What Is Pulsar TRAC? Features, Use Cases, and Pricing Guide

Pulsar TRAC is an audience intelligence and social listening platform that ingests public conversation across 45+ source types, maps the communities behind that conversation, and surfaces narrative and cultural context for brand, comms, insights, and agency teams. TRAC replaces generic mention monitoring with community-level analysis, audience segmentation, and narrative tracking at enterprise scale.

Published 22 May 2026 | Last updated 23 April 2026 with Pulsar TRAC knowledge-base grounding (search types, Boolean and Wizard construction, proprietary metrics, AI analysis, alerts, Hootsuite integration) and the 2026 product additions: the 3D Influencer Network Graph and Pulsar Workspaces.

What Is Pulsar TRAC?

Pulsar TRAC is an audience intelligence and social listening platform built for enterprise brands, global agencies, and government communications teams. It collects public conversation across social media, news, forums, reviews, and broadcast, then groups that conversation into communities, narratives, and audience segments that brands can act on. For how enterprise teams deploy this capability across functions, see our guide to enterprise social listening.

TRAC replaces the generic mention dashboard model with community-level analysis. Its core job is to answer who is talking about a brand or category, what story they are telling, and how fast that story is moving. For a full view of where TRAC sits in Pulsar's product suite, including its pairing with Narratives AI (see also the dedicated Narratives AI explainer) and Crisis Oracle, see the solutions overview. For the broader category primer, see social listening vs social monitoring.

How Does Pulsar TRAC Work?

TRAC operates in three stages from raw conversation to stakeholder-ready insight, with all analysis organized around a Search object that is either a Topic, Panel, or Content search.

1. Search setup: Topic, Panel, or Content

Every TRAC workflow begins with a Search. A Topic search tracks keywords, hashtags, or phrases for trend and sentiment analysis. A Panel search tracks specific authors or communities for audience insights. A Content search tracks URL shares and sentiment for a specific piece of content. Queries are built either with the Wizard (a comma-and-plus interface with operator shortcuts, CSV bulk upload, and a blacklist) or with full Boolean: AND, OR, NOT, parentheses, proximity (~n, up to 6 words), and target operators including LOCATION, LANG, MEDIA, and NOT SITE. Boolean expressions are capped at 5,000 characters, and an AI Boolean Generator converts natural-language briefs into valid queries.

2. Data collection: real-time, historic, and first-party

TRAC ingests data from 45+ source types spanning global social (X, Facebook, Instagram, TikTok, YouTube, LinkedIn, Threads, Pinterest, Discord, Twitch), APAC platforms (Weibo, WeChat, Xiaohongshu, Douyin, Bilibili, Naver), alt-social networks, forums, review sites (Amazon, TripAdvisor, Trustpilot), licensed online and print news (NLA, CLA, CFC, LexisNexis, CAL, PMCA, Isentia), broadcast (TV, radio, podcasts), and Google-sourced search data. X data refreshes every 90 seconds; historical depth varies by source (X from March 2006, blogs and forums on a 25-month rolling window, broadcast transcripts archived for 2 years). Historical orders up to 250K mentions are auto-approved; larger orders route to the Account Manager. Teams can also upload their own CSV data as a first-party source that sits alongside social and media data.

3. Community detection, AI analysis, and insight output

Incoming conversation is grouped into communities based on follow graph, shared interests, and content behavior via integrations with Audiense (Affinities and Interconnections Clustering, up to 100,000 authors, results in 5 to 20 minutes) or StatSocial (minimum 300 authors). Posts are then clustered into narratives rather than counted as standalone mentions, and a layered AI stack scores each item for sentiment (−1 to +1), emotion (Anger, Disgust, Fear, Joy, Sadness), topics, and entities (People, Organisations, and, from the July 2025 LLM upgrade, Products, Brands, and Events). Outputs surface through Snapshot Overview dashboards, Audience Insights, Content Insights, Custom Dashboards, Coverage Reports, Email Digests, KPI and Instant Alerts, and a full export and API layer that feeds BI, CRM, and Hootsuite.

What Are the Main Features of Pulsar TRAC?

TRAC's feature set is organized across three layers: proprietary metrics, AI analysis, and community/narrative intelligence. All three are accessible from the same Search object and surface through over 50 real-time visualizations, Custom Dashboards, Coverage Reports, and the alert and export stack. The 2026 release added two notable capabilities: the 3D Influencer Network Graph (an immersive upgrade to network analysis) and Pulsar Workspaces (the organizational layer for enterprise insight teams).

