A Swiss ecommerce company needed a US strategy it could only afford to get right once

The situation

A Swiss ecommerce company had built a profitable business in Europe and then watched growth flatten. The next chapter was the United States, a market where the company had no meaningful presence, no local customer base, and a budget that would not survive a wrong turn.

That combination raises the cost of bad research. A larger business can afford to enter a market, learn, and correct. This one needed the strategy to be close to right on the first attempt.

What was in the way

Funding was limited and exposure to the US market was thin. The company had no internal view of how American buyers in its category talk about the problem, price it, or decide. Conventional help meant paying consultancy rates for research on a timeline the business could not wait out.

There had been one earlier attempt. An in-house search campaign ran for two months and spent around EUR 1,600 across more than forty countries at once, with no lead capture wired to the pages it sent traffic to. It produced click volume and no way to tell who any of it was.

The company also had something it was not using. Months of recorded meetings and sales conversations sat unexamined, because reviewing them properly was work nobody had time for.

What we did

We used AI to filter and analyze those recordings for the strategic content buried in them: the language customers used, the objections that repeated, the moments where price entered the conversation and how it was received.

That analysis became the evidence base. The go-to-market strategy, the messaging angles and the pricing were all built on what the company’s own buyers had already said, rather than on category assumptions imported from somewhere else.

Then we tested it rather than presenting it. A ten-week US pilot put EUR 7,050 behind four competing messaging angles, each with its own landing page, in one market with lead delivery tracked end to end. Partway through we found that the second round of pages was firing the conversion event without delivering the lead to the client, which is the kind of break that quietly makes a channel look worse than it is. We caught it and closed it.

Human judgment did the strategy work. AI did the reading, at a speed and completeness that made the reading worth doing at all.

What came out of it

• 23,330 impressions and 693 clicks, generating 32 delivered leads at roughly EUR 220 each, reconciled against the client’s own records
• 12% of round-two clicks ran the product simulation, confirming the demo itself lands
• One messaging angle clearly out-pulled the other three, which settled a question the company had been arguing internally

Ten weeks and EUR 7,050 bought a working funnel and a read on the market that no amount of desk research would have produced. The company carried that into the next phase of its US work and expanded the engagement rather than ending it.

What this means for a growing business

Most companies are sitting on a research asset they treat as exhaust. Call recordings, support threads, sales notes and meeting transcripts hold the answers that market research is usually commissioned to go find. The constraint has always been that reading it all is nobody’s job.

That constraint is the part AI removes. The strategy still has to be someone’s judgment, and the market still gets the final vote. Which is the second half of this: a strategy is a hypothesis until something live tests it. Run that test small, early, and with the tracking wired up first, so that what you learn is about the market rather than about your own instrumentation.