Consumer demand validation and product configuration study for a new smart home device

Problem: an innovative hardware company needed to validate real consumer demand for a genuinely new category of smart home device — one with no established market or competitive pricing benchmark to anchor against.

Goal: quantify the demand, identify the most compelling use cases, determine the optimal product configuration, and produce OEM evidence of market readiness across the US and key Western European markets.

Geography: North America (US) and Western Europe (UK, Germany, France, Italy, Spain, Netherlands).

Stage 1: Use-case discovery, concept framing and competitor analysis

• Explored real-world applications of the technology to map where it could genuinely solve a problem for consumers.
• Worked directly with the client to generate a broad, unconstrained long-list of potential use cases, then narrowed it into a prioritized use-case architecture.
• Defined the attribute and performance framework (detection capability, form factor, power source, primary purpose) that would later anchor the quantitative trade-off analysis.
• Developed a neutral, concept-agnostic stimulus and ran cognitive pre-testing to confirm respondents understood the concept as intended before any large-scale fielding.
• Competitive landscape overview to identify white space for new device

Stage 2: Quantitative validation (fielding)

• Fielded a single-wave quantitative survey to 1,220+ validated respondents across 7 markets in the US and Western Europe, screened for smart home device ownership and recent category spend.
• Sourced respondents through a market research panel with multi-layered fraud prevention (device fingerprinting, ID verification, bot/speeder detection) and in-survey quality controls.
• Applied post-field quality checks, removing and replacing low-quality respondents to keep the final dataset clean and analysis-ready.

Stage 3: Qualitative validation interviews

• Conducted in-depth interviews with real users to confirm and enrich the quantitative findings with lived experience, not just stated preference.
• Explored emotional drivers and behavioral context behind the top-priority use cases identified in Stage 2.
• Surfaced trust barriers, ideal product requirements, and purchase-decision behavior that quantitative data alone could not capture.

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