How to use product discovery tools?

Designing a software product is not something to be taken lightly; an appropriate number of steps must be followed to ensure that the needs of the company and the specific project are met. To make this happen, the ideal approach is to implement product discovery tools.

Why use product discovery tools?

Before looking at “what to use,” it is worth understanding the why: what do we gain when we apply this approach correctly?

• Reduce the risk of investing in the wrong thing: by validating ideas with data and early feedback, you avoid developing features that users do not need. Several authors agree that one of the main reasons for failure in new products is launching something without clear demand.
• Save time and resources: by focusing on the features with the greatest impact, the development budget is optimized.
• Better internal alignment: when decisions are supported by data and clear processes, different stakeholders (business, design, technology) speak the same language.
• Embrace iteration and continuous learning: discovery is not a one-time phase but must be integrated throughout the entire product lifecycle (“continuous discovery”).
• Higher probability of commercial success: by better understanding the user, the market, and the technical possibilities, the product has a higher chance of resonance.

A recent study by Canhoto et al. (2025) reviewed industrial (grey) literature to synthesize phases, metrics, and techniques of product discovery, and recommended approaching the process with adaptability, not as a rigid straight line.

The phases of product discovery — and which tools to apply

For tools to be useful, they must be embedded in a well-defined process. According to recent literature, two phase frameworks dominate the discussion: the adaptive model (stages: alignment, research, ideation, prototyping/creation, validation, refinement) and the “double diamond” model. Here I propose a practical adapted version:

Alignment

Before choosing software, you first define objectives, basic hypotheses, key metrics (such as “adoption rate,” “engagement,” “retention”), and who the stakeholders are.

Tools: virtual alignment workshops (Mural, Miro), templates such as Lean Canvas / Value Proposition Canvas.

Research (quantitative and qualitative)

• Qualitative: interviews with potential users, focus groups, initial usability tests.
• Quantitative: surveys, user behavior analysis (clicks, scrolls), A/B experiments. This is where real “customer data analysis tools” and “product feedback tools” come in. For example, Hotjar offers heatmaps and session recording analysis. You can also use “quantitative product discovery” to guide decisions with robust metrics and statistical analysis.

Ideation and prioritization

With the insights gathered, you generate possible solutions. Here it helps to use frameworks such as the impact/effort matrix, RICE prioritization, or “Productboard” to manage ideas. At this stage, you already begin to filter using clear criteria (user value, technical feasibility, strategic fit).

Prototyping / test creation

Create visual prototypes (low or high fidelity) that allow you to put something tangible in front of the user. Tools like Figma (or interactive prototypes) work well. Some platforms, like Maze, allow usability testing of prototypes without coding.

Validation and refinement

Measure how users interact with the prototype, collect feedback, and adjust hypotheses. Use analytics tools, survey platforms (Typeform or similar), behavior studies, A/B tests (Google Optimize, for example).

Transition to development with well-informed decisions

With validated prototypes and reasonable metrics, you decide what to build, with what priority, and how to measure success post-launch. Here, backlog/product management tools (such as Jira, Productboard) integrate discovery decisions with execution.

These phases are not rigid: you can iterate, go back, refine hypotheses. That adaptability is part of the value of the modern approach.

Key tools and recommendations (with examples)

Below are some of the best analytics platforms and useful tools for product discovery — not as a generic list, but embedded within the workflow we reviewed:

• Maze: ideal for usability testing, prototype validation, and continuous research. Its no-code interface allows the product team to build tests and run quick analyses.
• Hotjar: for heatmaps, session recordings, integrated surveys; it helps you see how users interact with the actual product or prototypes.
• Productboard: a tool for gathering feedback, prioritizing features, and maintaining visibility of all product ideas (from discovery to roadmap).
• Google Optimize / A/B testing tools: to test different versions of an interface or feature and measure which variant generates better results.
• Figma (with feedback or testing plugins): to create interactive prototypes that you can then share with users for validation.
• Surveys and forms (Typeform, SurveyMonkey, etc.): useful to validate hypotheses with a broad audience.
• Advanced behavior analytics tools (Contentsquare, Mixpanel, Amplitude): to analyze product usage trends with real data.
• Mural / Miro: for collaborative alignment workshops, user journeys, empathy maps, etc.

Product discovery tools, when used within a conscious and structured process, are powerful allies to reduce risks, save costs, and build products with a higher probability of success. For a technology manager or executive decision-maker, adopting these tools is not an extra: it is an investment in ensuring that the product path is the right one.

At Rootstack, we can accompany you on this journey: designing the process, selecting the best tools for your context, and supporting execution. 

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