DEV Community: Juliet Ogochukwu

How to Conduct Market Research for a Startup: A Practical Guide"

Juliet Ogochukwu — Thu, 29 Oct 2026 22:51:51 +0000

Building a startup around an interesting idea is one thing. Establishing whether that idea solves a problem for a reachable group of customers is another.

Market research helps founders investigate that gap before committing development time and resources.

Whether you're building a software product, a solution or a technology-enabled business the underlying questions are similar: Who needs this? How do they solve the problem today? What evidence suggests they would consider an alternative?

Here is a practical framework for answering those questions.

## 1. Start with a problem hypothesis

Write down the problem you believe exists before focusing on the solution.

For example:

Hypothesis: A particular customer group struggles with a recurring workflow.
Question: How does that group handle the workflow today?
Evidence to seek: experiences, current processes, limitations and the consequences of the problem.

Keep assumptions from findings. This makes it easier to identify what your research has established and what still needs testing.

## 2. Define a customer segment

Avoid starting with a market description that is too broad to investigate effectively.

Narrow the segment using factors such as industry organization size, user role, operating environment and purchasing process.

In B2B settings distinguish between the user who experiences the problem the person who evaluates a solution and the stakeholder who approves spending. They may have requirements.

## 3. Combine primary and secondary research

Primary research gives you insight into customer experiences. It can include interviews, surveys, workflow observations and feedback on concepts.

Secondary research helps establish context through industry reports, public statistics, trade publications, competitor information and relevant studies.

A useful customer interview question is:

"What did you do the time this problem occurred?"

This encourages a discussion of behavior rather than a hypothetical response to your idea.

Remember that interviews can reveal patterns and generate hypotheses. A small sample does not necessarily represent the whole market.

*## 4. Investigate alternatives, not competitors
*
A competitor does not have to offer the same product.

Potential customers may already use:

Existing products
Internal tools
Manual processes
A workaround involving several systems
No solution at all

Investigate why customers use their current approach what limitations they encounter and what would influence them to consider a change.

This can help you understand the problem clearly before deciding which features to build.**

5. Estimate the market with assumptions

**
Three common market-sizing concepts are:

TAM: Total Addressable Market.
SAM: Serviceable Available Market.
SOM: Serviceable Obtainable Market.

Use industry sources where possible and complement top-down estimates with bottom-up calculations based on identifiable customers and plausible purchasing patterns.

For example multiplying the number of customers by an assumed annual contract value can produce an initial opportunity estimate.. That calculation does not establish how many customers will actually buy.

Be explicit about assumptions, customer reach and the limitations of the esti**mate.

6. Test the assumptions**

Research should lead to experiments.

If you're uncertain whether the problem is important interview relevant customers. If you're uncertain about the proposed solution test a prototype. Demonstrate an early concept. If the purchasing process is unclear investigate budgets and approval requirements.

Different signals provide levels of evidence. Someone expressing interest is not the same as someone agreeing to evaluate a prototype or purchase a product.

Choose tests that answer a question and use the results to decide what to investigate next.

## 7. Create a research summary that drives decisions

A research summary should identify:

The customer segment investigated
The problem and evidence supporting its importance
alternatives and their limitations
Market-sizing assumptions
Signals of demand
Unresolved questions
The next experiment or decision

The outcome might be to proceed refine the target segment change the proposed solution or pause development. Research is valuable when it helps you make that decision deliberately.
**

8. Keep learning after launch**

Market research is not a one-time exercise. Customer needs, competitors and operating conditions can change.

Continue gathering feedback as you develop your product and business model. Revisit assumptions when new evidence challenges your conclusions.

For teams developing industry-focused ventures customer validation can help connect opportunity selection with operational needs and subsequent development. Learn more about Aperture Venture Studio if that venture-building context is relevant to your interests.

Final takeaway

Good startup market research is not about collecting the data or proving your idea is right.

It is, about asking questions examining credible evidence and testing assumptions before making consequential decisions.

What research question would you want answered before committing to building your product?

startup #entrepreneurship #business #research

How to Turn Real-World Problems Into Profitable Business Ideas

Juliet Ogochukwu — Sat, 24 Oct 2026 11:54:22 +0000

How to Turn Real-World Problems Into Profitable Business Ideas

successful products don’t start with groundbreaking technology. They start with a problem.

It might be a factory that relies on equipment inspections a warehouse that struggles to track inventory or an operations team that makes decisions using outdated data.

For developers, founders and technical teams these everyday challenges can reveal opportunities to build products—and potentially profitable businesses.

The key is learning to identify the problem before investing too much time in a solution.

