Sprinklr Insights is an advanced analytics platform that helps businesses better understand customer interactions across multiple channels. The 26.7.1 release brings new features to enhance customer experiences, drive significant business impact, and improve overall outcomes.

Summary

New Features

The following new features are being introduced in Sprinklr Insights:

LLM Insights

The following features are being introduced in Sprinklr Insight's LLM Insights modules:

Expanded LLM Insights Use Cases for AI Visibility Analysis

LLM Insights now includes additional report use cases to help you analyze AI-generated brand and industry perception from multiple perspectives. In addition to Brand Visibility and Competitor Visibility, you can now analyze industries, regions, and negative brand sentiment across major AI platforms.

Available report use cases:

• Brand Visibility: Track how your brand is represented, mentioned, and perceived across major AI platforms.

• Competitor Visibility: Compare your brand's AI visibility and sentiment against key competitors.

• Region Visibility: Analyze brand visibility and sentiment across regions to identify geographic differences in AI responses.

• Industry Analysis: Explore industry trends, key players, emerging topics, and AI-generated insights independent of a specific brand.

• Negative Brand Visibility: Monitor how your brand appears in response to negative, critical, and problem-focused queries.

These enhancements expand analysis beyond brand and competitor visibility, enabling you to uncover industry trends, regional variations, competitive positioning, and potential reputation risks. This helps teams make more informed decisions and optimize AI visibility strategies.

Designed for Brand, Marketing, Competitive Intelligence, and Reputation Management teams, LLM Insights provides a comprehensive view of brand, competitor, industry, and regional visibility across major AI platforms.

For further details, see: Use Cases.

Feature Updates

The following feature updates are being introduced in Sprinklr Insights:

Data Sources and Channels

The following features are being introduced in Sprinklr Insight's Data Sources and Channels modules:

Instagram Benchmarking: New Enhancement Metrics

You can now access additional Instagram engagement metrics in Benchmarking, including Total Likes, Total Comments, and Reposts, providing a more complete view of content performance across owned and competitor accounts. These metrics are sourced from Instagram's latest API capabilities and help marketers, analysts, and social media teams evaluate engagement more accurately.

Key Benefits

• Access Total Likes, Total Comments, and Reposts for Instagram content. 

• Gain a more complete view of content engagement and performance. 

• Improve promoted post detection and engagement analysis. 

• Reduce metric discrepancies between Benchmarking and native platform analytics. 

This enhancement is particularly valuable when comparing owned and competitor Instagram content, helping you make more informed decisions using richer engagement insights and improved reporting accuracy. 

For further details, see: Instagram-specific data entities in benchmarking and major engagement stats.

Trend Discovery: X news post fetch in 3rd pane

You can now view the X posts associated with an X News cluster directly within the third pane of Trend Discovery. With this enhancement, marketers, analysts, and social listening teams can access related X posts and news insights in a single view, making it easier to investigate trending post, and validate emerging conversations.

Key Benefits

• View X posts associated with a news cluster.

• Switch between news details and supporting posts for deeper trend analysis.

When you select a news cluster, the third pane opens to provide additional context about the story. Information is organized into dedicated sections, allowing you to review details, related topics, key and the X posts driving the trend, all without leaving Trend Discovery. This unified experience helps you quickly connect news developments with the social conversations behind them.

For further details, see: Accessing Search News.

Threads Topic Tags: Support for Multi-Word Tags with Advanced Operator

You can now register and manage Threads Topic Tags containing spaces through the new advanced operator threadsTag, ensuring multi-word topic tags are accurately recognized and tracked.  With threadsTag, clients can now create topic tags using multiple keywords and phrases containing spaces, enabling more precise tag detection, reducing ambiguity, and improving the overall accuracy of Listening results by better capturing conversations relevant to their brands, products, and campaigns. 

Key Benefits

• Support for spaces in topic tags, enabling tracking of multi-word phrases. 

• Ability to create topic tags using multiple keywords, rather than being restricted to a single hashtag-style term. 

• Improved Listening accuracy, helping clients better capture and analyse conversations relevant to their tag.

For further details, see: Setting Up Threads Topic Tags.

LLM Insights

The following features are being introduced in Sprinklr Insight's LLM Insights modules:

Enhanced Analysis and Report Configuration

We have enhanced LLM Insights with new reporting dimensions, analysis capabilities, and usability improvements. You can now configure reports using inputs such as Region, Domain, and Industry, perform more accurate sentiment analysis, leverage new analysis use cases, manage brand aliases after report creation, access clickable URLs, and view unranked prompts directly in the interface. These updates make it easier to create, manage, and analyze reports for deeper competitive and brand insights.

Key Benefits

• Create more targeted reports with additional input dimensions.

• Expanded LLM insights with new reporting dimensions, analysis use cases, sentiment reporting enhancements, and usability improvements.

• To provide greater flexibility, improve analysis accuracy, and streamline report management.

You can generate richer insights and manage LLM Insights reports more efficiently.

For further details, see: Use Cases and Understanding LLM Insights Report.

