Why AI-First SaaS Products Struggle in the Market

AI or Not to AI? The Dilemma Every SaaS Founder Faces 

We're living in the AI era.  Many founders today feel pressured to build an AI-first product because that's where the excitement is. The trend is understandable, but that does not mean your SaaS product has to be AI-first to be marketable and profitable. 

Journey with me!

Avoiding the AI-Powered Product Charade as a way to win the market

Every week, a new AI startup launches. Every product seems to be adding an AI copilot. Investors ask about AI. Some customers (maybe) ask about AI. Even founders feel pressured to mention AI somewhere (as a source of differentiation) in their product description, telling users it is a feature that is integrated into the product without caring if users really want it, let alone use it.

SaaS doesn't win the market simply because you build it as an AI-first product. In fact, I believe that when building a SaaS product,  founders should be cautious of ending up with a costly and complex product all in the name of building a product that is AI-powered, since that is the new way of things. 

Making a product AI-First means the architecture roughly follows something like this. 

User → Product accepts a problem (LLM Call) →solve it (LLM Call)→Present the solution to the User (LLM Call)

Every single step is routed through an LLM call. It feels modern. It feels "AI-native." It's also, in a lot of cases, a worse product than the boring alternative — and more expensive to run, forever.

I call this the "Wrong Product Thinking Approach": starting with "we have AI, where can we use it" This makes it hard to distribute because the moat is weak, if non-existent, because AI is more of a technology and not the solution that users pay for. My argument here is that users don't care what's under the hood. They don't know, and don't care, whether their answer came from GPT-4o, Claude, or a hardcoded function block that never made an API call in its life. They just want to see the result. In fact, they care more about whether it's fast, correct, and cheap enough that you're not passing the cost back. 

You, on the other hand, know exactly what's running behind that button. And you know exactly how much OPEX you're incurring for every LLM API call per user, per task or per session. The same goes for how much you are saving every time a problem gets solved without an inference call.

Building a Profitable SaaS while going with AI Flow

On average, plenty of profitable SaaS products don't really need AI to sell well. Think about products like inventory management systems, pharmacy management software, restaurant POS systems, and school management systems, among others. The core value of products is not making them AI-first for users. Rather, they come from making business operations smoother, faster, and more reliable, and that may not necessarily require the integration of AI to be achievable

Don't get me wrong: AI actually adds value, and I am not advocating against the use of AI or its integration into SaaS products. Far from it. I am making a case for building a profitable SaaS that people use and conveniently pay for. Instead of thinking/asking: "How Do I Add AI?" when working on an idea, you should be asking questions like:

• Is AI needed to solve this problem?
• Does AI create enough value to justify the added complexity?
• How does AI affect the cost of the product for the user?
• What is the overall business gain if AI is to be added to this product?

Questions like these need serious consideration because the real value of a SaaS should not be rooted in its AI-First Nature. Long before large language models became mainstream, as we have been seeing for some time now, SaaS products were already solving real business problems. Their real value is largely in the way they help businesses and individual users achieve one or all of the following:

• Save time
• Reduce human error
• Automate repetitive work
• Centralize information
• Standardize workflows
• Make everyday operations easier

None of those outcomes requires the SaaS product to be AI-first. They require solving a painful problem exceptionally well. Whether those outcomes come from AI or carefully designed automation is often secondary. AI is great and will continue to be. But there's a difference between using AI as a feature and making AI your entire product strategy. AI use and integration become incredibly valuable when it improves an already useful workflow.  SaaS builders who understand this are those who will continue to build and distribute what people use profitably.

Until next time!

Gambatte!

Startup Journey— Lessons From 15 Users (Only 4 Paying) After 1 Month of Building BizTrack-OS

 

What 15 Users (and Only 4 Paying) Taught Me About BizTrack-OS In One Month

A month ago, I launched BizTrack-OS publicly. BizTrack-OS is an all-in-one business management tool that helps SMEs track sales, inventory, debtors, expenses, and profit- all in one place. Today I have 15 users on the platform, and 4 of them are paying. If you're a fellow bootstrapper, you already know what those numbers mean: 

                Most people who try the thing don't stick around. That's not a confession, it's just                  the honest starting point for everything I actually learned this month.

I didn't build this in a vacuum. 