1. Community detection and audience segmentation

TRAC maps online communities from network analysis and shared content patterns via Audience Insights. The Audiense integration supports two segmentation modes (Affinities Clustering from shared interests and Interconnections Clustering from mutual relationships) for audiences up to 100,000 authors, with results returned in 5 to 20 minutes; StatSocial is available as an alternate integration from a 300-author floor. Each community becomes a reusable audience filter that can be applied across feeds, charts, and alerts. For the methodology, see our community segmentation and audience segmentation strategy guides.

2. Proprietary metrics: Visibility, Velocity, AVE, and Media Reach

TRAC's metric layer goes beyond raw volume. Visibility is Pulsar's proprietary impact score, combining impressions, engagements, social shares, channel type, and content type, recalculated at 6 and 12 hours after publication to capture the content's full lifecycle. Velocity measures the rate of conversation per minute, hour, or day and compares the current period against the previous to show acceleration. AVE estimates the paid-advertising equivalent of unpaid coverage for online news, print, TV, radio, and podcasts. Media Reach and Media Impressions use SimilarWeb unique visitors for online news, circulation figures for print, and network audience for broadcast. Engagements are split into Engagements as Content (searchable items such as replies and reposts) and Engagements as Counts (numeric counts such as likes) to avoid volume inflation.

3. AI analysis: sentiment, emotion, topics, entities, credibility, image, and video

The NLP stack covers 68+ languages and bundles sentiment, emotion, topics, and entities; sentiment and emotion scoring have deep coverage for 25+ languages and score content in its native language rather than translating first. Sentiment runs on a −1 to +1 scale; emotion maps to Anger, Disgust, Fear, Joy, and Sadness. From the July 2025 LLM upgrade, entity detection covers People, Organisations, Products, Brands, and Events, with disambiguation (Apple the company vs the fruit) and per-entity sentiment and emotion. NewsGuard-powered Credibility scoring evaluates news sources on nine journalistic criteria out of 100 (green, red, platform, satire labels), supporting misinformation tracking and source quality reviews. Image Analysis runs logo detection for trained brand/industry logos plus general Image Caption labelling. Video Transcripts cover X, Instagram, YouTube, and Facebook video in 16 languages, with on-demand (up to 120 videos at once) and automatic modes, priced in hourly packages.

4. Narrative tracking and the 3D Influencer Network Graph

TRAC clusters related conversation into narratives and tracks how each narrative evolves over time. The Clusters analysis uses PageRank-based force-directed layouts to generate up to 10 distinct clusters per dataset, each named after its most central terms. Influencer Network Analysis visualizes engagement graphs across X, YouTube, Facebook, and other supported sources (authors and accounts as nodes; replies, comments, and reposts as edges; node size reflects engagement generated; automatic colour-coded clusters reveal distinct communities and sub-communities). The 3D Influencer Network Graph, released in 2026 at no additional cost for all TRAC licenses, adds spatial depth: teams rotate, zoom, and explore from multiple angles to disentangle complex conversations, surface bridge accounts, and map amplification patterns that remain hidden in two dimensions. Graphs export to PNG, XLS, SVG, or Gephi for independent analysis. For the dedicated narrative layer that pairs with TRAC, see narrative intelligence.

5. Trend detection

TRAC surfaces emerging conversation clusters ranked by momentum rather than current volume, feeding directly into Pulsar TRENDS for longitudinal analysis. Paired with Narratives AI, teams can detect cultural shifts weeks before they reach mainstream coverage, with community attribution baked into the signal. Google-sourced Search Data is available alongside social data (Search Questions, Search Connections, Search Comparisons, Related Searches) to reveal the gap between public discussion and private searching. For the step-by-step workflow, see our guide to detecting emerging consumer trends with AI.

6. Campaign monitoring, reporting, and alerting

Live campaign dashboards combine volume, sentiment, narrative formation, and community amplification. Outputs include Custom Dashboards (personal and team), Coverage Reports (curated via Shortlists, up to 20 charts and 1,000 media items, exported as PDF or scheduled email), Email Digests (Daily, Weekly, Monthly, Quarterly, with AI-powered summarization and up to three nested subgroups), KPI Alerts on Sentiment, Volume, or Visibility thresholds with optional GPT-4 summarization of alert drivers, and Instant Alerts delivered via email, in-app, or mobile push within minutes of publication across 20+ languages. Visualizations export to PNG, XLS, and SVG, and Excel exports ship up to 200K rows per Results tab.

