that takes in data based on what people are going to vote. How do we know what people are going to vote? We have no idea. We have to go poll, and that polling is first-party data collection. So in that case, you actually need to go direct to the people that are going to provide the data, and this was very successful.
Speaker 2:I don't know if you've seen the You've probably seen some articles somewhere the prediction, the results. We predicted, with some amounts of accuracy what would happen under certain circumstances, and it did happen. And then that is an example where first party data collection was just absolutely necessary and it was impossible to do without it. And then, in the same period, during the elections, we also monitored the elections, and there we did third party data, because at that point we had to look at the actual results that were coming out, and so right now on the Steers platform, of course we have an open data section that still deals with election monitoring, but then for the vast majority of the other data sets that we have, we're not doing first-party data collection sense that we have. We're not doing first-party data collection.
Speaker 2:There's a lot of third-party data collection where we're either pulling from other sources that we know have trusted data or we're going out to free sources that are producing that information, for instance the MBS or the CPN or other organizations that we I'm focusing on Nigeria for now, but we actually do the same for the equivalents of those organizations in Kenya or South Africa and other countries that we actually do the same for the equivalence of those organizations um in like kenya or south africa and other countries that we recover okay, okay, so it really depends on the case and what's needed.
Speaker 1:Then you could go first party, third party, um you know, or like polls, okay yeah, okay, and so so for first party being polls as well, oh yeah, they're just different ways of doing it.
Speaker 2:Yeah, exactly.
Speaker 1:Okay so, just for clarification, it sounds like the Well, first-party obviously surveys third-party organizations that put out the data, and then the third is polls, but those polls are more like focus groups, right? Or is it individuals? Okay so, focus groups, right, or is it um individuals?
Speaker 2:okay, so focus groups. Um, I wouldn't, I wouldn't call all the polls focus groups because, I think focus groups have a very specific definition, but I don't want to go into that because I might trip myself up. What I would say is that the polls for the election in particular are survey polls that were sent directly to. I don't know what they're called. They're like pre-subscribed pollsters.
Speaker 2:So, there are agents that have already been registered. This is actually not our business, so we worked with a partner to do this that has agents that have already been registered. This is actually not our business, so we worked with a partner to do this that has agents that have been registered in several different places to go out and pull people. And so it's like yeah, that's basically it.
Speaker 1:Okay, okay, and so how did Steers come to be, and what's the story behind that? And where were you when the first idea to launch this company was birthed?
Speaker 2:You should ask the same about Noble Conversations.
Speaker 1:Don't worry, I'll give you the answer. Yeah, yeah, don't give me answers.
Speaker 2:oh, I'll give my answers. I mean, I could go first. If you want me to make, yeah, go for it.
Speaker 1:Go for it, tell me, okay, noble conversations, um so, um it, I think 2023, um. I started a podcast with one of my friends and we started because like, well, anytime we would meet up right in college, we'll sit down, we'll talk, we'll talk for like five hours and it was about different things, like what's going on in the world, politics, all that and um. For me I was like okay, like why don't we just do a podcast? Right, and this is something he had been wanting to do for a while. And for me I was like, okay, cool, like let's do it. So he bought a few mics, started, you know, um, having conversations about different things, and he had to, you know, step aside because of, like, work and other commitments, um, but for me I was like, okay, this is something that I'm still interested in. I mean, over time it's changed and evolved into you know what it is now in terms of my actual niche, right, but I think in the beginning it was just like let's just talk about whatever, but now I would say it's more focused on advocacy, you know, creativity and entrepreneurship.
Speaker 1:I kind of just organize it that way because, you know, sometimes we have guests where we talk about, like politics and policy and what's going on in the world. And then we have other you know guests who are like creatives or that's you know, music, podcast, whatever. And then we also have guests you know fall into the entrepreneurship category, like yourself, and and so I think for me it's always been an exploration of the things that I'm interested in. Um, and I've always been interested in these three separate areas, but I never fully, it never fully clicked that these were the three buckets, right, it was just kind of like oh, I just like talking about this, oh, I like learning about this, oh I like reading about this, this, um. But it was just actually this month where I was able to say, okay, these are the three areas advocacy, creativity, entrepreneurship and I was like, oh, I even made a video about it, because I was like the, the exploration process and the journey of trying things, you know, changing, adapting, figuring out, okay, like, what is it that people want to hear from you, especially considering your own experience and the things you're interested in?
