Discovery Phase – Laying the Foundation for Successful Product Development

Srihari Maddula
Mar 18
10 min read

By Srihari M, Director Product Development at EurthTech

Published on March 18, 2025

The Discovery phase is the most critical step in hardware product development. It serves as the foundation upon which all future decisions are built. Without a deep understanding of the problem, market landscape, and user needs, even the most technically sound products can fail to find traction. This phase goes beyond just generating ideas; it focuses on structured problem validation, market research, and feasibility analysis to ensure that we are solving the right problem for the right users.

This article is the first in a multi-part series where we delve into the Discovery Phase in depth, covering:

Understanding the Problem Space
User and Market Research
Defining the Product Vision and Strategy
Proof of Concept (PoC) Development

Discovery Phase - Understanding the Problem Space

Why Understanding the Problem First is Critical

Many companies jump straight into designing and prototyping without fully validating the problem they are trying to solve. This often leads to wasted resources, delayed timelines, and, ultimately, products that fail in the market. At EurthTech, we take a problem-first approach to ensure that the solutions we build are genuinely valuable.

1. Problem Identification

The first step is to clearly define the problem we want to solve. This involves answering key questions:

What is the problem? (Be as specific as possible)
Who is affected by this problem? (Identify different user personas)
Why does this problem exist today? (Understanding root causes)
How are people currently solving this problem? (Existing alternatives and workarounds)
What are the major pain points of current solutions?

A well-defined problem statement is not just a broad challenge but a clearly articulated issue with measurable factors.

Example: Smart Industrial Sensor for Predictive Maintenance

Instead of saying: “Factories need better maintenance solutions.” A refined problem statement would be: “Factory managers struggle to predict machine failures due to a lack of real-time data, leading to unplanned downtimes, higher maintenance costs, and production losses.”

2. Identifying and Segmenting the Target Audience

Not every problem affects all users the same way. A deep understanding of the target audience is crucial to designing an effective product.

Who will be the primary users?
What industries, roles, or demographics do they belong to?
What are their biggest pain points?
What solutions do they currently use?
What factors influence their buying decisions?

Creating user personas helps in tailoring the solution for different types of customers.

Example Persona:

Name: Raj, Factory Operations ManagerIndustry: Automotive ManufacturingPain Points:

Frequent machine breakdowns causing 10% production downtime per month
High maintenance costs due to lack of predictive insights
Wants a simple plug-and-play solution without IT complexity

By developing multiple such personas, we ensure that our product addresses the right problems for different user types.

3. Competitive and Market Landscape Analysis

Understanding the market and competitive landscape is crucial for identifying differentiation opportunities. Key areas to analyze:

Who are the current players in this space? (Direct and indirect competitors)
What technologies are they using?
What are their strengths and weaknesses?
How does our product compare?
Are there any regulatory or industry standards we need to consider?
What trends are shaping the industry?

Example: Competitor Analysis for IoT-based Smart Sensors

Competitor	Key Features	Weaknesses
Company A	Wireless, AI-powered predictive analytics	Expensive, complex integration
Company B	Low-cost sensors, easy deployment	Limited analytics capability
Company C	Strong industrial partnerships, robust hardware	High maintenance costs

This analysis helps position our product uniquely, ensuring we offer superior value to customers.

Discovery Phase – User and Market Research

In the previous section, we explored the importance of defining the problem space, identifying user needs, and conducting competitive analysis. Now, we move to the next crucial step: User and Market Research. This phase validates our assumptions through direct engagement with potential users, data-driven insights, and early prototype testing.

A well-executed research phase helps eliminate guesswork and provides clarity on how our product should evolve before investing heavily in development.

This section covers:

Conducting Effective User Interviews
Surveys and Data Collection
Early Prototype Testing
Industry and Market Trends Analysis

1. Conducting Effective User Interviews

User interviews provide qualitative insights into pain points, behaviors, and expectations. Unlike generic surveys, interviews allow for deeper exploration of real-world challenges and potential solutions.

Best Practices for User Interviews

Avoid Leading Questions: Instead of asking "Would you use a smart sensor for predictive maintenance?", ask "How do you currently predict and prevent machine failures?"
Encourage Storytelling: Get users to describe past experiences, such as incidents where a lack of predictive insights led to operational issues.
Identify Patterns: Conduct multiple interviews and look for recurring pain points.
Separate Users by Segments: Interview different roles (e.g., factory managers, maintenance engineers, procurement teams) to get a 360-degree view.

Example Interview Questions

Can you describe a time when you faced [specific problem related to our product]?
What tools or solutions are you currently using?
What are the biggest challenges you face in your workflow?
If you could improve one thing about your current solution, what would it be?
What factors influence your purchase decision for new technologies?

Interview Insights Example

After interviewing 10 factory managers, we might discover that 7 out of 10 struggle with unplanned machine downtimes due to a lack of real-time monitoring. Such insights shape our product direction and messaging.

2. Surveys and Data Collection

While interviews provide qualitative data, surveys offer quantitative validation by collecting responses at scale.

