Validating demand for a low-cost learning device in India
A fake brand and two weeks of live testing, where what people want and what they'll pay for aren't the same number
- Who
- Global Tech Enterprise (Anonymous)Apr 2024 – May 2024 (2mo)
- Scope
- Experiment designBuildAnalysis
- Team
- SEO specialistLead strategist Product Designer
- Tools
Miro
Figma
Webflow
GSAP
Context
Validate demand before committing to build
A global tech brand was preparing to invest in a low-cost learning device for the Indian market and wanted to validate demand before committing further.
The open questions weren't ones a roadmap could answer. Laptop or tablet? Windows, Chrome, or Android? At what price? User interviews would have surfaced preferences but not willingness to pay, and a landing page would have measured interest but not commitment. The questions needed behavioural evidence from people actually trying to buy, which meant building something that looked like real e-commerce and seeing what they did with it.
The fake-door test
Build a fake brand, drive real traffic
The setup ran on Webflow, with two experimentation sites under a fake brand. Each site looked and felt like real e-commerce, with product specs, hardware bundles, and a full checkout flow. Google Ads drove targeted Indian traffic, Google Analytics tracked conversions, Hotjar tracked behaviour, and a post-purchase survey caught qualitative signal. The hypotheses were aligned with the strategy lead upfront and translated into testable flows before any build.
One site held price constant and varied form factor (laptop vs tablet) so users were choosing on substance. The other held form factor constant and varied OS across three price points, so users were trading off preference against cost. Running them in parallel meant we could answer "what do they want?" and "what would they trade away to get the price down?" in the same two weeks, against the same traffic source. Two parallel tests, designed to answer different questions. One experiment would have answered half the brief.
Behavioural evidence is strong but blind to motive, you see what users did but not why they did it. The post-purchase survey caught the why (perceived value, decision factors, price expectations), which gave the quantitative signal the context it needed to be useful. A clear number is useless if the team doesn't know what it means.
What we learned
Desire and willingness to pay aren't the same number
Laptops took 92% of conversions, tablets 8%, with search volume 8x higher for laptops. Form factor was answered emphatically on day one.
OS was more interesting. When users were browsing, Windows took 48% of intent, Chrome 21%, and Android 31%. When the same users actually bought, the order flipped, with Windows dropping to 21%, Chrome rising to 32%, and Android (the cheapest) jumping to 38%. What people want is one signal, what they'll actually pay for is another.
Heatmaps confirmed substance mattered too, with battery life and specs drawing repeated hovers.
A few other findings worth noting: 58% added hardware packages at checkout (the Performance bundle won, suggesting real demand for upgrade paths), Tier 1 cities drove 74% of conversions, and 95% of traffic came through mobile.
Outcome
Validated demand, validated direction
The test answered the brief:
- Laptop over tablet
- Windows preferred at parity, Chrome or Android when cheaper
- Real demand for hardware bundles
We handed over a synthesised view of the findings and a clear recommendation: refine the value proposition and price, then retest with the intended audience.
For $5,000 in ad spend and two weeks of live data, the client got behavioural evidence to commit to (or step back from) a much bigger investment. The value of the engagement came from the timing of the answer. Validating demand on a $5k budget when the alternative was a hardware programme commitment is the kind of leverage research can offer, when it's reached for before the commitment is made.