Speaker 1:And so for me, it's just been and it's still evolving, because I'm, you know, I'm still putting content out there, seeing how people respond, learning what unique insights or unique experiences I could bring to the market. You know, using business, business terms, um, so that's it, that's it. That's a big, you know, that's a summary. Um, if you have any specific questions, I can, I can go in, I can go deep, but that's, that's a summary.
Speaker 2:So it's been a year, a year.
Speaker 1:I would say yeah.
Speaker 2:And, uh, I'm I'm glad you, you, I'm glad you actually answered that, thank you. Yeah, I generally was not expecting that, because it like it's really refreshing. Um, the the scene that you mentioned because I was thinking about how we got started as tears and you talked about that scene of you and your friend just sitting down and talking and talking for five hours and I think of the co-founders at Stairs and before we had Stairs, like genuinely all four of us were like friends and some of us were very close friends.
Speaker 2:I would even say some of us were close to being best friends at some point would say that some of us like close to being best friends, um, best friends at some point, and we would have those conversations for a long, long long time, talking about whatever was on our minds. Um. So I went to primary school with one of the co-founders went to secondary school with another um. Two of them went to three of them. These other three went to the same university and so we're all we're, all you know, know a tight knit crew.
Speaker 2:Before Steers even occurred I mean at the time it occurred we didn't know each other very well there was.
Speaker 2:Preston, who knew all of us very well, and sort of brought us, all of us, together and that's how Steers started. So we um Preston and Abdul in particular, had this idea. Michael and I were very interested in it and it just sort of sparked and kicked off because we liked each other, we liked to work together, we were interested in this thing that we're working on. We said we're going to be the Bloomberg of Africa. We said, okay, let's make a website, because it doesn't make sense to start with a terminal, create a physical keyboard and all the different parts that go into the very complex and very, um, very well established bloomberg. As it was at the time, I think this was mid yeah, this was probably between 2016 and 2017 or something a long time ago and at that time I had so my role was CTO. At that time I was just starting my career as a software engineer, so everything here was a big experiment to me, like, oh, maybe I can get better at this, maybe I could do this, maybe I could experiment with that.
Speaker 2:I know for Preston, for Abdul, for Michael, in some ways, like some of the vision of what we're becoming and where we're going was completely already founded, like some of us. Some of us had really good foresight. Some of us were just, you know, go with the flow and seeing what was going on. And I think, eventually because at that time we weren't all doing stairs full time, we had jobs, or we were in school or we were doing something else and Zyvan did a master's at some point and then came back and worked again for another company and then by 2020, we all came full-time and I think things really started to pick up and move a lot faster.
Speaker 1:Okay, and so how have things changed, or how has Steers evolved since its inception, I believe in 2017. That's when it was founded, right?
Speaker 2:You know what, when people ask that question, I always struggle because I have so many memories of talking about something like Steers very early on, but I don't know the actual date. I think that the official cleaned up like polished dates that people say like stairs really was founded like 2020 or 20.
Speaker 1:It's 20, okay.
Speaker 2:Either 2017, 18 or 2020, because there was stairs before 2020, 100% Okay, but the stairs that we know today really materialized in 2020. Okay, okay. So how was it? Post elections, post elections, okay, but the stairs that we know today really materialized in 2020.
Speaker 1:Okay, so how has it evolved since the beginning, since it started?
Speaker 2:Yeah, I love that you kept it fake there since it started.
Speaker 1:Don't get absorbed by the dates.