I talked to the people who signed up, watched how they used it, and asked what was getting in their way. While getting people to try, use and pay for your product triggers a kind of dopamine rush, it creates a dangerous illusion of completeness— a false sense that you've already found and fixed what matters, because there are things and blind spots that only constant, sustained usage from real paying users will ever reveal to you. 

Here's what they told me, and what I did about it.

• Pain Point 1 ("It's hard to find products in a 500+ item catalogue"): One user runs a business with hundreds of stock keeping units (SKUs). Every time they needed to log a sale, they were scrolling through an unsorted list trying to spot one product among hundreds. They called it tiring, and they were right to. This is the kind of problem you only catch by watching someone actually use your product, not by imagining how they'll use it. I hadn't designed for catalogue size at all — I'd designed for the businesses I knew, which were smaller. This user expanded what "BizTrack-OS user" even meant to me.

• Pain Point 2 ("Staff shouldn't be able to delete sales without my permission"): A different kind of problem — not a feature gap, a trust gap. This business owner wasn't worried about using the app incorrectly. They were worried about losing control of their own sales history to staff who had access but not ownership. That's not a UI request; it's a request for the app to understand who's actually in charge. This was the interpretation I gave to the concern. It made me realise I'd been thinking about "users" as a single role, when in reality there's the business owner, and there's everyone the owner trusts with day-to-day operations — and those two groups need different levels of permission.

• Pain Point 3 ("I need supplier names attached to my inventory"): A simpler ask, but a real one. For some businesses, knowing where a product came from matters as much as knowing what it is — especially when something needs to be returned, reordered, or questioned for quality. I'd built inventory tracking around the product. I hadn't built it around the relationship behind the product. And supplier relationships matter a lot for SMEs. 

• Pain Point 4 ("I can't see what a debtor actually bought"): This one stuck with me. A business owner extending credit to a customer needs to be able to show, clearly and without argument, exactly what was purchased and what's still owed. Without that breakdown, every conversation about an unpaid balance becomes a he-said-she-said. The feature request wasn't really about data — it was about avoiding awkward, trust-eroding conversations with customers.

• Pain Point 5 ("I need expiry alerts on perishable products"): For businesses selling anything with a shelf life, losing track of expiry dates is losing money, full stop. This user needed the system to remember what they couldn't possibly track manually across hundreds of products. It's a small feature with an outsized impact on actual business loss prevention, compliance and of course, consumer safety.

Lessons Learned—What this month actually taught me

None of these five requests was things I'd have guessed sitting alone writing code. They came from real friction, in real businesses, during real use of BizTrack-OS — a pharmacy worried about inventory size, a shop owner worried about staff trust, a trader worried about supplier accountability, someone extending credit worried about disputes, and someone selling perishables worried about waste. 

As a solo bootstrapper, this is the actual value of having users before you have scale. Fifteen people is a small number. But it was enough to show me five blind spots I didn't know I had, and all five are now shipped in, making BizTrack-OS a true business pain reliever for SMEs.

On the business side of things, the 4-out-of-15 or 26.67% paid conversion rate stings a little to share publicly, but I'd rather be honest about it than dress it up. Some of that gap is product timing, some of it is trust — a business handing over its sales and inventory data to a new tool is a real ask, and not everyone's ready to make that leap in week one. I extended the free trial from 7 to 14 days for exactly this reason; some of these businesses needed more than a week of usage before the value was obvious.

What I do know is that the four who converted didn't convert because the product was perfect. They converted because the product kept getting better, visibly, in response to what they told me. That's the whole hack that will enable me to carry them to next month: not chasing 100 signups, but listening hard enough to the first 15 that the next 50 have an easier reason to stay.

Final Thought—What I'd tell another Builder

If you're building something right now and wondering whether any of this applies to you, here's what I'd pull out of this month that isn't really about BizTrack-OS at all:

• Your first users are a research tool, not a vanity metric: 15 signups and 4 conversions look small on paper. But each of those 15 people handed me a blind spot I couldn't have found alone — staff permissions, supplier tracking, debtor breakdowns, expiry alerts, catalogue search. No amount of solo planning gets you there. If you're sitting on an MVP with a handful of users, the number itself matters less than whether you're actually listening to what those few people are bumping into.

