How can I measure LLM and AI-driven customer interactions?

Customer Journey Analytics lets you tag and classify LLM and AI traﬃc with derived ﬁelds based on user agent, referrer and query parameters, so AI-generated interactions are separated from human behaviour and your existing KPIs stay accurate. From there you build segments and dashboards to trend AI traﬃc volume, journeys and downstream conversions with your other channels, rather than tracking AI traﬃc in a separate tool.

How does Customer Journey Analytics connect AI conversations to business outcomes?

Conversation and AI interaction data is ingested into Adobe Experience Platform, modelled with intents, topics, sentiment and outcomes, then joined with your web, app and oﬄine datasets in Customer Journey Analytics to build full cross-channel journeys. That join is what lets you attribute a downstream outcome, a purchase, a churn event, a support resolution, back to a speciﬁc AI conversation and measure its impact on your KPIs over time, rather than treating the conversation as a cul-de-sac.

What does conversational AI analytics add beyond a transcript review?

Conversational AI analytics extracts intent, topics, keywords and sentiment from chat or voice transcripts using NLP or LLM models, then combines those signals with journey data in Customer Journey Analytics for aggregate reporting and conversation replay, rather than leaving each transcript as an isolated record. Tracking sentiment with outcomes is what makes this useful for a decision: it lets you pinpoint the speciﬁc experiences or ﬂows frustrating customers, prioritise ﬁxes and conﬁrm which changes actually improved satisfaction and conversion.

How does LLM insights support AI search visibility and brand discoverability?

LLM insights, together with Adobe Brand Visibility, shows where your brand is mentioned or cited in AI answers, how often AI agents crawl your content and which prompts or topics you are winning or losing. Once that visibility data is connected into Customer Journey Analytics, you can see which AI-sourced visits actually drive engagement and revenue, which is what turns an AI-visibility metric into a content and SEO investment decision rather than a vanity number.

What data sources and integrations are required to implement LLM insights?

You typically combine AI interaction and log data, chat transcripts, bot metadata, CDN or edge logs and Brand Visibility agentic and referral traﬃc, with your existing digital and oﬄine datasets in Adobe Experience Platform, then connect them into Customer Journey Analytics for reporting. Optional integrations with Customer AI, Adobe Journey Optimizer and native Brand Visibility data sharing enrich those journeys further with predictions and AI-visibility metrics, so you can start with the interaction data alone and add the rest as it becomes available.

Adobe Customer Journey Analytics Features

LLM insights

Transform your own branded conversational experiences into a measurable new channel with an intelligence layer that reveals and explains AI behaviour within customer journeys. By making these emerging touchpoints transparent and actionable, Adobe empowers businesses to understand the impact of LLMs on customer engagement and act with confidence.

Conversational insights (coming soon)

Turn branded conversational experiences into actionable intelligence by understanding the impact of tone, sentiment and intent on business outcomes. This helps improve AI agent performance, personalise experiences and strengthen engagement across channels. Contextualise these signals within the full customer journey to understand how conversational touchpoints influence downstream customer behaviours and impact.

UI mockups showing a conversation reply with text messages and an insights summary table displaying intents and conversation scores.

Ad featuring a red puffer jacket with performance metrics showing 2K Chat GPT mentions and a 24% increase in LLM conversions.

Brand Visibility integration

Understand how brand discoverability in LLMs results in unique customer engagement with actionable insights that connect platform traffic, content demand and engagement patterns to business outcomes such as form fills, purchases or pipeline influence.

LLM app data

Use your existing Adobe Experience Platform Web SDK and Data Collection APIs to bring interaction data from your LLM‑embedded applications into Customer Journey Analytics. Connect LLM‑based engagement to customers and unify those behaviours with activity across web, mobile and in‑store channels.