How do I Track User Sentiment in Analytics?

To track user sentiment in analytics, first define which emotional signals matter for your product. Collect them through surveys, reviews, support tickets, and in-app reactions. Standardize scores and labels into structured events, feed them into your analytics stack, and build dashboards that trend sentiment by journey stage, segment, and revenue impact.

Scope:
Last updated: December 2025. Applies globally; align sentiment tracking and exports with local privacy laws such as GDPR and CCPA/CPRA

Clarify what “user sentiment” means in your analytics

User sentiment usually means how customers feel about your product, service, or brand, summarized as positive, neutral, or negative attitudes. It’s derived from things like survey responses, reviews, and open-text feedback using sentiment analysis models that classify emotional tone. 

Different but related concepts:

• Sentiment – emotional tone (happy, frustrated, mixed). 
• Satisfaction (CSAT/NPS) – how well expectations were met or loyalty to your brand. 
• Effort (CES) – how easy it was to complete a task or resolve an issue.

Next, map sentiment to your journey:

1. List key stages (onboarding, adoption, renewal, support, churn risk).
2. Decide what “good” sentiment looks like at each stage (e.g., “onboarding feels simple and guided”).
3. Identify which teams rely on these signals (product, CS, support, marketing).
4. Decide review cadence (e.g., real-time alerts plus weekly or monthly dashboards). 

This framing guides which sources, metrics, and dashboards you actually need.

Pick sentiment data sources to track

Surveys and in-app feedback

Surveys are the most direct way to quantify sentiment.

Core survey types:

• NPS (Net Promoter Score) – “How likely are you to recommend…?” on a 0–10 scale, used for long-term loyalty and relationship sentiment. 
• CSAT (Customer Satisfaction Score) – “How satisfied were you with…?” used for specific interactions or features. 
• CES (Customer Effort Score) – “How easy was it to…?” used to measure friction in key flows.

Best practices:

1. Trigger CSAT/CES immediately after important touchpoints (onboarding completion, support resolution, key feature use). 
2. Run NPS periodically (e.g., quarterly) to track overall relationship sentiment. 
3. Keep surveys short (1–2 core questions + optional “Why?” text field). 
4. Prefer in-app surveys where possible for higher response rates and better context. 

Reviews, support, and public feedback

A lot of emotion shows up outside structured surveys.

Key sources:

• Support tickets & chats – detailed, high-intent descriptions of pain and confusion.
• App store / marketplace reviews – public sentiment snapshots and trend lines over time. 
• Social media & communities – fast-moving reactions and complaints about issues you may not survey on. 

How to turn these into analytics-ready data:

1. Centralize text feedback from tickets, chats, reviews, and social into a common store or CX platform. 
2. Run sentiment analysis to label messages (e.g., positive/neutral/negative, plus scores).
3. Auto-tag themes like “onboarding”, “billing”, “performance” so you can trend sentiment by topic. 
4. Create alerts for spikes in negative sentiment around specific themes or releases. 

Implement tracking for sentiment signals

Once you know where sentiment comes from, you need to make it trackable.

1. Define your data model
   • Metrics: nps_score, csat_score, ces_score, sentiment_score.
   • Labels: sentiment_label (positive/neutral/negative), sentiment_topic, channel (survey, support, social).
   • Events: survey_submitted, feedback_reacted, review_received, etc.
2. CX platforms and product analytics tools typically expose similar metrics and properties.
3. Normalize scales
   • Keep raw scores (0–10 NPS, 1–5 CSAT, 1–7 CES).
   • Optionally derive a standard sentiment index (e.g., -1, 0, +1 or 0–100) for cross-source comparisons.
4. Instrument collection points
   • Fire analytics events whenever users submit surveys or in-app reactions. 
   • Attach sentiment properties to relevant lifecycle events (e.g., onboarding_completed with csat_score and sentiment_label).
5. Ingest external channels
   • Import structured outputs from ticketing, review, and social tools into your warehouse or analytics platform.
   • Ensure timestamps and user identifiers line up with your product analytics data for easy joins.
6. Backfill history
   • Run sentiment analysis on historical reviews and tickets to build a baseline for trends and cohort comparisons.

The goal is to treat sentiment the same way you treat product events: structured, joinable, and queryable.

Analyze and report on sentiment over time

With sentiment signals in place, you can start answering real questions.

1. Build sentiment dashboards
   • Trend NPS, CSAT, CES, and overall sentiment index weekly or monthly. 
   • Segment by plan, geography, lifecycle stage, and acquisition channel.
2. Connect sentiment to outcomes
   • Compare churn, expansion, and support volume between promoters, passives, and detractors.
   • Check whether negative sentiment in specific topics (e.g., “onboarding”) precedes higher churn or lower activation.
3. Monitor sentiment by journey and topic
   • Slice sentiment by onboarding vs. adoption vs. renewal to see where emotions dip.
   • Use topic tags to find “hotspots” (e.g., billing issues causing disproportionate negative sentiment). 
4. Set alerts and thresholds
   • Monitor sentiment and user feedback weekly or monthly using CustomGPT.ai’s analytics and export features. For NPS, CSAT, or CES, use external survey tools and integrate results with CustomGPT.ai exports for unified analysis. Set up alerts and thresholds using your analytics stack, as CustomGPT.ai does not natively provide automated notifications for these specific metrics.
5. Feed insights back into product and CX
   • Make sentiment dashboards visible to product, CS, and marketing.
   • Tie major changes (new onboarding, pricing, features) to explicit “before vs. after” sentiment reviews so you can see impact.

How to do it with CustomGPT.ai

CustomGPT.ai gives you built-in analytics and customer intelligence features that capture user emotion and feedback directly from AI agent conversations.