• Low conversion isn't always a "you" problem: It's tempting to read 4-out-of-15 as a verdict on the product. Sometimes it's a verdict on trust, timing, or how much friction there is in someone handing their business data to a stranger's app. Before you panic-rebuild the product, ask whether the issue is what you built or how much runway people need before they're ready to commit to it. For me, that meant extending the trial — a pricing and timing change, not a complete product rewrite.

• Pain beats feature requests: Don't just collect what people ask for — understand why they're asking. "Add supplier names" is a feature request. "I need to know where this came from when something goes wrong" is the actual pain underneath it. Build for the second one, and you'll often solve problems the user didn't even think to ask about.

• Ship the fix while the pain is still fresh: Every pain point in this post got addressed close to when it was raised, not batched into some future roadmap I may never touch as the user abandoned the app. That responsiveness is the actual unfair advantage of being small — you can close the loop in days, not quarters. Use it.

On a final note, you don't need a hundred users to learn and understand your product better from a customer's perspective. You need a few who are honest with you, and the discipline to actually act on what they say to make your product better. That's a conversion logic.

Check BizTrack-OS Here

The Main Playgrounds in the Tech Ecosystem

Know the Four Playgrounds in the Tech Ecosystem

The tech ecosystem is not a flat environment. It is layered, dynamic, and structured around multiple arenas where value is created, captured, and scaled. To navigate it well, you need to understand not just what role you play (builder, enabler, connector, operator, or governor) but where you play it. The same skill set performs differently depending on the arena.

Think of these arenas as playgrounds. Each one operates under different assumptions about risk, speed, ownership, visibility, compensation, and career progression. What wins in one may fail in another. Choosing where to play is a strategy. Understanding how to win there is leverage. And at different stages of your career, you may find yourself committed to one playground or deliberately moving between them.

What exactly does  the Tech playground look like

The tech ecosystem has four major playgrounds: Big Tech, Startups, Enterprise & Institutional, and Independent. Each rewards a different kind of player.

1.  The Big Tech Playground

This is the arena of established giants. This is where you will find the likes of Google, Apple, Meta, NVIDIA, Microsoft, Samsung, Anthropic, as well as OpenAI, amongst other tech titans. These are companies with scale, structure, huge capital, and global influence. On average, the big tech environment is defined by:

• Specialised roles,
• Mature systems
• Clear performance ladders
• Strong compensation, and 
• Significant brand leverage.

For any tech professional in this playground, big Tech companies reward depth, consistency, and the ability to operate within complexity. Since systems here are already built on sophisticated infrastructure, the role of most professionals who find themselves here is to improve or scale them, not reinvent them. Impact is often narrow but deep, and success depends as much on navigating organisational dynamics as on technical output. Politics, cross-team alignment, and internal influence are real skills here, not distractions.

Winning in Big Tech requires mastering internal systems, building influence within structured hierarchies, and optimising for precision over improvisation. It is a game of depth and structural alignment.

2. The Startup Playground

If Big Tech is structured chess, startups are fast-paced street basketball. Startups generally operate with high uncertainty, limited resources, rapid iteration, and fluid roles. If you find yourself as a professional in this environment, you are unlikely to be confined to one lane because a startup modus operandi may necessitate a crack builder to also sell, an operator to shape product strategy, and a connector to define and implement a go-to-market strategy. The boundary between roles in this environment is thin and constantly shifting.

But one thing that makes the startup environment fun and thrilling is that startups reward adaptability, an ownership mindset, speed, and comfort with ambiguity. You are closer to both the creation of value and the reality of failure. Equity may replace salary stability, giving you huge leverage. Growth may replace structure. 

Winning in the startup playground requires resilience, initiative, and the ability to execute without a safety net or a clear playbook. It is a game of momentum and survival with feedback loops that are shorter and more brutal.

3. The Enterprise and Institutional Playground

This arena is underrepresented in most career conversations but absorbs a significant portion of the tech workforce. It includes large non-tech corporations undergoing digital transformation. Here you will find banks, healthcare systems, manufacturers, retailers, as well as government agencies, NGOs, and multilateral institutions deploying technology at a societal scale.

The environment here is defined by complexity, compliance, long decision cycles, legacy systems, and high stakes. Innovation moves slowly, but impact can be massive. Resistance and change management are constant things to deal with here because success has largely been built on legacy systems. Roles like enterprise architect, digital transformation lead, public sector technologist, and implementation specialist live here. The challenges are less about speed and more about navigating entrenched systems, stakeholder alignment, and institutional politics.

