---
title: AI Visibility & Performance Measurement Using LLM Signals
description: Track AI presence, sentiment, citations, and interactions. Our frameworks integrate AI-native signals with traditional performance metrics that lead to actionable insights and recommendations.
---

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# Defining Success & Metrics

As user behavior changes, and brands evolve to meet consumers on AI (and AI-influenced) channels, the success metrics will need to shift. 

![clicks_icon](https://chartis.io/hubfs/clicks_icon.svg)

## Clicks → Presence

Clicks miss where influence now happens within AI Platforms. Brands now need to track AI Answer Presence, Summary Accuracy & Sentiment, Citation Share to accurately measure if and how the brand is being represented on these platforms.

![destinations](https://chartis.io/hubfs/destinations.svg)

## Destinations (Apps) → Tasks (LLMs) 

LLMs are evolving to build applications within their experience (Ecommerce/buy now, Zillow property search etc.) in an effort to remove friction in experiences. It also captures/engages users within these platforms for a bigger part of their journeys. Brands need to monitor this development closely and prepare to integrate (if beneficial).

![visits_icon](https://chartis.io/hubfs/visits_icon.svg)

## Visits → Interactions

With the growth of agentic ecosystem, more users will opt to interact with the brand content and functionality through agents.  Brands need to prepare for developing services/APIs specifically built for these agents.

## Action Plan

An Action Plan outlines the changes needed to modernize measurement frameworks, integrating AI-specific signals with traditional performance metrics for clearer decision-making

![one_logo](https://chartis.io/hubfs/one_logo.svg)

#### Redefine Measurement Framework

Evolve measurement beyond traditional digital acquisition metrics, introducing KPIs that reflect AI search performance, content efficiency, experience quality, and the brand health, tying presence to revenue impact and business outcomes. 

![two_logo](https://chartis.io/hubfs/two_logo.svg)

#### Integrate New Data Systems for AI Insights

Expand analytics capabilities by incorporating new data sources (1P+3P) for AI Search Visibility (LLM inclusion), entity recognition signals, semantic coverage, brand influence, and content performance intelligence. 

![three_logo](https://chartis.io/hubfs/three_logo.svg)

#### Establish AI Measurement Governance & Alignment

Create shared KPI definitions, workflos, reporting standards, and ownership models across teams to ensure consistent adoption and reliable decision-making.

![four_icon](https://chartis.io/hubfs/four_icon.svg)

#### Explore Custom Workflows Specific to Brands

Design measurement models tailored to the brand’svv business structure, by Services, Regions, audience personas, verticals and more. Build dashboards and workflows that reflect how search and content influence pipeline generation across key business unit and prioritize Global markets. 

![five_icon](https://chartis.io/hubfs/five_icon.svg)

#### Rebaseline, Test & Learn

Set new performance baselines aligned to update AI metrics, then test continuously as AI platforms continue to evolve and mature. 

[← PREV](https://chartis.io/ai-content-data-structuring-for-entities-schema-agents?hsLang=en)

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