Speaker 2:Yeah, it's evolved a lot. People-wise it's evolved. Um, the business has evolved. I think the latest evolution is the pivot, or what we call. What we call the pivot in steers I get it startup lingo, you know it's pivot is you change your business in some way. The significant but, um, we remember the pivot as this, like whole, whole year-long period of discovery and lots of change. I guess I'll touch on that. But before that, when we started earning subscriber revenue, that was also a big evolution and a big change. And then before that, there was the election, which really brought the team together in a way that it had not been brought together before. So when Stare started, we focused on media.
Speaker 2:We saw that Bloomberg had media and data and we didn't have the skills to execute a Bloomberg. On data side, that'd be very ambitious and, to be honest, on the media side things seemed a bit simpler and we had some very excellent, let's say, economist firepower in the team, which we still do and we used that to gather a bunch of very creative, very intelligent individuals to analyze the Nigerian economy, because we saw that that was really missing the kind of analysis you get from Bloomberg, ft the Economist in Western countries.
Speaker 2:It was just missing on the continent in Nigeria for the issues that we were dealing with at the time and people were very aware of that. They were very cognizant to that, so we gathered a team. How do you say we gathered a team? I wasn't gathering the team at that time.
Speaker 2:I was just watching and smiling and being like, guys, you're doing a fantastic job, Good job. Yeah, I was responsible for building the websites, building the infrastructure, that type of thing, but I think the real value came from the insights that we're able to produce in a short period of time. That led Stiers to becoming one of the most trusted sources of insights on the economy and on the Nigerian situation at the time and on the Nigerian situation at the time, and that led us to a point where we understood that this need was real because we were very popular. People are constantly using us for their work or using us on a daily basis and waiting for the next thing we're going to put out, and we had a commitment of putting out one report or one article a day. At the time it was articles. Now it's reports because they have changed dramatically but we had a commitment of putting out an article every day that a few excellent people in the team, people like Preston Michael TJ Abdul, really led. People like Preston Michael TJ Abdul really led.
Speaker 2:Anyhow, it really formed this culture that put out such great content that by the time it got to 2020, we could pay for it and at that time, it was really the first of the new age type of subscription businesses on media and a lot of people told us this can't be done for this market, it's not possible. But you know we're like, fine, we'll try, We'll see what happens. And we did, and the customers that had been using Stairs up until then signed up, they paid and before we knew it we were a subscription business and we had new problems. We had new problems like collecting subscription revenue, and then we realized how nascent the payment space is. If you wonder why payments is such a big, it's not just Nigeria. A lot of different markets. Payments is very big now A lot of emerging markets. Payments is a very hot topic and, of course, it's just a hot topic in general, not only because it's some nascent in some areas, but because of the technology there.
Speaker 2:There's always new technologies, Like crypto is an example you know Um, but at the time we realized that there was um there's also missing some basic infrastructure for collecting subscription revenue that the leaders at the time were innovating on. Like we're using um paystack and they were innovating on it um we. We looked at other providers that were doing very exciting things, but we realized that the um, the level that you got in the west, like the uh, the subscription businesses like the world are called charge B and a few we just didn't have that level of integration. We needed to rest, not have to worry about collecting revenue. Well, it's just not there, and so we spent a lot of time building our own subscription revenue and that really galvanized the team. It really pulled people together again, again, in a nice way.
Speaker 2:This is actually after the first elections, which was the event that caused everyone to say, okay, we can build a team to build this offense in the future. So the sequence of events was like elections, 2019 elections. Someone said, hey, stairs is the obvious candidate for tracking the 2019 elections. You guys should do this. I know that Preston had been thinking about it and at that point he was like guys were doing it, um, and so we, we gathered every. I was working part-time for stairs.
Speaker 2:I had my day job yeah I was, at the same time, working with an intern and another engineer that I had hired and three of us me working part-time, them working some part-time, full-time to build this platform and on the back end, we're collecting the data. We're setting up. There was quite an elaborate setup of polling analysis, reporting, et cetera that had to go into the election and gave us the confidence to. You know, we can actually build this data business and 2019 elections. You know it taught us a lot of lessons on the technology side as well on the software side, but, like that's for me, it taught me a lot of lessons on the software side and taught the team a lot of lessons in general.