Winning here requires patience, systems thinking, and the ability to drive change inside organisations that were not built for change. It is a game of influence and institutional endurance.

4. The Independent Playground

This is the arena of freelancers, consultants, solopreneurs, and independent operators. No fixed salary. No predefined ladder. No organisational buffer between you and the market. You create value, package it, price it, and sell it repeatedly. It is the most flexible, brutal and risky tech playground to operate in because the reward only comes from serious grind. Think of this as the street where fighters fight in the absence of strict rules and regulations.

Particularly, this playground rewards market positioning, personal branding, negotiation, network leverage, and consistent delivery. Technical skill alone is insufficient here. You must also understand client psychology, how to communicate your value clearly, and how to build a reputation that generates inbound opportunities over time. 

Income variability is real, but so is the ceiling on what you can earn and the autonomy you can build. Long-term winning here requires strategic positioning, disciplined delivery, and relationship capital accumulated over time.  

So, which Playground Should I Start From

Most careers are not spent in a single playground. Many professionals start in Big Tech or Enterprise for stability and credential-building, move to startups for growth and equity exposure, and eventually transition to Independent work once their reputation and network can carry them. It is the other way around for a lot of other professionals, particularly starting from the independent playground like the freelance market.  

Understanding which playground you are entering is important. Knowing what it demands is the foundation of a deliberate career strategy. At the end of the day, where you start from doesn't matter. What is more important is your growth, how you scale and sustain a great career in a given domain within the broader tech ecosystem. 

The field of Artificial Intelligence is one such lucrative domain you can venture into and grow strategically. If this field appeals to you, I urge you to Enter AI using this comprehensive guide 

         Get Book Here

Get Started and Grow in Tech by Finding Your Place

Choose Your Place FIRST in the Tech Ecosystem 

The technology sector is arguably one of the most influential forces shaping modern life. It does not replace the importance of other sectors; rather, it amplifies their effectiveness. Agriculture increasingly relies on sensors, drones, and data analytics to improve yields and optimise operations. Healthcare uses software systems, artificial intelligence, and digital records to enhance diagnosis and patient care. Financial services depend on a complex software infrastructure to securely process billions of transactions while expanding financial inclusion. Logistics companies use optimisation algorithms, cloud platforms, and tracking systems to move goods more efficiently across the world.

The examples are endless.

Technology has become woven into the fabric of modern society. Nearly every industry now depends on digital systems, software, data, or automation to create value and remain competitive. For anyone considering or building a career in technology, this raises an important question: Where do you fit within this ecosystem?

Before learning a programming language, earning a certification, or building a portfolio, understanding the structure of the technology ecosystem may be one of the most valuable investments you can make.

The Myth That Tech Is Full

For years, people have claimed that technology is becoming saturated. Yet the ecosystem continues to expand. New startups emerge daily. Existing businesses continue their digital transformation efforts. Entirely new industries are being created around artificial intelligence, cybersecurity, cloud computing, robotics, and data-driven decision-making.

The technology ecosystem behaves less like a finite resource and more like an expanding frontier. Every year, new problems emerge, new products are created, and new opportunities appear. If breaking into technology has always been your goal, you have chosen a sector with extraordinary long-term potential. More importantly, YOU'RE NOT TOO LATE.

In many ways, entering technology today is easier than it has ever been, largely because of the following factors.

• Learning Is More Accessible: A decade ago, gaining technical skills often required expensive programs, specialised institutions, or significant geographic advantages. Today, anyone with an internet connection can access world-class resources covering software engineering, cloud computing, cybersecurity, artificial intelligence, product management, data analysis, and countless other disciplines. The knowledge barrier has never been lower.

• Building is More Accessible: In the past, demonstrating competence required access to companies, employers, or professional networks. Today, you can build projects publicly, publish code on GitHub for free, launch personal websites (without paying a cent), contribute to open-source projects, share your work on social platforms, and launch digital products and SaaS applications. You no longer need permission to demonstrate capability.

• Productivity Has Been Democratised: Modern tools have dramatically increased what individuals can accomplish. AI coding assistants, cloud platforms, no-code tools, low-code tools, and developer platforms allow individuals to build solutions that previously required entire teams. The barriers have not disappeared entirely, but they have become significantly lower. 

