Treasure AI has taken aim at two recurring martech headaches at once: marketers’ dependence on technical teams for personalization, and email contracts that reward message volume over customer response.
At its Agentic World 2026 conference, the company introduced Personalization Studio, a marketer-facing interface for building, launching and managing real-time website personalization. The studio is part of the Personalization AI Suite inside Treasure AI’s Agentic Experience Platform, and it is built on the company’s unified customer data and real-time decisioning.
What the studio does
Personalization Studio combines batch profile data — audiences, loyalty status, purchase history — with real-time behavioral signals. That combination enables sub-second personalized experiences, Treasure AI says. More importantly for marketing teams, the interface is meant to be used directly by marketers, reducing the back-and-forth with developers or data engineers.
Rafa Flores, chief product officer at Treasure AI, explained the gap in traditional setups: standalone personalization tools require separate integrations, data pipelines and profile stores. As a result, everyday campaign changes get stuck in technical queues.
“You can’t really sit in the data warehouse and do personalization in real time because the data warehouse can’t do real time,” Flores said. “Personalization Studio allows marketers to come in and build a campaign with personalization in 10 minutes.”
The ten-minute claim is the product’s central pitch. It reframes the buying conversation from total cost of ownership to time-to-launch, a metric marketing leaders can observe directly.
Email pricing tied to engagement
Alongside the studio, Treasure AI announced a new engagement-based pricing model for email. Instead of charging for the number of messages sent, the model ties costs to customer actions such as clicks.
“We’re betting on the click-through rate,” Flores said.
This is a real shift in incentive design. Under a per-send model, a vendor benefits from volume. Under engagement pricing, vendor revenue grows only when audiences act, which brings the supplier’s commercial interest closer to the marketer’s performance goal.
Treasure AI is not alone in rethinking pricing. HubSpot recently introduced a new pricing model for new customers in the EMEA region, and Salesforce’s Agentforce pricing has evolved since its debut. As AI works its way into martech platforms, vendors are reevaluating how they charge for usage versus outcomes.
Why this matters for marketing teams
The announcement reflects a broader shift in martech architecture. As AI-powered applications and data warehouses become the center of gravity in the stack, operational questions become strategic: who can launch a campaign, how quickly, and what does the contract reward?
Marketing leaders can use the news as a checklist for their own stack:
- Time to campaign: How long does a personalization change take from idea to live?
- Dependencies: Are routine iterations blocked by data, web or engineering teams?
- Pricing alignment: Does the contract reward sends, impressions or actual customer response?
Where those answers are slow or misaligned, the category is beginning to offer clearer alternatives. Treasure AI’s argument is that a unified customer-data foundation and a marketer-friendly interface can remove both the delay and the volume incentive in one move.
What to watch next
Personalization Studio’s impact will depend on how easily teams can move existing audience data into the platform and how well the real-time decisioning performs at scale. The engagement pricing model will test whether a vendor is confident enough in its intelligence to accept payment on clicks rather than sends.
For now, the launch gives marketers a concrete signal of the next front in martech: less technical dependency, faster campaign control and pricing that follows customer response.
Source: MarTech