1. Find Problems Worth Solving

Start by observing how people work.

Look for tasks that're repetitive expensive error-prone or difficult to scale. Pay attention to workarounds spreadsheets used as systems and processes that depend heavily on manual input.

In environments potential problem areas include:

Unexpected equipment downtime
Inefficient inventory tracking
Manual quality inspections
Limited visibility into machine performance
Delays caused by disconnected systems

A problem becomes a stronger business opportunity when it occurs frequently creates measurable costs and affects people who are motivated to solve it.

2. Talk to the People Experiencing the Problem

It's tempting to jump into coding a prototype. Resist that urge until you understand the problem.

Talk to users and ask questions such as:

How do you handle this process today?
What happens when something goes wrong?
How time or money does the current approach cost?
What solutions have you already tried?
Who would approve the budget for a solution?

Focus on their behavior rather than whether they simply like your idea.

A user saying a product sounds interesting is not the same as a customer demonstrating a willingness to pay for it.

3. Explore Where AI and IoT Can Help

Some problems require more than a software application. They may involve equipment, real-time data or decisions that need to be made before a failure occurs.

This is where the Internet of Things (IoT) intelligence (AI) and AIoT—the combination of AI and IoT—can become useful.

Consider an examples:

Predictive maintenance: Sensors collect vibration, temperature or other equipment data. Machine-learning models can help identify patterns associated with failures.

Inventory visibility: Connected devices and tracking systems help businesses monitor stock levels and asset movement.

Quality control: Computer vision can assist with identifying defects or inconsistencies during manufacturing inspections.

Operational optimization: Data from machines and business systems can help teams identify bottlenecks and improve resource allocation.

The important distinction is that these technologies are tools, not business models. Start with the problem then determine whether AI, IoT or a simpler solution is the best fit.

4. Validate the Idea Before Building Too Much

Once you've identified a problem and a possible solution test your assumptions.

You don't always need a developed application to find out whether an idea has potential.

Try a validation process:

Interview potential customers.
Document the workflow and its costs.
Create a prototype or minimum viable product (MVP).
Test it in a realistic environment.
Measure whether it improves the outcome that matters.
Ask whether the customer would pay to continue using it.

For solutions validation should also consider integration with existing systems, data quality, cybersecurity, reliability, deployment costs and the practical realities of the work environment.

A impressive prototype isn't enough if it cannot be implemented reliably or deliver measurable value.

5. Evaluate the Business Potential

A solution isn't automatically a profitable business.

Before scaling investigate the fundamentals:

Customer demand: Is the problem important enough to justify spending money?
Willingness to pay: Does the expected benefit justify the price?
Implementation costs: How time and support does each deployment require?
Repeatability: Can the solution work for customers without extensive customization?
Scalability: Can revenue grow without costs increasing at the rate?
Competitive advantage: What makes the solution meaningfully better than existing alternatives?

For example a predictive maintenance product may reduce downtime. Its business case depends on more than model accuracy. It must also deliver results, fit into maintenance workflows and produce savings that justify the investment.

6. Build Around the Outcome, Not the Technology

One common mistake in technology entrepreneurship is starting with a technology and searching for a problem it can solve.

A better approach is to work from the desired outcome.

Of asking, "How can we use AI in manufacturing?" ask, "How can we help manufacturers reduce unplanned downtime?”

That shift changes the development process. It helps teams define requirements select appropriate technologies and measure success using business outcomes rather than technical metrics alone.

The same principle applies beyond technology: understand the need validate the solution and build only what creates genuine value.

From Problem Discovery to Venture Building

Turning an idea into a company requires more than writing code. It combines customer discovery, technical feasibility, market validation, commercial strategy and execution.

This is particularly important for ventures applying AI and IoT to challenges, where a solution must work in the real world—not just in a demo.

Aperture Venture Studio focuses on building and scaling ventures at the intersection of AI and IoT for applications. Learn more about its approach on the Aperture Venture Studio website.

Final Thoughts

Profitable business ideas often emerge from problems that people already experience but haven't been able to solve

If you're a developer or aspiring founder start by observing workflows talking to potential customers and measuring the cost of the problem. Then test the solution that can demonstrate meaningful value.

Find the problem first. Validate the demand. Build the solution. Measure the impact.

That's a foundation for a business, than technology alone.

Industrial AI Startups: Seven Questions to Answer Before Building

Juliet Ogochukwu — Wed, 21 Oct 2026 04:13:37 +0000

An evidence-led framework for evaluating feasibility customer demand and commercial viability.*

Artificial intelligence is creating opportunities across manufacturing, logistics, asset management and industrial operations. Connected devices can provide visibility into environments while AI systems can help organizations interpret operational data and make better-informed decisions.