Technology is no longer accessible only to computer science students/graduates from related fields or people with privileged access to Ivy League institutions. It is increasingly accessible to anyone willing to learn, practice, remain consistent, and endure the occasional 2 a.m. learning session.

Technology Ecosystem and Roles Within it

The technology ecosystem is not a single industry. It is a vast network of disciplines, organisations, markets, professionals, and technologies that work together to create and deliver value. Fields such as software engineering, artificial intelligence, cybersecurity, cloud computing, blockchain, data science, product development, and information technology each represent important components of this larger system.

Although these disciplines have different tools, methodologies, and objectives, they rarely operate in isolation. Instead, they depend on one another. This interconnectedness is what makes technology an ecosystem rather than merely a collection of separate professions.

Understanding this ecosystem is critical because breaking into technology is not simply about acquiring technical skills. It is about understanding where you can create the most value. This is what I want you to know and look into before you even write your first programming language. 

Generally, every company, team, and technology product is supported by individuals performing different but complementary functions. These functions can be grouped into five primary categories as follows:

• Builders: Professionals in this category create the core technology. They design, develop, and improve the products, systems, and intellectual assets that power modern organisations. Examples include Software Engineers, AI Engineers, Machine Learning Researchers, Data Scientists, Product Designers, and Hardware Engineers.  Builders transform ideas into reality. If you are the type that enjoy creating things, solving technical problems, and developing products from scratch, this category may be your natural home.

• Enablers: Professionals in this category provide the infrastructure, platforms, and operational foundations that allow builders to work effectively. Examples include DevOps Engineers, Cloud Architects, Platform Engineers, Systems Administrators, and Site Reliability Engineers. While builders create the product, enablers ensure the product can be built, deployed, scaled, monitored, and maintained efficiently. Without enablers, innovation slows down.

• Connectors: Professionals in this category bridge the gap between technical complexity and human understanding. They help organisations, customers, and stakeholders understand, adopt, and trust technology. Examples include Solutions Architects, Developer Relations Specialists, Technical Account Managers, Sales Engineers, and Technical Consultants. Connectors are translators. They convert technical capabilities into business value.

• Operators: Operators bring products to market and ensure those products generate meaningful outcomes. Examples include Product Managers, Growth Specialists, Marketing Professionals, Sales Teams, and Customer Success Managers. Building a great product is not enough. Operators ensure products reach users, solve real problems, and generate adoption, revenue, and impact.

• Governors: These professionals ensure that technology remains secure, ethical, compliant, and sustainable. Examples include Security Engineers, Risk Managers, Compliance Officers, Legal Professionals, AI Governance Specialists, and Privacy Experts. As technology becomes increasingly powerful, governance becomes increasingly important. In fact, Governors are no longer operating on the sidelines. They are becoming central participants in how modern technology is developed and deployed.

Professionals across these categories work hand-in-hand. Together, they create the products and services that millions of people use every day.

Where Should You Start?

One of the most common mistakes aspiring professionals make is focusing immediately on tools instead of positioning. People ask: 

"What programming language should I learn?" 

"What certification should I get?"

"What framework should I study?"

These are important questions, but they come after a more fundamental one: Who do you want to become within the tech ecosystem?

Your answer will determine the skills you need, the projects you build, the communities you join, and the opportunities you pursue. It is also important to recognise that many careers sit at the intersection of multiple categories. In fact, most careers in tech sit at the intersection of multiple categories. A Product Manager operates between Connectors and Operators. A DevSecOps Engineer operates between Enablers and Governors. A Founder often works across all five categories simultaneously.

Your primary category provides direction. Understanding adjacent categories provides leverage.

The professionals who become exceptionally valuable are often those who develop deep expertise in one area while gaining enough understanding of neighbouring areas to collaborate effectively.

Final Thoughts

Breaking into technology is no longer primarily a problem of access. The resources exist. The tools exist. The opportunities exist. The greater challenge is developing clarity.

Technology is a vast ecosystem filled with builders, enablers, connectors, operators, and governors. Every successful product, startup, platform, and organisation depends on the coordinated efforts of these groups. Your goal is not to chase the most popular role. Your goal is to understand where you can create the most value.

Once you understand your place within the ecosystem, learning becomes more focused, your career decisions become more deliberate, and your long-term growth becomes more sustainable.