Speaker 2:But yeah, we moved on from that and immediately said, um, the analysis we're creating, we can charge for it. And so we did. And when we did that, we realized our customers were professionals, as we expected, because we were building um africa's data giants for global professionals looking into Africa. This wasn't just. I mean, our data is valuable for a lot of people, but the truth is that, at the scale we're producing it, the people who use it the most are professionals, like people using it for their work, whether that be in a non-governmental organization, or in a bank, or in a fintech operator or in wherever, yeah. And so at that point we said, okay, let's take a closer look at what they want and let's figure out how we can package this in a different form. Well, not really package it, because we weren't producing what we wanted to produce.
Speaker 2:We were only producing analysis on time and we said we have the ambition to produce data. We have the ambition to produce data, so how can we get the data to our customers in a package that they will buy like they would buy the average Bloomberg?
Speaker 2:At that time we're still looking at Bloomberg, right and so we had a few attempts. Of course, again, this didn't start on that date. We had been attempting previously, we had made mock-ups, we had made prototypes, we had many names. Start on that date. We had been attempting previously, we had made mock-ups, we had made prototypes, we had many names. We had Simba, we had Ken, we had Core Data, we had the data catalog. We had just lots of stuff.
Speaker 2:But I think at this point, when we had done elections, when we had charts for subscriptions, we then reattempted and we found that we we fell a bit short because we realized that, um, we felt our customers who were using, consuming our analysis, would directly consume the data that we produced, and so we started producing that data and just selling it to them, um, and we wanted to sell them a subscription, like we were doing for the analysis, and it didn't work as well. And so we we took a bit of time to figure out why there were a few problems, and I mean, looking at the problems from here, it's obvious what the problems were we didn't know our customers well enough. That's what I would say today. It's simple as that. We just didn't know them well enough, I think over the last year. So what I would say today?
Speaker 2:It's as simple as that. We just didn't know them well enough, I think, over the last year. So what I'm describing now is probably two, three years ago. Over the last year, we have learned a significant amount. Last year, last two years, we've learned a significant amount about our customers, and then last year was the B2B pivot, at the point where we decided, in order to sell to these customers, we need a completely different architecture for how we go to market. We need a platform that represents what they are actually about in a different way. This is because we need them a bit more narrowed down.
Speaker 1:It's like these are, yeah, these are the people, and we've been building that since and, yeah, it's, it's been a wild ride since then so it sounds like the elections were huge in helping you define who your customers were, but also, you know, making that pivot, at least of recent. From b to c to b to b, that would you say. Is that accurate?
Speaker 2:I would say the elections were huge in getting us to solve difficult problems at scale.
Speaker 2:Software problems, data collection problems, organizational process problems, and having solved those problems, we turned back and looked at the business which was completely unrelated to elections and said, okay, we can do. And looked at the business which was completely unrelated to elections and said, okay, we can do more, and so we charged for it. And then after that, even like long after that, another election came. So 2013 elections came and we tested ourselves again and again it, you know it revealed that. You know we still have more to learn in terms of um scaling, but we did a far better job than we did last time and it's always just been a nice event every four years to test our ability to coalesce into something bigger than this. Parts Okay, yeah.
Speaker 2:I think of it as a catalyst, but it was not the cause of the trajectory we were on. I think we were already on the trajectory.
Speaker 1:Okay, yeah, I'm thinking about my first introduction to Steers, because I've been using Steers for about two years now, mostly just the articles that come out.
Speaker 1:I think my first introduction was I was looking on TechCrunch.
Speaker 1:I think my first introduction was I was looking at, I was looking on TechCrunch and I saw that, you know, this company had raised 3.3 million, you know, in in venture funding, I think, from a couple like Mac VC, serena Ventures, and for me I was always like and yeah, cause I'm currently based in Houston, right, and for me I'm always like I want to, I want to learn as much as I can about what's going on in Nigeria, but I didn't really have.