An industrial AI opportunity is not automatically a viable startup.

Between a concept and a sustainable business lie several difficult questions: Is the problem important enough to solve? Can the necessary data be accessed? Will the technology operate reliably in the target environment? Can the customer justify the cost?. Is there a realistic path from a pilot to commercial adoption?

Founders can reduce uncertainty by validating their assumptions before committing time and capital.

1. What problem are you solving?

Broad propositions such as "bringing AI to manufacturing" do not provide information to evaluate a business opportunity.

A stronger starting point is a defined problem affecting a known customer segment. Examples include equipment downtime, inaccurate inventory records, inefficient inspection processes or limited visibility into the movement of assets.

The next step is to understand the consequences. How frequently does the problem occur? What resources does the current process consume? Does it create production delays, quality issues, unnecessary labor or other measurable costs?

The objective is not to assume that every inefficiency represents an opportunity. It is to identify a problem that customers consider sufficiently important to address.

What to validate: The affected workflow, the frequency of the problem its consequences and whether the customer considers it a priority.

2. What evidence confirms that customers care?

Customer discovery should begin before product development.

Interview operational users, technical stakeholders and budget owners. Ask them to describe incidents existing workarrows, previous attempts to solve the problem and the limitations of current solutions.

Focus on behavior and evidence than enthusiasm for a proposed product.

A manager who says an idea sounds interesting may be offering encouragement than expressing purchasing intent. A customer who agrees to evaluate an approved dataset, defines pilot requirements or identifies a funding route provides more actionable evidence.

Founders should also examine alternatives. An established system, a process improvement or a simpler analytical tool may solve the problem without requiring an AI platform.

Understanding these alternatives helps clarify where a startup could deliver value.

What to validate: Repeated evidence of the problem limitations in existing solutions the people. Concrete next steps from potential customers.

3. Is the required data accessible and useful?

Industrial AI depends on more than the availability of sensors or large datasets.

Relevant information may be distributed across equipment controllers, maintenance systems, enterprise software, inspection records and human observations. Data may be incomplete inconsistently labeled or collected under conditions that differ from the intended deployment environment.

Before building a model establish what data the use case requires and whether it can be obtained lawfully and reliably.

Assess data ownership, access permissions, quality, timestamps, historical coverage and the availability of outcome labels. Determine whether the solution needs sensing infrastructure or integration with existing systems.

These questions expose dependencies that might otherwise emerge late in development.

What to validate: A legitimate path to data access, a data-quality assessment, labeling requirements and any infrastructure or integration dependencies.

4. What is the smallest experiment that could disprove the idea?

A startup does not always need to begin with a viable product. The appropriate test depends on the uncertainty.

If customer demand is uncertain, interviews and workflow observations may provide the useful evidence. If model feasibility is uncertain a limited prototype using approved data may be appropriate. If the business case remains unclear a scoped customer pilot may be necessary.

Consider a computer-vision system intended to identify product defects. An early experiment might compare its results against a labeled sample reviewed by inspectors.

The team could evaluate detection performance, false positives, missed defects, inspection time and the consequences of classifications.

A favorable result would justify investigation, not an immediate claim that the product is production-ready. Live environments introduce variability, integration requirements and operational risks.

What to validate: A stated hypothesis, a limited test, measurable success and failure criteria and a decision tied to the results.

5. Can the customer measure the benefit?

Technical performance and business value are related,. They are not interchangeable.

A system that detects anomalies accurately may still fail to reduce costs if employees cannot act on its alerts or if the underlying problem rarely causes material losses.

Before a pilot begins establish a baseline. Define the intended outcome. Depending on the use case metrics might include downtime avoided, inventory discrepancies reduced, inspection effort saved or equipment utilization improved.

Estimate the cost of deployment as well. Hardware, installation, integration, training, connectivity, maintenance, support and cybersecurity requirements can materially affect the business case.

The economic analysis should distinguish improvements from assumptions and account for uncertainty in both costs and benefits.

For example a hypothetical system expected to generate $60,000 in savings and costing $45,000 to deploy and operate in its first year appears to offer a $15,000 benefit before other costs and uncertainties. That estimate is only meaningful if the expected savings can be supported by evidence. The figures are illustrative, not a forecast.

What to validate: A measurable baseline, total costs, a defined outcome and a clear separation between observed improvements and projected benefits.

6. Can the solution fit the customers environment?

Industrial technology is deployed within existing systems not in isolation.

