Artifact §05 · Research synthesis
Seventy six conversations, one page
Before SKADI designed anything, we talked to 76 people: freestyle skiers and snowboarders, from people learning their first 360 to a nationally ranked athlete, plus the coaches and judges who watch them. This is what that looked like from the inside, what it changed, and the two assumptions it killed.
This one is real. The numbers are from the work, the quotes are paraphrased, and nobody is named, because they gave a stranger twenty minutes and did not sign up to be on a website.
How it was run
- Who
- Competitive and recreational freestyle skiers and snowboarders. Coaches and judges, who see more tricks in a weekend than a rider lands in a season.
- Format
- Conversations, not surveys. About twenty minutes each, on the hill, in clubs, and over calls. Notes written up the same day.
- Cadence
- Spread across the season, with one sprint of 24 in four weeks when we needed to settle the product direction before the engineering team committed to a design.
- The check
- A live Meta ads campaign in March 2026, to test whether cold strangers behaved the way interviewees said they would.
- My part
- Ran the interviews with the team, kept the record, turned what we heard into the product requirements, the positioning, and the financial model.
The questions that did the work
Every interview wandered, which is the point of an interview. These four came up every time, in roughly this order, and they did most of the work.
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Tell me about the last trick you tried to learn. How did you know whether it went well?
Why It starts with a story, not a product. The answer to the second half is the whole problem: they did not know. They guessed, or they asked a friend with a phone.
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What do you use today, and what does it not tell you?
Why Shaky GoPro footage filmed by someone else. Apps that record speed and vertical and nothing about the trick. The gap between what they used and what they wanted was the requirement.
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Who else sees your numbers, and why does that matter to you?
Why This is the one that overturned an assumption. We expected the answer to be "my coach". For a lot of riders it was "everyone", and they wanted the numbers to be impossible to fake.
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If this existed, what would you pay, and what would make you not buy it?
Why The first half gave us a price. The second half gave us a list of things not to build, which is the cheaper list.
What we expected, and what was true
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We assumed
Riders want their data as stats, and more detail is always better.
What was trueYounger riders wanted stats they could not edit. The point was not analysis, it was proof: to friends, to sponsors, to the group chat. A number you can change is worth nothing to them.
What changedSession recaps built to be shared, and built so they cannot be altered. Social proof became a product requirement rather than a marketing idea.
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We assumed
"Find my ski" would be the feature every backcountry rider paid for.
What was trueThe people at the most risk trusted a five dollar physical leash over a two hundred dollar tracker, and said so without hesitation.
What changedThe feature was cut before anyone designed it. That is the cheapest a feature ever gets killed, and it is the reason you interview before you draw.
And two things we did not expect at all.
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The market already understood ski trackers, and was frustrated with them.
People knew what Carv and the GPS apps did. Their complaint was specific: everything on the market measured turns and carving. Nothing measured a trick. The gap was not awareness, it was the thing being measured, which is a much better gap to find.
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Even sponsored athletes were guessing.
Coaches and judges said the same thing from the other side: there was no objective record of what happened in the air, so feedback was opinion, delivered from the bottom of the landing. A coach described the concept as a coaching tool before we had used the word. That sentence set the positioning.
What it changed in the product
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Automatic trick detection became the core, not a feature.
Airtime, rotation, height, a landing stability score, and pop timing, detected without anyone filming anything. Every competitor tracked the run. We tracked the jump.
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Boot mounted, not insole.
Two pods, strapped to the outside of any ski or snowboard boot in under thirty seconds. Better for capturing the body's motion through a trick, and it keeps clear of the insole patents the incumbents sit on.
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The price came from the room, then got tested against strangers.
Two hundred and fifty dollars was the average of what interviewees said they would pay, and it sat next to Carv at 249. The model priced the kit at 249.99, and the ad campaign checked whether people who had never heard of us would raise a hand at that number.
What people said, against what they did
Interviews tell you what people say. A paid funnel tells you what they do. In March 2026 we ran a small Meta campaign to a cold audience, with the price in the creative, and counted.
| Measure | Result | For context |
|---|---|---|
| Impressions | 10,917 | Cold audience, no prior awareness |
| Click through rate | 7.44 percent | Against a 1 to 2 percent norm for the format |
| Waitlist signups | 13 | Email addresses, with the price shown first |
| Cost per signup | $5.77 | The number every unit in the model traces back to |
Thirteen is a small number and I would not build a forecast on it alone. What it bought was a real cost per signup instead of an assumed one, and the financial model was built so that every unit in it traces back to that figure: ad spend, divided by cost per signup, times a signup to purchase rate of 7 to 14 percent depending on the year. The sensitivity tables were built around the two inputs we controlled least, the cost per signup and that conversion rate, because those are the two numbers an investor should push on, and I wanted the answer ready.
The model also said plainly where it fell short. At the year three run rate the venture was still about 750 units a year below break even. I would rather show an investor that line than have them find it, and the same instinct is why the open items sit at the front of every requirement I write.
What this has to do with business analysis
Everything. The skiers did not speak in specifications any more than the warehouse supervisors did. They described a bad day, a workaround, and a thing they wished existed, and the job was the same: listen for the requirement underneath the request, write it down precisely enough to build against, and check it against behaviour before anyone spends money on it.
The difference was the cost of being wrong. On the ERP, a missed requirement was a change request. At SKADI, a missed requirement was a hardware revision. It made me more careful about the gap between what people say they want and what they will actually do, and that carefulness comes with me.
Written by Nasmaan Ibrahim for nasmaan.com. Figures from SKADI's own research and the March 2026 campaign. In August 2026 I handed SKADI's day to day to my co-founder and stepped back to an advisory seat.
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