How to Design an Effective Survey

Keep it Short: 5–10 questions maximum to maintain engagement.
Use a Mix of Question Types: Include multiple-choice, Likert scale (1-5), and open-ended questions.
Target the Right Audience: Use platforms like LinkedIn, industry forums, and email campaigns.
Analyze Data Trends: Identify key takeaways from response patterns.

Example Survey Questions

On a scale of 1-5, how often does machine failure affect your operations?
How much do you spend annually on machine maintenance?
What features would you prioritize in a smart sensor solution?
What is your company’s biggest concern regarding adopting new industrial IoT technologies?

Interpreting Survey Data

If 80% of respondents indicate that cost is their biggest concern, we might adjust our strategy to focus on affordability rather than premium features.

3. Early Prototype Testing

Once we gather user insights, we create low-fidelity prototypes to test initial concepts.

Types of Prototypes

Paper Prototypes: Simple sketches for UI/UX feedback.
3D-Printed Models: Useful for testing physical form factors.
Interactive Wireframes: Clickable digital prototypes.
Minimum Viable Products (MVPs): Functional but limited-feature prototypes.

Testing Approach

Usability Testing: Observe how users interact with the prototype.
A/B Testing: Compare different versions to see which performs better.
Pilot Deployments: Small-scale real-world usage before mass production.

Example of Early Testing Insights

A startup testing a smart IoT lock found that users struggled with connectivity issues in areas with poor network reception. This feedback led to adding an offline unlock feature before full-scale development.

4. Industry and Market Trends Analysis

Understanding broader market dynamics ensures that our product is aligned with industry needs and remains relevant in the future.

Key Market Research Questions

Is there growing demand for this technology?
What regulations or certifications must we consider?
Who are the emerging competitors?
What is the projected market size and growth rate?

Sources for Market Research

Industry Reports (Gartner, McKinsey, etc.)
Conferences and Trade Shows
Competitor Analysis (Reviewing patents, product roadmaps, and case studies)
Customer Feedback Loops (Monitoring forums, reviews, and support tickets)

Example Industry Trend Insight

In 2024, AI-powered predictive maintenance became a top investment priority for industrial firms. This indicates that adding machine learning capabilities could increase product adoption.

User and market research are the backbone of the Discovery Phase. By conducting structured interviews, surveys, prototype testing, and industry research, we ensure that our product is grounded in real-world needs and aligned with market trends.

Discovery Phase – Defining the Product Vision and Strategy

With a clear understanding of the problem space and validation from user and market research, the next step in the Discovery Phase is to define the product vision and strategy. This phase bridges research insights with actionable steps to develop a focused, well-structured product roadmap.

The goal of this phase is to create a compelling vision for the product and outline a strategic development plan that aligns with business objectives, technical feasibility, and customer expectations.

In this section, we will cover:

Crafting a Clear Product Vision
Defining Core Value Propositions
Setting Measurable Success Criteria
Creating a Product Development Roadmap
Stakeholder Alignment and Go-To-Market Considerations

1. Crafting a Clear Product Vision

A product vision serves as the North Star guiding the entire development process. It provides a high-level statement of what the product aims to achieve and why it exists.

Elements of a Strong Product Vision:

Inspiration: Why does this product matter?
Target Audience: Who will benefit from it?
Value Proposition: What unique advantage does it provide?
Long-Term Impact: How will it evolve over time?

Example of a Well-Defined Product Vision:

“To empower factory operators with real-time predictive maintenance insights through an affordable, easy-to-deploy IoT sensor network—reducing unplanned downtimes and optimizing production efficiency.”

This vision statement is clear, specific, and problem-driven, ensuring that the entire team stays aligned on the product’s purpose.

2. Defining Core Value Propositions

The value proposition defines why customers should choose our product over alternatives. It directly ties into the problem statement and ensures that the product delivers meaningful benefits.

Key Questions to Define Value Propositions:

What is the primary pain point our product solves?
How does our solution improve the user’s workflow?
What unique feature sets differentiate us?
How do we justify the cost vs. benefits?

Value Proposition Framework:

Feature	Benefit to User	Competitive Advantage
Plug-and-play IoT sensor	Easy deployment without IT complexity	Lower setup costs than traditional solutions
AI-based predictive analytics	Reduces machine downtime by 30%	Outperforms manual monitoring methods
Wireless and battery-operated	Works without infrastructure changes	Eliminates wiring costs and long installation times

3. Setting Measurable Success Criteria

Defining clear metrics ensures that product development aligns with business and user needs. These success criteria guide decision-making, prioritize features, and establish a benchmark for evaluating progress.

Key Performance Indicators (KPIs):

Market Fit KPIs:
- % of target users adopting the product within the first 6 months
- Net Promoter Score (NPS) from pilot customers
Technical Performance KPIs:
- Device uptime percentage over 6 months
- Accuracy rate of predictive analytics models
Business Impact KPIs:
- Cost savings realized by customers using the product
- Reduction in unplanned downtime across test facilities

Setting SMART (Specific, Measurable, Achievable, Relevant, Time-Bound) goals ensures we track progress effectively.

4. Creating a Product Development Roadmap

A roadmap provides a timeline-based strategy for execution, ensuring structured product development.

