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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.

Conversation Insights

Add the tone, sentiment and topics of conversational experiences, along with their contextual signals, to your full customer journey.

  • Group conversations by intent and topic, then see what customers are asking, how often and whether it was resolved.
  • Catch topic and friction shifts before they hit conversion or support volume, then drop into individual conversations to see why.
  • Attribute conversation outcomes to revenue, tickets and other downstream KPIs to prove what those conversations are worth to the business.
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.

Adobe 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.

Diagram linking AI search enhancer, AI marketplace and AI customer support to a line chart comparing increasing agentic traffic to declining traditional channel traffic across Q1-Q4.
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Learn how to use LLM insights features.

Find what you need in Experience League, our vast collection of how-to content — including documentation, tutorials and user guides.

Learn more | Learn more how to use LLM analytics features

Questions? We have answers.

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 topics, sentiment, tone 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?

Conversation Insights extracts tone, 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.
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m spacing
title
Explore the impact of LLMs on customer engagement.
CardDescription
Measure and analyse LLM interactions with Adobe Customer Journey Analytics to further understand impact and best actions for customer engagement.
cardDate
2026-04-20
cardImage
primaryTag
caas:content-type/product-page
Tags
caas:content-type/product-page, caas:products/customer-journey-analytics

LLM analytics

LLM analytics

Conversational insights (coming soon)

Conversational insights (coming soon)

{{brand-visibility}} integration

{{brand-visibility}} integration

LLM app data

LLM app data

Learn how to use LLM analytics features.

Learn how to use LLM analytics features.

Learn more

Learn more

Questions? We have answers.

Questions? We have answers.

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

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

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

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

What is conversational AI analytics and how does sentiment analysis improve performance?

What is conversational AI analytics and how does sentiment analysis improve performance?

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

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

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

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

Let’s talk about what Customer Journey Analytics can do for your business.

Let’s talk about what Customer Journey Analytics can do for your business.

Get started

Get started