October 13, 2026 • 5 min read
Contentsquare Relaunches Its Platform; Now It Analyzes Both Human and AI Customer Experiences
Director of Content & Market Research
October 13, 2026
Contentsquare has relaunched its Experience Platform to analyze, understand, and optimize human AND AI customer experiences.
Announced during its annual CX Circle event, the “reimagined” platform is designed to help organizations prepare for a future of personal AI agents.
Brands have already had a taste of this with agentic commerce, where customers ask a large language model (LLM) to research, compare, decide, and - in some cases - complete purchases on their behalf.
However, personal agents represent the next frontier of AI customers. Taking instructions from a human, they autonomously carry out tasks across a company’s website and, if they get stuck, contact and interact with customer service.
That future is fast approaching. Just three weeks after its launch last month, Meta Muse was downloaded five million times. Soon, similar personal AI agents may even become available via smartphone assistants, like Siri and Google Gemini.
Reocognizing this, Contentsquare has reimagined its platform to capture, analyze, and active behavioral data, conversation intelligence, and qualitative insights across two types of customers: humans and AI agents.
“Businesses now need a consolidated view of the full spectrum of customer interactions - across sessions, devices, and channels. Not only their customers, but also the LLMs and AI agents acting on their behalf. The winners will be those that can turn every signal into intelligence, and every insight into better journeys - quickly and automatically.”
Against this backdrop, here’s a look at the “reimagined” Contentsquare Experience Platform.
The New-Look Contentsquare Experience Platform
Contentsquare has developed new tools to analyze AI referrals, online customer conversations, and AI agent activity, capturing significantly more customer experience data.
Primarily, the new tools include:
- LLM and AI Traffic Analysis: This separates LLM and AI traffic from human visits, helping businesses understand how AI-driven discovery impacts shopping.
- Self–Service and Conversational Analytics: Contentsquare can now spotlight where AI and human customers get stuck on the website and why they escalate to customer support. It also connects what customers say to human or AI support agents with what they do next on the website, linking conversations to actual behavior.
- Agent Activity Monitoring: This uses WebMCP to monitor the activities personal agents perform that conventional analytics tools may miss.
By adding this new intelligence to the data Contentsquare already collects across sessions, channels, and devices, the vendor can provide a more complete picture of the journeys both human and AI customers take.
Here’s an example of why that matters. Imagine a scenario where the customer asks their personal agent to recommend a new pair of shoes. It surfaces a pair of shoes and the customer follows a link to look closer, opens a chat to ask about sizing, scrolls back through the product page, and then abandons.
The new-look Contentsquare platform will understand the relationship between the AI referral, online conversation, website experience, and abandoned purchase, gathering intelligence to recommend website and conversation design improvements.
Additional Platform Upgrades
Alongside new tools to analyze and optimize AI agent experiences, Contentsquare introduced two other major platform innovations: Benchmarks and Agentic Experimentation.
1. Benchmarks
The new “Benchmarks” capability uses AI to compare organizations against industry peers on specific website and mobile app features, interface elements, and user flows.
In doing so, it identifies underperforming elements of the online experience and quantifies the opportunity to improve them.
Consider a product page that’s converting below expectations. Instead of a conversion rate to benchmark against, it identifies poor-performing components and flows.
Ultimately, that gives CX leaders a more targeted starting point to decide what to test and change.
Meanwhile, brands can compare how well their website is set up for personal agents against the competition and, if behind, gather intelligence to close the gap.
2. Agentic Experimentation
Sense is Contentsquare’s AI layer that runs ongoing analysis to surface CX improvement opportunities, recommend tests, and suggest what to do next (now with visual stories).
Now, with agentic experimentation, it can take an identified issue and autonomously execute much of the optimization workflow.
In doing so, Sense utilizes AI agents to:
- Identify an opportunity: It identifies friction points or performance issues and estimates their potential financial impact.
- Form a hypothesis: It proposes a change intended to improve the experience or business outcome.
- Design and build the experiment: It designs and codes the proposed variation so it can be tested.
- Launch the test: It initiates the experiment to assess how the change performs.
- Validate the impact: It evaluates whether the experiment delivered the expected revenue impact.
As this workflow suggests, Sense turns insight into action, which is another key theme guiding the future of customer experience intelligence innovation.
The Future of Customer Experience Intelligence
For years, customer experience intelligence lived in siloes, where customer support teams gathered conversational intelligence and marketing teams looked at website and app analytics.
Now, brands are unifying that intelligence to surface new insights into where they can improve online experiences, reduce unnecessary customer contacts, and lower customer effort.
Many are also feeding these combined insights into adjacent CX platforms, from personalization engines that deliver more tailored online experiences to advertising platforms that optimize ad spend.
Contentsquare is supporting this trend by announcing new integrations with Dynamic Yield, Optimizely, and Kameleoon to support deeper personalization and testing workflows.
Looking further ahead, expect Contentsquare to extend further into cross-platform agentic workflows, putting its insights into action to autonomously improve experiences for both human customers and AI agents.