Earlier this year, we launched Adaptive Generation Strategies — an update to how Jacquard searches for high-performing language. It introduced AI-driven feedback loops that let our Optimisation campaigns learn from live performance, your human control, and your team’s approval decisions — and adapt generation in response.
Today, we’re extending that capability to Contextual Messaging (CM).
It’s a bigger step than it sounds. In Optimise, a campaign learns about one audience. In CM, a single campaign runs across many contextual categories at once — different customer segments, behaviours, or moments, each with its own mini-landscape of what works. Until now, each of those categories largely had to learn on its own. From today, they learn together.
A search space that multiplies with every category.
Contextual Messaging already does something distinctive. It uses a contextual multi-armed bandit (CMAB) to match the right variant to the right customer context — so the language someone sees is shaped by who they are and what they’re doing, not just what the campaign is about.
But this creates a harder version of the search problem we described in our last update. Every contextual category is its own terrain of peaks and valleys. Multiply that by the number of categories in a campaign, and the cost of learning from scratch in each one adds up fast — especially in “cold start” categories, where the CMAB hasn’t yet seen enough engagements to act confidently. With this update however, learnings from one category will transfer and accelerate all others.
Meta-analysis across categories.
The new capability runs a meta-analysis across all contextual categories in a campaign. It deconstructs and segments the linguistic and contextual cues shared by high-performing language — and, just as importantly, by low-performing language — right across the campaign.
Those patterns become seeds for new content generation. Our upgraded generation process uses them to produce a diverse set of variants with targeted coverage of the strategies we already know are working. We also draw on your human control to extend the information and linguistic coverage beyond what the brief alone provides.
The effect is that a winning pattern discovered in one category doesn’t stay there. It informs generation for the rest of the campaign — including categories still waiting on their first wave of engagement data. What one part of the campaign learns, the whole campaign benefits from.
Language intelligence, language performance, customer context.
Adaptive Generation Strategies in Optimise showed what was possible when language generation could respond to real-world performance. Extending it to CM brings together the three things that matter most for genuine 1:1 messaging at scale:
- Language intelligence — generation that understands brand voice, intent, and creative strategy.
- Language performance — feedback loops that learn from what actually works in-market.
- Customer context — the CMAB matching the right language to the right moment.
Individually, each is useful. Together, they compound. Every impression becomes a signal that improves generation. Every category strengthens every other. Every campaign you run makes the next one start from a better position.
This is the direction the platform is heading: less a tool you use, more a system that learns — across language, across audience, across time.
Available today.
The improvements to Contextual Messaging take effect immediately for both live campaigns and newly set-up CM campaigns. No user action is needed. Existing campaigns will begin benefiting from cross-category learning on their next generation cycle.
Book a demo to see what Jacquard can do for your 1:1 messaging.
Adaptive Generation Strategies for Contextual Messaging is available today to all customers using Jacquard’s CM module.
Want to explore how Language2 and Neural2 can transform your marketing performance?