Speaker 1:I mean, I would look at different news sites and different articles, different websites, but I didn't feel like I was getting that economic data in the way that I wanted to, like you know, to understand what's going on from an economic perspective, you know to understand what's going on from an economic perspective, and so for me I was just like, oh, let me go check these guys out. And that was how I got introduced to Steers. What would you say is the biggest obstacle that the team has had to overcome in building this company? You talked about getting to a place where you finally understand your customers, but in addition to that, what would you say is the biggest obstacle?
Speaker 2:It's going to sound cliche, but I say, um, finding the right people, hiring, um there's I just can't, yeah, talent, I just can't understate how having certain types of people in certain roles just changes the game. It's, yeah, it's, it can't be understated. At the same time, um, it doesn't mean that there's one person for the job and so you have to find that person, because usually there's a certain skill set you need and if you don't have it, there's just some things you can't do. Yeah, so I think that the story of Stairs, as much as it is a story of all the things I said, like the business, changing the business model, the kind of customers we had it's also been a change in the team, in the team's abilities, because of growth of individuals as well as because we've hired people who had already experienced that growth elsewhere and were able to point us in the right direction and say, hey, this is not how you do it, that's how you do it, or this is not how it should work. You should go over here and get this, or this is not where to get this data from. This is how you do it, this is how you get it.
Speaker 2:Of course, it helps to have money. So VC also changes the game. When we raised money who we were able to hire completely changed. If you don't have the cash to pay those people, then like I mean you can I mean the startups has a very romantic idea that you're working on something so fundamental and so true that when you explain it to someone, their tears rolling out of their eyes and they're like hire me, please take me, I will work with you. And I mean, the honest truth is just like people have kids and they need to eat and so like you need to pay them enough so that they are comfortable and they can focus. They can just relax, not worry about what's going on and focus on the job at hand.
Speaker 2:And when you are, when you're a small team of people starting a company, you usually have well, if you're lucky, you usually have less responsibility than that seasoned, experienced person in whatever field, but at the same time, because you have that less responsibility, you can take a pay cut and you can spend a bit of time fleshing out this idea, getting into a point where it has some legs before you get, like any of those people, into your team. Um, it's not to say like go find the big company and try to hire all the execs, because there's also the problem with um getting in, getting the wrong person if they're just not right for the company where it's at, they can't even execute what they would because the level you're at is not ready for them to do that.
Speaker 2:And if that's all they know how to do, then actually they're not useful at the stage that you're at. So there's like a balance to be had of knowing when to hire what type of person, and it's something that we've all had to learn and develop over time through trial and error and digging through the mud okay, okay, how would how would you say so talent?
Speaker 1:okay, looking at how at least the current economic climate in in nigeria, right, like, how would you say steers is navigating that you know, as a business? Um, I'm not so familiar with other markets you know, like I think you mentioned kenya, it's I believe it was one of them that steers is in. But for nigeria, how would you say steers is navigating that climate?
Speaker 2:yeah, nigeria is rough. Um, it's quite tough these days. There's like, I mean, I don't know if you saw, but the there's been a fuel hike in the last couple of days. Um, I mean before that it's not like fuel prices were low. Yeah, it's crazy, it's there's. I mean, that's not, that's just the icing on the cake. Right, there's non-constant electricity you have to supply yourself, um, basically everything you need to work. Um, the current FX trend is not pretty and that means that salaries are being devalued.
Speaker 1:So it's not just the currency.
Speaker 2:Of course, it's actually people's disposable income that's disappearing if they're earning in Naira and there's so many other issues here and there that just can make it a very tough environment to work. Of course, every environment has its ups and downs, um, yeah, there's there's nowhere that is completely free of problems, but I would say venture is a particularly tough environment to work in and, um, the company has taken the strategy of and trying to ensure that the people working in the company um have everything they need to, just, you know, to just like not. It's not because, like, they need to work, because I mean, of course, we want them to do a good job.
