The Challenge
Dexxter had aggressive plans for paid media growth, aiming to double acquisition over the next year. But while ad spend was scaling fast, their website wasn’t evolving at the same pace.
Optimizing landing pages manually was slow and resource-intensive. For a company focused on automation and smart scaling, relying on ad-hoc A/B tests didn’t fit their growth ambitions.
They needed a way to make experimentation faster, easier, and scalable, without slowing down their teams.
The Solution
Dexxter integrated Dalton directly into their marketing workflow, with no additional developer work required.
Dalton's AI agent began suggesting and deploying experiments automatically across key pages: optimizing messaging aligned to paid campaign traffic, personalizing based on search keywords, and testing different landing page structures.
With Dexxter running multiple ad campaigns with different messaging angles, visitors were now automatically shown landing page versions that matched their specific ad - instead of everyone hitting the same generic site.
Winning versions scaled automatically to more traffic, underperformers were phased out, and fresh experiment ideas were surfaced weekly based on live visitor behavior, keeping the site continuously improving with minimal manual input.
The Impact
In just the first two weeks after deploying Dalton, Dexxter achieved a 40% increase in click-through rates to free trial on key paid landing pages, alongside a 30% reduction in bounce rates.
By engaging more visitors and keeping them in the funnel longer, Dalton drove immediate improvements in top-of-funnel performance, setting the foundation for better lead conversion and more efficient acquisition as Dexxter’s paid campaigns continued to scale.
Experimentation shifted from being a slow, manual task to an always-on optimization engine, continuously learning from real visitor behavior and campaign intent.