A potential customer may require compatibility with legacy equipment, specific data formats, reliable offline operation, controlled access, auditability or integration with established maintenance workflows.

Security, safety and operational continuity can influence purchasing decisions much as model performance.

For applications that inform decisions founders should specify how uncertainty is communicated, when human approval is required and what happens when the system fails or encounters unexpected conditions.

These requirements are part of product design and commercial validation. They should not be postponed until after a demonstration.

What to validate: Integration requirements, security and operational constraints, human oversight and how the system will be maintained and supported.

7. Is there a path to commercial adoption?

A pilot is an experiment, not proof of a business.

Founders need to understand who owns the problem, who approves spending, how technical evaluations work and which procurement or compliance steps are required. They should also establish what evidence would lead a pilot customer to purchase and what conditions might prevent expansion.

A successful technical evaluation does not automatically produce a contract. The solution must fit the customers budget, purchasing process, operating environment and expectations for support.

A strong validation process ends with a decision than an indefinite sequence of experiments.

Proceed when the problem is significant the technology is feasible the economics are credible. Customer commitment is developing.
Refine when evidence reveals a valuable use case, a better target segment or a solvable technical or commercial obstacle.
. Pivot when the problem lacks importance essential data cannot be accessed the economics remain unconvincing or customer interest does not translate into meaningful commitment.

The decision should reflect the quality of the evidence, not the amount of effort already invested.

A Practical Validation Checklist

Before committing to product development ask:

[ ] Can we describe the customers problem and its consequences in specific terms?
[ ] Have we spoken with users, technical stakeholders and budget owners?
[ ] Is there evidence of a need beyond general enthusiasm?
[ ] Can we access the data and systems the solution requires?
[ ] Have we tested the riskiest assumption with a limited experiment?
[ ] Can we measure the expected improvement against a baseline?
[ ] Have we accounted for deployment, integration, maintenance and support costs?
[ ] Do we understand the path from a pilot to a purchasing decision?

Not every question needs an answer before development begins. However unresolved assumptions should be visible prioritized and tested in an order that limits investment.

Building an AI startup requires more than a promising model or an impressive demonstration. It requires a customer problem, accessible and useful data, reliable performance in the intended environment, measurable economic value and a credible route to adoption.

The effective validation process starts with the assumption that could invalidate the business and tests it as early, as practical.

Start with the problem. Test the assumptions. Measure the outcome. Let the evidence determine what to build.

Venture Studio vs. Startup Accelerator: How Should Founders Choose?

Juliet Ogochukwu — Mon, 19 Oct 2026 22:31:07 +0000

When you start a company the help you need at the idea stage can be very different from the help you need when your business is already running.

This is why founders often compare venture studios and startup accelerators. Both venture studio and startup accelerator can help a business grow. They differ in how they work with founders the resources they give and the role they play in building a company.

Understanding these differences can help you decide which model is worth exploring and which questions you should ask before committing.

*## What Is a Venture Studio?
*
A venture studio helps create and grow businesses. Depending on its model it may help find opportunities check ideas, build products put together teams and set up the foundations of a company.

Some venture studios create business ideas. Then work with founders to build them. Others work with entrepreneurs who already have an idea and need help turning it into a business.

The defining feature of a venture studio is its involvement in building the company. Of just giving advice or running a program a venture studio may help build the venture directly.

However venture studios differ a lot. Their resources, duties, ownership deals and how much they get involved depend on the studio and its agreement with the founder.

A venture studio might be worth looking into if you have an idea need big help building a company or want to work closely with a partner who helps build the company.

The important question is what the venture studio will actually do, not what it says it does.
**

What Is a Startup Accelerator?**

A startup accelerator usually supports existing startups through a program that is designed to help them grow during a set time period.

Programs usually include mentorship, workshops, business strategy help, networking and chances to meet investors or partners. Some give money while others focus mainly on advice and networking.

Accelerators usually expect participants to come with a business idea or startup that has reached a stage of development. The program then helps founders sharpen their plan solve problems and get ready for growth.

A startup accelerator might be worth exploring if you already have a startup can run its work and would benefit from clear feedback, mentorship or useful connections.

As with venture studios, the quality and amount of help vary. Program length, who can join, money, mentor help and contract terms should all be checked one by one.

Five Important Differences

1. Where the journey begins

A venture studio may help build a business from an idea. A startup accelerator usually works with startups that already have an idea and are ready for a program.

This difference can help you narrow your choices even though some organizations work at stages.

2. How the organization gets involved

A venture studio may help build the company depending on the deal.