The technology ecosystem is still expanding. The opportunities are still there. The question is no longer whether there is room for you. The question is: Where will you position yourself?

Until next time

Gambatte.

The Nigerian SaaS Market Reality

 Building for Value: How to Win the Nigerian  SaaS Market

 

The global SaaS playbook is often written from Silicon Valley assumptions: find a painful problem, build a solution for it, ship fast and ugly, distribute, and keep improving as it grows. The strong tech culture, mature infrastructure, and deep digital adoption in high-income countries naturally favour the SaaS model.

Subscriptions are normal. Trust in recurring payments is high. Users are willing to pay for software quickly — even for products that are boring, hilarious, exciting, or somewhere in-between.

But when you bring that same model into Nigeria, things shift. Not because the ideas are weak. Not because there is a shortage of talent. But because the environment is notoriously different. From struggling to onboard users, to weak monthly recurring revenue (MRR), to high churn rates, SaaS in Nigeria faces realities that many global startup playbooks do not prepare founders for.

Having built, shipped, distributed, failed, and won with products in this market, here are some things I have learned about building SaaS in Nigeria — and what I think every indie hacker building for this market should understand.

What I am Finding Out

1. Weak willingness to pay for software

The average Nigerian has a low disposable income level and is extremely cautious about how money is spent. Unless a product is critical, helps make money, or has some clear financial reward attached to it, many users are unwilling to pay for software. This is why bootstrapped B2C SaaS products may struggle when monetized aggressively from Day 1 especially if the problem is something people can “work around.” A prospective user once told me:

"Although your product makes this easier and faster, I can still do it manually. It will just take me two days".

That conversation changed how I started thinking about SaaS in Nigeria. In many mature markets, speed and convenience alone are enough reasons to pay for software. But in Nigeria, many users are willing to trade time for money if the manual alternative still works. Not everyone thinks this way, of course. But there is a large segment of the market that will not pay purely for productivity gains unless the financial value is extremely obvious.

The same thing applies to B2B SaaS. Nigerian businesses, especially SMEs, are built around survival, not software optimisation. Instead of paying for tools, many businesses would rather stick to free alternatives or manual processes that “still get the job done.” That reality changes how SaaS must be designed here.

2. Low trust in recurring billing

Monthly subscriptions still struggle heavily in Nigeria because recurring payments are not deeply normalised. Many users fear unexpected deductions, prefer manual control over payments, and are unfamiliar with subscription-based systems. This makes the classic SaaS “monthly billing flywheel” harder to activate compared to mature markets. In fact, many Nigerians are likely to cancel a free trial before it ends simply because they are afraid their card will be charged automatically.

That fear translates directly into churn. Something that is bad for a SaaS in its early stage.

3. Market fragmentation and small ARPU

Nigeria’s economy is dominated by SMEs, which sounds attractive for B2B SaaS — until you zoom in. The market is heavily fragmented into clusters of very small businesses with low average revenue, high price sensitivity, and of course, a traditionally unstable operating conditions. Even when businesses adopt software, pricing power remains limited because many businesses are constantly fighting rising operational costs. A premium SaaS product may genuinely improve efficiency, but if the business owner cannot clearly justify the recurring expense every month, retention becomes difficult.

4. Distribution is harder than the product

In mature markets, SaaS growth is often driven by paid ads, SEO, product-led virality, marketplaces, and mature acquisition systems. Nigeria is different. Here, growth is still heavily driven by WhatsApp selling, personal relationships, referrals, manual onboarding, direct outreach, and sometimes even field sales. Building the product is only half the work. The other half is aggressive human distribution.

What I am Learning to Do

Building and scaling SaaS in Nigeria is absolutely achievable. But winning here is not about blindly copying global SaaS templates. It is about adapting to local realities while solving painful daily business problems. I have observed that the products that win here usually do one or more of these things:

• Help users save money,

• Help users make money,

• Reduce operational chaos or improve survival directly.

Here are some approaches I finding to work in this building such product for the market

1. Design and build around value

Nigerians love free solutions — but they are willing to pay for obvious long-term value. This is why SaaS products that solve painful problems and demonstrate value early tend to perform better. For example, a product that allows users generate a 10-page document for free and only charges when they want to export it will likely outperform a product that tries to monetise from the beginning before users experience the value fully.