7. API access and integrations

Full API access (use-case approved, keyed off the search hash) exports TRAC data into existing BI tools, CRM platforms, and custom dashboards. A dedicated Hootsuite integration surfaces saved Pulsar TRAC searches as Hootsuite streams for publishing, reposting, and reply workflows. Influencer networks export to Gephi for deeper graph work, and first-party CSV uploads let teams blend their own survey, CRM, or support-ticket data alongside social and media sources inside the same Feed.

8. Pulsar Workspaces (2026)

Pulsar Workspaces, introduced in February 2026, is the organizational layer that transforms Pulsar TRAC from a flat catalogue of searches into a structured enterprise system. The hierarchy nests Workspaces at the top, Search Folders as organizational containers (the first capability to roll out), and individual Searches within folders. Teams group searches by project, client, team, region, or function; set folders to public or private; assign ownership; and share access in one click or in bulk rather than one search at a time. As Pulsar CEO Francesco D'Orazio framed it: "As enterprise insight teams scale globally, the tools they use must evolve from simple catalogues of individual datasets into structured systems of insight." The layer is built for distributed regional teams, agencies, and large internal teams where dozens of markets and hundreds to thousands of active searches otherwise overwhelm flat-list management.

What Are the Main Use Cases for Pulsar TRAC?

TRAC is deployed across six recurring use cases, each mapped to a core enterprise function.

Brand health monitoring (Brand Manager)

Brand teams track sentiment trajectory, share of voice, and narrative evolution across key markets via TRAC's brand reputation monitoring workflow. Continuous community-level monitoring replaces quarterly survey waves, giving brand managers early visibility into perception shifts driven by campaigns, competitor moves, or external events. Dashboards are configurable by market, product line, and audience segment.

Audience segmentation and strategy (Strategist)

Strategy teams use TRAC's community detection to map audience clusters for positioning, targeting, and creative development. Behavior-based segments surface communities that conventional demographic profiling misses, including cross-category audiences and emerging affinity groups that conventional research cycles cannot easily uncover at timely speed. For the methodology, see our audience segmentation strategy guide and the deeper case for audience research beyond demographics.

Crisis detection and early warning (Comms)

Communications teams configure TRAC to monitor niche communities where reputational narratives typically form first. Paired with Crisis Oracle, escalation is flagged through narrative momentum and velocity scoring, giving comms leads hours of lead time before mainstream coverage begins. For the dedicated framework on adversarial narrative formation, see our guide to narrative attacks and narrative risk, and for ongoing measurement, how to monitor your brand narrative.

Campaign analysis and reporting (Agency)

Agencies use TRAC to brief, track, and retrospectively analyze campaigns. Community amplification data identifies which audience clusters picked up the creative, which messages resonated, and which channels drove the strongest organic lift, supporting both mid-flight optimization and final performance reporting to clients. For the full agency playbook, see our guide to audience intelligence for agencies.

Cultural intelligence (Insights)

Insights teams use TRAC to understand the cultural context behind audience behavior. Narrative clustering, trend detection, and community affinity data surface the underlying drivers of category conversation, which feeds into innovation roadmaps, positioning reviews, and long-range strategic planning for the executive team. For the full range of deployments TRAC supports, see the social listening use case guide.

Competitor tracking (cross-functional)

Every function uses TRAC for competitor tracking. Competitor mentions, narrative share, audience overlap, and community migration are tracked side by side with your own brand, revealing where rival audiences are shifting, which competitor narratives are gaining traction, and where a strategic opportunity is opening up. For the full workflow, see our guide to social listening for competitive analysis, or for a head-to-head view, our Pulsar vs Brandwatch comparison.

Pulsar TRAC Capabilities at a Glance

The table below summarizes what ships inside a TRAC workspace, mapped against the metric, analysis, and output layers.

Is Pulsar TRAC the Right Fit?

TRAC is built for teams whose work depends on understanding who is driving conversation, not just how much conversation there is. Use the persona-based guide below to judge fit.

Enterprise brand and insights teams

If your remit includes global brand health across 10+ markets, audience segmentation that feeds innovation and positioning, and cultural intelligence for long-range strategy, TRAC's community detection, 68+ language NLP, and Audiense integration are the deciding factors. If you only need English-language volume dashboards, a lighter tool is a closer fit.

