Larger customers weren't buying better translation. They were trying to operate globally.
How a localisation platform stopped being a tool for one team, became the platform for every team's language needs, named AI as its real competitor, and more than tripled its average contract value.
Problem, solution, result in thirty seconds.
A developer tool with enterprise customers.
The company builds a platform that helps software businesses translate their product, website and content into many languages, and keep those translations up to date every time something changes.
It grew up with developers. Engineers adopted it to manage translation files inside their build process, and that is how most of the market still saw it: a useful tool for the engineering team.
That origin shaped everything commercial. Most deals were sold to one team, for one use case, and priced like a developer tool. Average contract value sat under $5K a year.
The brief was bigger customers. The barrier was being a tool for one team.
The ask was clear: win bigger customers, so deal sizes would grow.
When I looked at how the product was actually used, the same pattern showed up everywhere. The company had some enterprise customers, but not enterprise-wide adoption. In enterprise, mid-sized and small accounts alike, one team, usually engineering, used and bought it. That was the real barrier to winning big companies. A large company has language needs across many teams: engineering, product, marketing and content. As a tool for one of them, you are a small line item. As the platform for all of them, you are a strategic purchase. The platform wins.
There was a second problem the company hadn't recognised yet. AI translation was improving fast, and buyers were starting to ask why they needed the platform at all if AI could translate, or whether they could simply build it themselves. The competitor wasn't another localisation vendor any more. It was AI. I identified that shift and made it part of the strategy.
Every commercial problem sits somewhere on this chain, from choosing the market on the left to what Sales says on the right. Decisions on the left shape everything to their right.
The company thought the fix was finding bigger customers. But big companies buy platforms, not single-team tools. Until the product served every team's language needs, and had an answer to “why not just use AI?”, bigger customers wouldn't pay bigger prices.
Four decisions, in order.
I didn't start with the messaging. I started with how the product was really being used, and by whom.
Show why one team isn't enough.
I mapped how customers of every size used the product. The pattern was consistent: some enterprise customers, but no enterprise-wide adoption. Almost always, one team, usually engineering, used and bought it. That gave me the case to take to leadership. If we wanted big companies, being a tool for one team would never be enough. We had to be the platform for every team with language needs. I then prioritised the enterprise segment by buyer maturity.
Become the platform for every team.
I repositioned the product from a developer tool to the platform for all of an organisation's language needs, where engineering, product, marketing and content teams work from one place. A tool for one team is a small line item. A platform for every team is a strategic purchase, and it is priced like one.
Name the real competitor: AI.
I showed leadership that the threat was AI tools and in-house builds, not other localisation vendors. I positioned the platform AI-first, as the workflow, data, integrations and governance that make AI usable at enterprise scale, and built a build-versus-buy narrative Sales could use whenever a buyer asked “why not just use AI?”
Launch it, and make it stick.
The new positioning went to market with the launch of a new enterprise product. To keep it consistent after launch, I built two tools: an AI agent that checks new content against the approved positioning before it ships, and a Language Market Fit Score, a diagnostic that shows how well a company's languages match the markets it wants to grow in.
The buyer changed. So did the contract value.
Three measures, before and after the repositioning.
More than tripled, because the product was now bought as a platform for many teams, not a tool for one.
The repositioning contributed to 29% year-on-year ARR growth.
One in five opportunities was touched by the AI-first story.
Big companies buy platforms, not tools.
The company asked for bigger customers. But in accounts of every size, the product was used by one team, and big companies don't make big purchases for one team's tool. Once the product was positioned as the platform for every team's language needs, with a clear answer to “why not just use AI?”, the contract value followed.