The logic is simple:

• Let users experience the value first,
• Get them hooked through usefulness,
• Then monetise at the point of highest perceived value.

That model tends to work better in this environment.

2. Design SaaS around a win-win strategy

The “You win, I win” strategy is one of the biggest SaaS growth hacks in Nigeria. Software that helps businesses cut costs, reduce losses, improve visibility, generate leads, or increase revenue will almost always perform better than software built purely around convenience. 

Everyone is fighting difficult economic realities. So when a SaaS product creates a visible net win for the user, adoption becomes easier. For example products like inventory systems (that reduce theft, loss, or damage), software that helps businesses attract more customers, or tools that improve operational efficiency directly tend to gain traction faster because the value is measurable.

In Nigeria, businesses rarely pay for software because it is “cool.” They pay because it helps them survive better.

3. Less features, more affordability

Whether B2B or B2C, affordability is a major competitive advantage in Nigeria. People are already used to solving problems with Excel, Google Sheets, WhatsApp, notebooks, and manual workflows. So a costly SaaS product (even if technically better)  may struggle if the pricing feels disconnected from local realities.

The average Nigerian is highly price-sensitive and constantly searching for the cheapest reliable solution available.This is why simpler, more affordable products often outperform feature-heavy “premium” solutions in this market even if the UI is less polished or the feature list is smaller.

4. Simplify pricing and payment models

Globally, SaaS thrives on recurring subscriptions because predictable MRR and ARR are attractive business models. But Nigeria does not always behave like mature SaaS markets — especially in B2C. Many users are uncomfortable leaving their card details on payment gateways for automatic monthly deductions. They want both security and control. This is why pay-as-you-go, wallet systems, usage-based pricing, and one-time top-ups work well in this market. Users like feeling in control of when and how they are charged and this work for most SaaS in the local market. When people believe they only pay when they actively use a feature, trust improves and churn reduces significantly.

Final Thoughts

One of the biggest mistakes founders make is assuming the SaaS model is universal. It is not. Winning in the Nigerian market is not about copying foreign SaaS strategies word-for-word. It is about adapting to local realities, local psychology, and local spending behaviour.

The opportunities here are still massive. SaaS in Nigeria is far from dead. But the founders who will win are the ones who understand that Nigerian users behave differently — and design accordingly. Once you understand the environment, the psychology, and the economics of the market, building sustainable SaaS in Nigeria becomes far more achievable. 

Until next time,

Gambatte!

The Problem With Building in Silence

Why I Started Building in Public After 10 Years of Growing in Silence

The Context

Before I became a builder of AI systems and SaaS tools in the indie hacking space, I never truly understood the importance of building and sharing in public. For almost 10 years, while writing research papers and delivering data analytics solutions to individuals, businesses, and institutions, my routine was almost always the same:

• Work behind closed doors, wrestling with difficult problems late into the night — sometimes waking up at 3 a.m. to finish tasks I couldn’t solve the previous day.
• Work out by 6 a.m. while listening to street jams — a way of disconnecting from work and, ironically, recharging for the tasks ahead.
• Begin the real workday at 9 a.m., continuing from where I stopped while racing against deadlines
• Deliver pending solutions to clients or attend long strategy and consultation sessions.
• Wrap up one sprint, move on to the next problem, and repeat the cycle all over again

This was my normal routine as a researcher and data analyst. And through all of this, I shared almost nothing publicly. I got paid for my work — something I remain deeply grateful for — but the highs, lows, lessons, failures, and breakthroughs stayed private. That is simply the nature of research writing and analytical consulting. Client privacy is non-negotiable. Silent execution pays the bills. You accept the task, wrestle with uncertainty through long nights, deliver the solution, and move on. The “ta-da” moment, if it exists at all, is usually invisible to everyone except you and the client.

Every profession has its own rules, and for years, those were the rules I operated under...and still operating with to some extent.

The Change

That mindset of silent execution followed me into the indie hacking space. Around 2021, after spending much of the 2019/2020 pandemic period expanding beyond research and data analytics into software engineering, I started building more technical systems independently. I taught myself Python more deeply, explored scalable system architecture, and began experimenting with real software products. But even then, I still shared almost nothing publicly.