Comms and crisis leads

If early-warning detection in niche communities matters, TRAC's combination of Velocity tracking, KPI Alerts (threshold-based, with GPT-4 summarization of drivers), and Instant Alerts (mobile push within minutes of publication, 20+ languages) is the core reason teams choose it. Pair with Crisis Oracle for the predictive layer.

PR and media monitoring teams

If your workflow centres on press coverage, TRAC's PR & Comms workspace, licensed news coverage (NLA, CLA, CFC, LexisNexis, CAL, PMCA, Isentia), AVE calculation, News Syndication handling (Exclude / Group / Merge), Coverage Reports, and broadcast transcripts in 16 languages carry the weight. For agencies, the Hootsuite integration adds publishing and reply workflows on top of TRAC monitoring.

Data science and analytics teams

If you plan to build internal models on top of social data, the full API, first-party CSV ingestion, Gephi-ready network exports, and 200K-row Excel exports (with documented column schema) provide a stable pipeline. Boolean queries up to 5,000 characters and proximity operators (NEAR/n, ~n) give enough precision for research-grade datasets.

How Is Pulsar TRAC Different From Standard Social Listening Tools?

Most social listening platforms treat conversation as a stream of individual mentions and score them for sentiment in aggregate. TRAC segments conversation by the community producing it. For the underlying category distinction, see social listening vs social monitoring.

Community detection is the architectural differentiator. Rather than grouping audiences by age bracket, income band, or self-declared interest, TRAC builds audience segments from real network behavior: who follows whom, what content circulates between them, and which narratives they share. This produces segments that reflect how people actually cluster online, including communities that cross conventional demographic lines entirely. See community segmentation for the methodology.

The practical consequence is that TRAC surfaces audiences and narratives other tools cannot see. A demographic segment may contain three distinct communities with different relationships to the brand; TRAC identifies them individually. A trend forming in a niche community of 5,000 users is visible in TRAC's momentum scoring weeks before it reaches the volume that standard tools track. This architecture is also what makes TRAC the listening layer most teams pair with Narratives AI. For the full competitive landscape, see our guide to the best social listening tools in 2026.

How Much Does Pulsar TRAC Cost?

Pulsar TRAC is priced on an enterprise basis. There is no published tier list and no self-serve checkout. Pricing is configured per contract based on the variables that actually drive cost at enterprise scale.

Quotes are typically shaped by four inputs:

• Team size and seat count: number of users, workspaces, and the permissions architecture required across brand, insights, and agency teams.
• Data volume and source coverage: number of active queries, saved searches, and the breadth of source types (global social, APAC social, licensed news, broadcast, podcasts, forums).
• Language coverage and regional data: which of the 70+ supported languages are in scope, and whether APAC or alt-social data feeds are included.
• Support and integrations: named customer success manager, SLA tier, API access, and any custom onboarding or training.

For an accurate quote, request a TRAC demo and share your team's data scope. Enterprise contracts typically include onboarding, named support, and API access within the base agreement.

Frequently Asked Questions

Sources

• Pulsar TRAC: product page, first-party feature and coverage details
• Pulsar: Social Listening Solutions
• Pulsar Narratives AI: companion narrative detection layer (see also the Narratives AI explainer)
• Pulsar Audience Insights: community-based audience segmentation
• Pulsar TRENDS: longitudinal trend analysis with 17 years of historical data
• Pulsar: Brand Reputation Monitoring
• Pulsar Crisis Oracle: predictive crisis intelligence using narrative momentum scoring
• Pulsar: Narrative Intelligence Hub
• Pulsar: Social Listening Use Case Guide
• Pulsar: Audience Community Segmentation: segmentation methodology and strategy
• Pulsar: Audience Segmentation Strategy
• Pulsar: Enterprise Social Listening
• Pulsar: Audience Intelligence for Agencies
• Pulsar: Best Social Listening Tools 2026
• Pulsar TRAC Help Center: first-party product documentation for search types, metrics, analysis, filtering, alerts, and exports (referenced throughout this guide)
• Introducing the 3D Influencer Network Graph: Pulsar product announcement covering the 2026 upgrade, supported platforms, rotate/zoom exploration, and Gephi export positioning
• Introducing Pulsar Workspaces: Pulsar product announcement (16 February 2026) on the Workspaces organizational layer, Search Folders rollout, and governance framing for enterprise insight teams
• NewsGuard: source-credibility ratings powering TRAC's Credibility analysis (nine journalistic criteria scored out of 100)
• Audiense: community segmentation integration powering Affinities and Interconnections Clustering inside TRAC

Platform data reflects publicly available product information as of May 2026.

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