LLM insights

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.