Key Stages of the Roadmap:

Phase	Milestone	Timeline
Discovery	Finalize problem validation & research insights	Month 1
Prototype Development	Build functional MVP for pilot testing	Month 2-3
Engineering Validation	Test and refine product performance	Month 4-5
Pilot Launch	Deploy test units for early adopters	Month 6
Mass Production	Final version ready for scale-up	Month 8-12

A roadmap ensures the team remains aligned, resources are allocated efficiently, and realistic expectations are set for stakeholders.

5. Stakeholder Alignment and Go-To-Market Considerations

A well-defined vision and roadmap need buy-in from key stakeholders, including internal teams, investors, and potential customers.

Steps to Ensure Stakeholder Alignment:

Conduct strategy meetings with engineering, marketing, and sales teams
Gather early feedback from industry partners and potential customers
Secure budget approval and investment for the next development phase
Align the vision with overall business goals

Preliminary Go-To-Market Considerations:

Pricing strategy based on cost structure and competitor benchmarks
Distribution channels (Direct sales, B2B partnerships, online platforms)
Marketing strategies (Early adopters, pilot programs, press releases, industry events)

Defining the Product Vision and Strategy is essential for ensuring clarity, focus, and alignment before moving into full-scale engineering and development. With a well-structured vision, clear value propositions, measurable KPIs, and a roadmap in place, the team is equipped to move forward with confidence.

Discovery Phase – Proof of Concept (PoC) Development

With a clearly defined product vision and strategy, the next step in the Discovery Phase is to develop a Proof of Concept (PoC). The PoC serves as an early functional model of the product to validate feasibility, user interaction, and core technical assumptions before investing heavily in full-scale development.

A well-executed PoC helps answer critical questions such as:

Is the core technology feasible?
Can the product effectively solve the problem?
What are the key technical and design challenges?
Will users find it valuable and usable?

In this section, we will explore:

Objectives of a Proof of Concept (PoC)
Types of PoC and Choosing the Right Approach
Developing and Testing a PoC
Evaluating PoC Success and Next Steps

1. Objectives of a Proof of Concept (PoC)

A PoC is a small-scale, early-stage version of the product designed to test fundamental concepts and identify challenges before scaling up.

Key Objectives of a PoC:

Technical Feasibility: Can the hardware, firmware, and software work together as intended?
User Feedback: How do potential users interact with the prototype?
Risk Mitigation: What potential roadblocks could arise in full-scale development?
Cost Estimation: What are the material and development costs?
Timeframe Validation: How long will development and testing take?

A successful PoC does not need to be feature-complete but should demonstrate the core functionality of the intended product.

2. Types of PoC and Choosing the Right Approach

Different types of PoC models are used depending on the complexity and requirements of the product. Selecting the right approach ensures efficiency and cost-effectiveness.

Common PoC Types:

Type	Description	Best For
Hardware Prototype	A basic, working version of the physical product	IoT devices, wearables, embedded systems
Software Simulation	A digital model of the system’s functionality	AI-driven solutions, industrial automation, predictive analytics
Mechanical Prototype	Focuses on form factor and material selection	Robotics, medical devices, consumer electronics
Conceptual Prototype	A non-functional design to evaluate usability	UI/UX design, interactive hardware

Example: If we are developing an IoT-based smart sensor, a hardware prototype with basic firmware is essential for initial testing, while a software simulation can be used to test cloud integrations.

3. Developing and Testing a PoC

Once the PoC type is determined, the next step is to build, iterate, and refine the prototype.

Steps to Build a PoC:

Define Core Functional Requirements
- Identify the minimal set of features necessary to test feasibility.
- Example: A smart industrial sensor PoC might include basic sensor data collection and wireless transmission but exclude advanced analytics.
Select Components & Technologies
- Choose hardware components, development boards, and software tools that enable quick prototyping.
- Example: Using an ESP32 or LoRa E5 module instead of designing a custom PCB in the initial stage.
Rapid Prototyping & Assembly
- Develop the first iteration using 3D printing, breadboards, or off-the-shelf modules.
- Software PoC can be built using low-code/no-code tools to accelerate validation.
Conduct Initial Testing
- Test against predefined benchmarks to evaluate performance, power consumption, and response times.
- Identify early technical challenges and solutions.

4. Evaluating PoC Success and Next Steps

Once the PoC is built and tested, evaluation is necessary to determine if the project is ready to move into full-scale engineering.

Evaluation Metrics for PoC Success:

Metric	Evaluation Criteria
Technical Feasibility	Can the core technology work in real-world conditions?
User Validation	Do test users find the product useful?
Cost Estimates	Are projected costs within reasonable limits?
Scalability	Can the prototype be expanded into a full-fledged product?

Decisions After PoC Evaluation:

Proceed to Engineering: If the PoC meets all technical and user requirements.
Refine and Retest: If critical issues are identified, refine the PoC before scaling.
Pivot or Abandon: If the concept proves unfeasible, reconsider or pivot the approach.

The Proof of Concept (PoC) Development phase is a critical milestone in hardware product design. It ensures that the product is technically viable, cost-effective, and aligned with user needs before investing in mass development.