Speaker 2:But, honestly, sometimes people leave and that that's fine. Like, sometimes they move on, they want to do something else or they find something that's more suited to their needs. But, like, while they're there, while they're at the company, we just want to make sure that everybody is is comfortable enough, um, to the extent that we can. So, over the last I think, couple of months we've actually reviewed everyone's salaries. We've given um raises, without any performance base, of like multiple tens of percentages across the board, just just to level people, because I mean we were not paying very low or anything. We've been quite competitive.
Speaker 2:But, um, with devaluation, we were specifically in nigeria yeah because we hire outside nigeria as well and well, and actually the trend in some places outside Nigeria has not been negative. For instance, kenya's currency just appreciated recently compared to US dollar, I think. But anyhow, for our employees all in Nigeria, for our employees all in Nigeria, we've just raised everybody's salary because it was basically necessary to keep people able to buy the same thing at the start, just to use my salary in the same way I was going to use last month, even though the salary increased.
Speaker 1:Okay.
Speaker 2:And that's actually fine, I think, because a lot of people would be like whoa, I think in the West you don. And that's actually fine, I think, because a lot of people would be like whoa, I think in the West you don't see that very often. It's rare. Yeah, it's like well, you're still a startup, you got funding and you're making some money, but you're not super profitable or anything. You really just raise everybody's salaries and the truth is that if you, um, if you don't take care of the people well, I didn't say take care of like the, they take care of themselves but if you don't ensure that they have everything they need, um in this relationship, the company then they go do somewhere else Cause they're really smart.
Speaker 2:Basically, the market is competitive. If you have good people in your team, which we do, then there's a company that can pay them more. And if, like the last thing I told you about the romantic startup idea, it only works when you can only convince people to not maximize their economic input, or rather their pay, by just moving to the highest payer a bit, um, by giving them something worth their time to work on and um making it a good environment to do so okay, okay.
Speaker 1:So looking at looking at stairs in the future, like what important areas would you say like stairs is currently not covering but is looking to cover in the future, whether it's industries or you know, yeah, industries.
Speaker 2:Okay.
Speaker 1:That you can share.
Speaker 2:I would say we are going to expand our coverage likely of healthcare.
Speaker 1:Okay.
Speaker 2:We are already covering. We already have some on a bit of healthcare. We're already covering financial services, Um, and, as you were covering a few sectors already, um, and we are, we're really just looking to ensure that, uh, the current customers we have whatever sectors that they're most interested in, we cover, but then our role in the industry is more of an aggregator role. That the current customers we have whatever sectors that they're most interested in, we cover, but then our role in the industry is more of an aggregator role and we are content providers. We do make our own intelligence, but at the same time we aggregate. So really we want to cover all the sectors. It's a matter of time.
Speaker 1:Okay, so one sector that I am particularly interested in is the creative sector, right? So I know like that's a huge in nigeria and africa, like that sector is huge and it's it's growing, you know, especially between now and 2030 it's. It's growing, um, is that a sector that you know steers, because I know steers doesn't cover it currently because I checked, uh, but like, is that a sector that?
Speaker 1:you know, steers might explore in the future. Um, you know I'm using the word might so in case it's you're not ready to share um you know every sector is game.
Speaker 2:Every sector is game. The thing is, each sector, depending on what our customers need from it, will have a different type of coverage. So we could do sector reports that dig into some key issues or data, or just try and map the sector. What's going on, who are the suppliers, what does the industry look like, what are the different players and what they do, or what are the trends in the sector. We could also just aggregate data on the sector, like what are the companies operating there? Like who are the key players, the key people, key things to pay attention to? You know, key players, okay, key people, key things to pay attention to. We could figure out the competition in the sector. Um, there are a couple of different ways to cover it. So, okay, I think, yeah, we, every sector is game, but it'll depend on what, what exactly is needed when we get there okay, okay, sounds good, sounds good.
Speaker 1:Every sector is game. I'll keep that in mind. Um so, okay, what's? What's something you wish more people knew about you and your work at steers that maybe they don't currently know?
Speaker 2:that's a difficult question because I haven't thought about that let me think I think something interesting about us is our views on technology. So we see technology as an enabler of what we're doing. I know that's the basic view, so I'll get a bit deeper. In our industry in particular, there have been significant changes in the last couple of years. Gen AI is the most significant of them.