A startup accelerator usually gives advice through scheduled sessions, mentorship, workshops and networking while the founding team still runs the business.

Neither approach automatically gives value. The question is how much help your startup needs and what you want to keep in charge of.

3. What support is available

Venture studios may give skill, product building help team‑building tools or help with other parts of starting a company.

Startup accelerators often focus on mentorship, learning, peer support, professional networks and investor introductions.

These are patterns, not rules for every case. Look at the resources each offers instead of assuming all give the same package.

4. How the relationship is structured

Startup accelerators usually run in cohorts with set schedules and end dates.

Venture studios may work with companies, over a timeline depending on the business and the studios way.

Ask how long the partnership will last what milestones will show progress and what help stays after the phase.

5. Ownership and financial terms

Neither model has one equity or money arrangement.

A venture studio may take an ownership share in a company it helps build. A startup accelerator may also take equity give money, charge fees or run without taking ownership.

Before you agree to either arrangement you must understand the implications for equity, intellectual property, decision making, fees, funding and future fundraising.

A programs headline benefits are part of the picture. The complete agreement matters a lot.

Which Model Fits Your Startup?

Than choosing based on reputation or terminology you should start by identifying the biggest challenge.

If you are still developing an idea: Consider whether you need help validating the opportunity building a product assembling a team or turning the concept into a company. A venture studio may offer an approach.

If you already have an operating startup: Consider whether structured mentorship, business feedback, networking or investor access would help you address the priorities. An accelerator may be worth investigating.

If you are in between: Compare both models. Some venture studios work with existing businesses and some accelerators support very early‑stage founders. The specific offering matters more than the category.

Use these questions to assess your options:

What is the biggest obstacle preventing the startup from progressing?
Which resources or expertise are missing for the startup?
How much hands‑on involvement would be useful for the startup?
What responsibilities and decisions would remain the startups?
What equity, fees, funding or other commitments would be required for the startup?
What progress should the startup expect from the relationship?

Your answers will help you assess whether the organization addresses a need or simply offers benefits that sound attractive.

Questions to Ask Before Committing

Once you identify a venture studio or accelerator look beyond its public description.

Ask who will work with the startup what resources will actually be available. How frequently the startup will receive support. Clarify who makes business decisions how progress will be evaluated and what happens if the strategy changes.

Review the legal terms carefully especially those involving equity, intellectual property, fees, funding and the ability to leave the arrangement.

Where possible speak with founders who have previously worked with the organization. Ask what support they received whether expectations were clear and what they would want to know before joining.

These conversations can help you understand how the relationship works in practice than relying entirely on the organizations own description.

Choose the Relationship, Not the Label

Venture studios and startup accelerators offer different approaches, to supporting entrepreneurs. A venture studio may suit founders seeking a hands‑on company‑building partner while an accelerator may suit existing startups looking for guidance and connections.

The distinction is not absolute and neither venture studio nor accelerator is automatically better.

The right choice depends on the startups stage the pressing needs the support an organization can genuinely provide and the terms of the relationship.

If you are researching venture creation Aperture Venture Studio is one resource to explore when learning more about the venture studio approach.

Ultimately focus on what the organization will contribute what it expects in return and whether that arrangement will help the startup move toward its meaningful milestone.

What is the hardest part of turning industrial sensor data into decisions people can trust?

Juliet Ogochukwu — Tue, 13 Oct 2026 07:09:10 +0000

Collecting sensor data is one thing. Knowing when that data is reliable enough to act on is another.

Imagine a machines vibration sensor reports a reading. Is the equipment developing a fault operating under a load or sending bad data?

Even if an AI model detects the anomaly correctly there's still a decision to make: trigger an alert collect information schedule an inspection or take automated action?

That gap between detecting a pattern and making an operational decision seems like one of the most interesting challenges in industrial AI.

I'm curious which part tends to cause the trouble in real-world systems:

Data quality: Unreliable sensors, missing readings or delayed data.
Integration: Combining information from machines and disconnected systems.
Generalization: Getting models to work outside controlled test environments.
Workflows: Turning predictions into actions people can actually take.
Trust and ROI: Demonstrating that the system is reliable and improves outcomes.

I'd be especially interested, in lessons: What turned out to be harder than expected and what would you approach differently next time?

Is the bottleneck usually the data, the model or everything that has to happen after the model produces an output?

What is the hardest part of turning industrial sensor data into decisions people can trust? Collecting sensor data is one thing. Knowing when that data is reliable enough to act on is another. Imagine a machines vibration sensor reports a reading. Is the

Juliet Ogochukwu — Tue, 13 Oct 2026 07:01:24 +0000