1. Enable feedback and analytics for your agent

First, create or select the agent you want to track. CustomGPT.ai’s analytics stack includes Account analytics, Agent analytics, and Customer Intelligence for deeper conversation insights.

To collect simple thumbs-up/thumbs-down sentiment, enable User Feedback in the agent’s settings. Once enabled, your agent starts collecting reaction feedback that can be reviewed and exported later. 

2. Use Agent Analytics to monitor user feedback trends

Open Explore agent stats and data for the relevant agent. There you’ll see activity metrics (queries, conversations, guest activity) and User Feedback summarized over a chosen timeframe (e.g., last 7 days, last 30 days). 

You can:

• View the volume of positive vs. negative feedback and how it changes over time.
• Filter by date ranges such as “Today”, “Last 30 days”, or a custom range to align with releases.

Use this as a high-level sentiment signal from your AI support or onboarding assistant.

3. Monitor emotion in User Insights and advanced metrics

Go to Monitor queries & conversations. In User Insights, CustomGPT.ai exposes advanced metrics including Emotion, Intent, and top languages for user queries. 

The Advanced Customer Intelligence Metrics documentation explains that these metrics analyze user queries to detect intent and emotion, helping with deeper decision-making. Use these views to:

• Track emotion trends (e.g., frustration vs. satisfaction) across all conversations.
• Filter to specific time ranges or deployment types to see sentiment during launches or campaigns.

4. Inspect conversation-level emotion in Customer Intelligence

Within Customer Intelligence, you can Explore and filter conversations to see detailed metrics for individual sessions. For each conversation, you can access:

• User Emotion and Intent
• Content source usage (whether relevant content was found)
• Other advanced metrics extracted by the analytics agent

This lets you review specific frustrated or delighted conversations and connect them to documentation gaps, product issues, or agent behavior.

5. Export sentiment data to your analytics stack

CustomGPT.ai provides multiple export paths:

• Export agent stats and data – export conversation data, breakdown charts, and user feedback from analytics screens (including daily query breakdowns and feedback).
• Export customer intelligence data – export advanced analytics fields like Query ID, Conversation ID, Agent ID, User Emotion, User Intent, Language, User Feedback, User Location, and more in CSV, Excel, or JSON. 

For programmatic access, the API provides:

• Customer intelligence analytics data endpoint – returns user interactions, emotions, intents, feedback, and behavioral analytics for a project.
• Conversation reports endpoints – summarize conversation metrics for dashboards and offline analysis, or export full conversation transcripts with feedback and metadata.

You can load these exports into your warehouse or BI tool and join them with product usage, revenue, or ticket data to build unified sentiment dashboards.

Example — Tracking sentiment for a SaaS onboarding flow

Let’s combine everything for a concrete SaaS onboarding use case.

1. Define the goal
   Reduce early churn and support load by making onboarding feel easy and trustworthy, measured via sentiment and satisfaction.
2. Instrument surveys and micro-feedback
   • Add a short CSAT/CES survey after users complete the main onboarding flow.
   • Run a periodic NPS survey to capture overall relationship sentiment for new cohorts.
   • Use in-app surveys or smiley/emoji surveys for quick, contextual reactions to specific steps.
3. Collect qualitative feedback
   • Tag onboarding-related support tickets and chats.
   • Monitor app store or marketplace reviews mentioning onboarding difficulty.
4. Deploy a CustomGPT.ai onboarding assistant
   • Build a CustomGPT.ai agent trained on your onboarding docs, FAQs, and troubleshooting guides.
   • Embed it in your app or help center to answer setup questions and deflect tickets.
5. Track sentiment inside CustomGPT.ai
   • Enable User Feedback and review positive/negative reactions in Explore agent stats and data.
   • Use Monitor queries & conversations → User Insights to monitor Emotion trends for onboarding queries.
   • Export Customer Intelligence data (including User Emotion and User Feedback) to CSV/JSON using the export guide.
6. Join sentiment with product analytics
   • Load CustomGPT.ai export files and survey data into your analytics stack.
   • Join on user ID or session where possible, then compare:
     • Activation and time-to-value for users with positive vs negative onboarding sentiment.
     • Drop-off points in the onboarding funnel vs topics with high frustration in conversations.

Finally, iterate: improve the steps and docs that show the worst sentiment, and watch the combined product + CustomGPT.ai sentiment metrics over time.

Conclusion

The real tradeoff isn’t whether to measure sentiment, but whether you keep guessing from vanity scores or actually wire emotion data into how you run the business.

Customgpt.ai closes that gap with built-in emotion analytics, customer intelligence, and exports that plug straight into your existing dashboards.

If you’re ready to turn every conversation into a measurable sentiment signal, get started with CustomGPT.ai and track user sentiment at scale.

FAQ’s

How do I track user sentiment in analytics without overcomplicating my stack?

Start by defining what user sentiment means for your product, then choose a few core signals like NPS, CSAT, CES, support tickets, and in-app reactions. Standardize them into events and properties such as scores, labels, and topics, and send them into your analytics or warehouse. From there, build dashboards that trend sentiment over time, slice by journey stage or segment, and link it to churn, retention, and revenue.

How can I use CustomGPT.ai for user sentiment analytics?

You can treat CustomGPT.ai as a live sentiment source by enabling User Feedback, which captures positive and negative reactions on conversations. Agent Analytics and User Insights expose emotion and feedback trends over time, while Customer Intelligence lets you inspect sentiment at the conversation level. Exporting this data as CSV, Excel, JSON, or via API lets you join CustomGPT.ai sentiment with your broader product and revenue analytics.