Speaker 2:I don't know if you saw the McKinsey reports and stuff about Gen AI saying, hey, this is not a blue-collar, because blue-collar you have blue color, white color yeah and blue color is, um, it's supposed to be more like manufacturing, and there's a lot of technology that people have um claimed will eliminate a lot of blue color jobs. Um, in the past, like, hey, these robots are coming out and um, now it's going to mean that factories are going to cut headcount, people are going to lose their jobs, people start getting worried and there are a lot of regulations designed to protect people from that, etc. A lot of reports have come out saying, hey, jni is going to get rid of a lot of white-collar jobs, a lot of analysis, a lot of data science, a lot of programming, a lot of information processing, and basically the technology wave right now is a large information wave, and information providers, data aggregators, people in our industry should be paying attention and we're paying attention. So that's something that's interesting about us. We're looking into integrating these technologies into not only our process but actually our user experience as well, because information processing now can be done.
Speaker 2:Quite the problem has always been how do you get from quantitative processing to qualitative processing? Even just on a human level, it's difficult, right? You have things like thematic analysis for qualitative data analysis, which is very different from making charts and graphs. Which, making charts and graphs, everyone can tell if you're doing the right job. But when we get into something like thematic analysis, it's all contextual, it's subjective, it's based on what you see, and so you have to have the domain expertise to pull out the right insights.
Speaker 2:Now you can get a computer to do that and it doesn't mean it's going to give you the same answer you would have come out with, but it does mean that the computer can do your analysis, and a computer can synthesize analyses and give you an answer. A computer can take a bunch of a database of information and you can ask it a question and it can give you a 20-page report detailing the answers. You can string all of that together to literally make whatever sequence of additional programming, because most of programming today is all conditional. I mean there's declarative type of stuff. Most of it is just if statements and control flow loops of like if this, then that.
Speaker 2:You know, the popular startups. If this, then that. They're basically like web programming Basically now, you can, or you should be able to. If this, then that any problem you want in natural language. You should be able to write out a paragraph describing what you want and let the computer do it, and that is game changing for all of our customers as well as our team.
Speaker 1:And so we're excited to see what happens next, so what we do attention to ai's role, specifically as it relates to going from quantitative to qualitative right, especially in this space yeah, yeah, well, going, going from processing quantitative information and qualitative information differently to synthesizing them and processing them in the same way.
Speaker 2:Yeah, I would say, pay attention to what Dias does on AI in the next couple of years.
Speaker 1:Okay, interesting, interesting, I love that, I love that. Okay, interesting, interesting, I love that, I love that. Um. And then here, how can, how can our audience, you know, including myself, um, like, how can our audience in nigeria, africa and, and you know, the rest of the world, contribute to the work that steers is doing? You can buy a license okay, okay, yeah, a subscription, well the corporate license is all we sell at the moment.
Speaker 2:Okay, so it might be a bit steep, but, yeah, if you can't buy a subscription, you can just pay attention to social media and pay attention to all the. We do have an open stair, so open data part of the platform that's accessible to anybody, so you don't actually have to have a corporate license to see that. And that covers democracy, it covers elections, it covers political risk. It's an interesting part of the platform that we pay attention to and we devote resources to 24-7. So, yeah, that's something else that you can if you just want to see what we're up to. But, yeah, pay attention to us on social media. We're usually on Twitter, instagram, instagram, linkedin. We're always posting, so, yeah, okay, okay, awesome.
Speaker 1:Thank you so much, boo. Um, I you know I really enjoyed this conversation because I think I've heard about cs for a while, right, um, but being able to go in depth. I had a like 10 minute conversation with preston but wasn't able to go as in depth, and so I appreciate you taking out the time to really like lay it out from the founding story to where Steers is now. So really appreciate it.
Speaker 2:No problem, thanks. It's been very nice chatting with you and hopefully I'll get to speak to you again in the future.
Speaker 1:Now if you got value from that noble conversation, to you again in the future.