• No posts about learning how to code.
• No documentation of the struggles.
• No reflection on big howlers and small wins. Not when I built my first calculator with Python. Not when I developed a restaurant ordering system using Python and SQLite3. Not even when I locally deployed my first AI chat wrapper using the Mistral 7B API through OpenRouter ...and later mistakenly pushed the API key to the github repo for an app no use eventually!

Even after deploying a working application into a real production environment — an app that is still running to this day — I said almost nothing about it publicly. My entire journey existed mostly in my head. The issue was not lack of confidence. I was willing to ship products and push systems into production environments, sometimes even before writing proper test scripts!. And the problem was not necessarily the quality of the products either.

The real problem was that of mindset.

I came from a professional culture where solving problems quietly was considered normal. Visibility was optional. Documentation was unnecessary. Public scrutiny was avoidable. But the indie hacking world operates differently. Eventually, I began to understand something important: In the modern creator and startup ecosystem, building alone is no longer enough. You also need to document, communicate, and share PUBLICLY

The Turning Point

Beginning my indie hacking journey seriously in 2023 changed my perspective completely. For a long time, I was exploring AI systems, data analytics tools, and SaaS ideas in isolation. I would build things, break them, rebuild them, learn new technologies, and quietly move on to the next idea. But over time, I noticed a pattern: I was learning a lot, but none of that learning was compounding publicly because nothing was discoverable. I was largely waiting for my work to be discovered instead of pushing it to public discovery. 

It took me a while to realize that the growth dynamics of indie hacking are fundamentally different from the world of research ghostwriting and private consulting. In the indie hacking space, the rules of the game are different. It says:

• Don’t just learn and build in isolation.
• Document it. 
• Share it, and your experience. 
• Let people see the journey.

Understanding these as the basic rules changed everything for me. Almost every app and products I have been building are done in the public. If I don't do, I don't get to know what people think of it and how to make it better. A testament to this is when I was building Bayantx360-Suite as a no-code platform for advanced statistical analysis for researchers, students and data analysts. Feedback from early users was crucial for me in developing and integrating features that they truly need and eliminates what I thought they might want. 

The Results

Building in public became a major turning point in my journey. By sharing the tools I use, the systems I am building, the mistakes I make, and the lessons I learn, I began improving more than just the discoverability of my work. I was also improving across clarity of thought, communication, consistency, accountability, and long-term professional leverage.

Instead of isolated learning cycles disappearing into memory, my experiences began turning into assets: access to mentorship/guidance, industry exposure, product launches, and collaborations that I believe will compound over time because the general public (startup founders, technical leads, developers, hiring managers, product users, policy makers, etc.) is not just seeing only the products/solutions I am working on, but also the thinking behind them. 

The impact of building in public is reflected in the early success of Bayantx360-Suite as a no code tool for non-technical users. They basically find it a useful statistical tool to generate custom dataset of any size, automatically clean messy dataset, fix factor loading issues that makes a dataset failed Exploratory Factor Analysis (EFA), as well as run panel regression without writing a single line of code, without installing complex software, or paying for huge license fee. Building in public helped me to inject and build what user think, need and wants into Bayantx360-suite, and make it a really useful product.

The Lesson

Having experienced both worlds (silent execution in research/data analytics, and public building in indie hacking), I have learned something important:

• When people build entirely in isolation, they still learn, experiment, fail, and improve. 
• But that growth remains limited to their private mental scope.

Building in public changes that. It forces thinking to become clearer. It makes progress measurable. It allows learning to compound over time. And most importantly, it makes work (and the owner) more discoverable. This is because they are not just building only for delivery but also building a reputation, a network, and a long-term narrative around their skills and ideas. It is a transformational growth strategy.

Here’s my takeaway for anyone building a career in the tech ecosystem — and in the professional world at large: Play by the rules of your profession, but never neglect the importance of building yourself and your work in public. Technical competence will always matter.  Deep work will always matter. But in today’s world, visibility matters too. A mid-level professional with strong visibility, documented work, and a clear public narrative will often encounter more opportunities for growth and career advancement than a highly skilled expert whose work remains largely invisible. This is because people cannot value what they never see.

• Building in public is not about showing off. It is about:
• Documenting growth
• Creating discoverability
• Compounding learning
• Having a traceable digital footprint and positioning yourself to attract opportunities
• Building long-term professional leverage.

In many ways, visibility has become part of the skill set itself. Cultivate it.

See you next time, Gambatte.