Customer Feedback Management

The following features are being introduced in Sprinklr Insight's Customer Feedback Management modules:

CFM Questions Support in Case Conversation View

You can now view CFM survey responses directly within the Case Conversation timeline, giving agents, supervisors, analysts, and customer experience teams a unified view of case interactions and customer feedback in one place. Survey feedback is displayed alongside case activity, eliminating the need to switch to separate modules during case reviews. This helps you better understand the customer experience, validate feedback against case actions, and accelerate investigations, audits, and resolutions.

Key Benefits

• View survey responses directly within the Case Conversation timeline.

• Access customer interactions and feedback in a single, unified view.

• Understand customer experiences in the context of case activity.

• Accelerate investigations, audits, and resolution workflows.

• Improve agent efficiency when reviewing customer feedback.

This enhancement enables agents and analysts to review customer feedback alongside case history, identify service gaps, validate customer concerns, and gain a more complete understanding of the customer journey without leaving the conversation view.

Distribution Sampling Support

Distribution Sampling Support is now available for In-App Distributions, allowing you to define the percentage of eligible users who receive a survey. This gives you greater control over survey reach, helping to balance feedback collection with the overall user experience. By limiting survey exposure, you can gather meaningful insights from a representative audience while minimizing survey fatigue.

Key Benefits

• Control the percentage of eligible users who receive an in-app survey.

• Reduce survey fatigue by limiting survey exposure.

• Collect feedback from a representative sample of users.

• Maintain response quality while optimizing the user experience.

• Improve survey distribution management.

This enhancement is especially useful for high-traffic applications, targeted feedback programs, and scenarios where you want to gather representative feedback without overwhelming users. It helps optimize survey reach, improve feedback quality, and maintain user engagement across digital experiences.

For further details, see: Setting up In-App Distribution

CSAT Mobile and Web Experience Enhancements

The embedded CSAT survey experience has been enhanced across web and mobile email clients to provide a more consistent and user-friendly experience. Improvements include better scale label visibility, optimized alignment for larger rating scales, and accurate rendering of multi-statement CSAT questions. These updates help respondents complete surveys more easily while ensuring surveys display correctly across devices and email clients.

Key Benefits

• Improves the readability of CSAT scale labels across devices.

• Optimizes label alignment for larger rating scales.

• Ensures multi-statement CSAT questions render correctly in emails.

• Delivers a more consistent survey experience across email clients and devices.

This enhancement is available in the latest release and applies to embedded CSAT surveys accessed through supported web and mobile email clients.

For further details, see: Setting Up Adhoc Email Distribution

Exclusive "Other" Option for MCQ Questions

This release enhances Multiple Choice Questions (MCQ) by allowing the Other option to be configured as an exclusive choice when Exclusive Options are enabled.

Previously, respondents could select Other alongside other answer choices, even when exclusive options were configured on the question. This could result in contradictory responses and reduce the reliability of survey data.

With this enhancement, the Other option now participates in exclusivity rules. When a respondent selects Other, any previously selected options are automatically cleared. Similarly, if a respondent selects another option after choosing Other, the Other selection is automatically removed. The platform also prevents the creation of multiple Other options within the same question, ensuring a consistent and logical survey experience.

For further details, see: Multiple Choice Questions

MCQ Answer Condition Limits for Non-Mandatory Questions

This release enhances Multiple Choice Questions (MCQs) by extending minimum and maximum answer selection limits to non-mandatory questions, providing greater control over response validation and data quality.

Previously, answer condition limits were enforced only for mandatory questions. Respondents could select an unlimited number of options when answering an optional question, making it difficult to control response quality and collect meaningful preference data.

With this enhancement, survey creators can configure and enforce minimum and maximum selection limits regardless of whether a question is mandatory. For non-mandatory questions, the minimum selection value can be set to 0, allowing respondents to skip the question entirely. If respondents choose to answer, the configured maximum selection limit is enforced. For mandatory questions, the minimum selection requirement continues to be enforced at 1 or more selections.

This capability is supported across standard, conversational, and email-embedded surveys, as well as all supported distribution channels, views, preview modes, and templates. Survey creators can use this enhancement to improve response quality while preserving flexibility for optional questions.

For further details, see: Multiple Choice Questions

Reduced Minimum Scale Item Requirement for Matrix Questions

This release reduces the minimum number of scale items required in Matrix questions from three to two, enabling simpler binary-response survey designs.

Previously, Matrix questions required at least three scale items, preventing survey creators from building common two-option matrices such as Yes/No, Agree/Disagree, or Pass/Fail. As a result, administrators often had to add unnecessary scale items, which could complicate survey design and affect response quality.

With this enhancement, Matrix questions can now be configured with as few as two scale items, while maintaining all existing functionality, rendering behaviour, and validation capabilities. The platform continues to enforce a minimum requirement, preventing configurations with fewer than two scale items.

This capability is supported across standard, conversational, and email-embedded surveys, as well as all supported distribution channels, views, templates, preview modes, and AI-generated surveys. Survey creators can use this enhancement to build cleaner, more intuitive matrix questions that better align with binary-choice feedback and compliance use cases.

For further details, see: Survey Logics Introduction

Thank you for using Sprinklr Insights. The 26.7.1 release brings significant enhancements and new features designed to make your experience even better. Please contact us at tickets@sprinklr.com for any questions or assistance.

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