Sprinklr Insights AI/AI+ Features
Updated
Sprinklr AI+ is an advanced AI-powered suite integrated into Sprinklr's Unified-CXM platform, designed to transform unstructured customer experience (CX) data into actionable insights. The following table gives details on multiple AI+ features of Sprinklr Insights.
Module | Feature Name | Feature Description | KB Article Link | Available in AI+ Studio (Y/N) | Can feature be turned on or off? | Is this feature access controlled? |
Social Listening | Entity Operator | Utilizes Sprinklr’s AI-driven entity linking to refine search queries based on contextual relevance, reducing confusion from similarly named entities. Enhances search accuracy, saves time, and improves user experience by automatically filtering out irrelevant data for specialized research, market analysis, and more. | No | No | No | |
Social Listening | Smart Theme Explorer | Smart Theme Explorer (STE) is an AI-powered solution that transforms unstructured conversational data into actionable insights with minimal manual intervention. By leveraging advanced NLP techniques (such as SpaCy’s part-of-speech tagging and dependency parsing), STE automatically identifies and highlights key recurring themes in real time, helping businesses enhance customer engagement, monitor brand sentiment, and inform strategic decision-making. | No | No | Yes | |
Social Listening | Influencer Score | The Influencer Score feature quantifies a social media user's influence to improve user experience. It evaluates influence by analyzing metrics like followers, followings, and posts/tweets. The score is categorized into buckets of 10, allowing users to easily understand and use this information for informed decision-making. | No | No | No | |
Social Listening | Conversation Insights | Analyzes and synthesizes large volumes of social media or conversational data using advanced NLP and clustering algorithms. Automatically groups similar messages into clusters and provides concise sum Conversation Insights maries, helping businesses quickly detect emerging trends, monitor brand sentiment, and track competitive activity at scale, all with minimal manual effort. | No | No | Yes | |
Social Listening | Intuition Moderation | Intuition Moderation automatically categorizes high volumes of social media messages so important interactions aren’t overlooked. By filtering out irrelevant content, it improves response times and enhances customer satisfaction, helping teams focus on high-value engagement. | No | Yes | Yes | |
Social Listening | Global Category Dimensions | The Global Category Dimensions feature categorizes and analyzes data using predefined classes, offering a structured overview of main themes like Service Quality, Product Performance, and Customer Churn. This helps users gain quicker insights and make informed decisions efficiently. | No | No | ||
Social Listening | Brand Reputation Dimensions | Enables teams to monitor and analyze social data for emerging crises and track brand impact across key reputation factors—such as employee issues, financial performance, operations, diversity, ethics, and legal challenges. By customizing AI to catch new trends and filtering out noise or spam, analysts gain a clear picture of brand stability and can respond proactively to potential reputation risks. | No | No | No | |
Social Listening | Sentiment Detection | Sprinklr Sentiment AI Models analyze the emotional tone of text-based messages (for example, reviews, tweets, or news articles) and classify them as Positive, Negative, or Neutral. They provide real-time brand perception insights, case-level sentiment aggregation, and multilingual support. The out-of-the-box model can be retrained to improve accuracy, handle industry-specific terminology, and account for context (such as product or brand names). | No | No | No | |
Social Listening | Emotion | Uses AI-driven text classification to identify and categorize emotions in messages. This enables deeper, more granular sentiment insights for use cases like social media monitoring, marketing strategy refinement, content moderation, and overall sentiment analysis. By detecting emotions in real time and at scale, organizations can more effectively understand public perception, tailor communications, and intervene promptly when harmful or negative emotions surface. | No | No | No | |
Social Listening | Crisis Management | Crisis Management is an AI-powered solution that monitors, detects, and analyzes crisis events for a brand in real time. It captures crisis conversations, categorizes them, and provides summaries and automated alerts (including anomaly and summary cards). With a minimal setup process and comprehensive detection, it ensures timely insight, streamlined crisis handling, and protection of brand reputation. | No | Yes | Yes | |
Social Listening | Insights Copilot: Research Copilot | Insights Copilot is an advanced AI feature that parses and summarizes high volumes of social media data—quickly delivering actionable insights to users. It supports natural language queries, provides traceable sources for each insight, and is designed for ease of use and rapid time-to-insights. | No | Yes | Yes | |
Social Listening | Insights Copilot: Dashboard Copilot | An AI-embedded chatbot that provides responsive, conversational analytics on dashboard data. It offers both quantitative and qualitative insights, identifies the root causes of metric shifts, provides trend tracking, and suggests drill-down prompts to encourage user exploration. Future enhancements include the ability to add AI-generated widgets directly to the dashboard for deeper data storytelling. | No | Yes | Yes | |
Social Listening | Summarizer | Sprinklr AI+ is designed to enhance efficiency by offering summarization tools that extract key insights from various contexts. It includes features like the Article Paraphraser & Message Summary, which condenses lengthy messages for quick comprehension, and the Drilldown Summarizer, which provides concise summaries of top conversations linked to any data point. Additionally, the Widget Summarizer allows users to generate and pin key insights directly from widgets, minimizing the need for extensive data exploration and speeding up access to important information. | No | Yes | No | |
Social Listening | Spam | Uses a machine learning model to identify and label spam messages from listening data as either “Spam” or “Not Spam.” The model categorizes spam into subcategories—such as advertisements or inappropriate content—so you gain valuable insights into which types of spam messages are being fetched. This helps you filter or exclude irrelevant and potentially harmful messages to keep your social channels clean and align with your brand’s objectives. | No | No | No | |
Competitive Insights and Benchmarking | Sentiment Detection | Sprinklr Sentiment AI Models analyze the emotional tone of text-based messages (for example, reviews, tweets, or news articles) and classify them as Positive, Negative, or Neutral. They provide real-time brand perception insights, case-level sentiment aggregation, and multilingual support. The out-of-the-box model can be retrained to improve accuracy, handle industry-specific terminology, and account for context (such as product or brand names). | No | No | No | |
Competitive Insights and Benchmarking | Emotion | Uses AI-driven text classification to identify and categorize emotions in messages. This enables deeper, more granular sentiment insights for use cases like social media monitoring, marketing strategy refinement, content moderation, and overall sentiment analysis. By detecting emotions in real time and at scale, organizations can more effectively understand public perception, tailor communications, and intervene promptly when harmful or negative emotions surface. | No | No | No | |
Competitive Insights and Benchmarking | Spam | Uses a machine learning model to identify and label spam messages from listening data as either “Spam” or “Not Spam.” The model categorizes spam into subcategories—such as advertisements or inappropriate content—so you gain valuable insights into which types of spam messages are being fetched. This helps you filter or exclude irrelevant and potentially harmful messages to keep your social channels clean and align with your brand’s objectives. | No | No | No | |
Competitive Insights and Benchmarking | Paid Post Detection | The Paid Post Detection feature analyzes social media posts to determine whether they are paid or organic, using engagement metrics like likes, shares, and comments. It also estimates the spending value for promoted posts, providing brands with insights into competitor marketing strategies and budget allocations. | No | No | No | |
Competitive Insights and Benchmarking | Global Insights Category Dimension | Automatically classifies fan and customer messages into key brand performance categories (for example, Service Quality, Product Performance, Pricing) and analyzes the sentiment (positive, negative, neutral) attached to each category. This helps teams spot trending issues, benchmark competitor performance, and route feedback internally to the right groups. | No | No | No | |
Competitive Insights and Benchmarking | Consumer Equity Affairs Level I & II Dimensions | Uses AI-generated categorization to identify and label the primary call-drivers in fan messages (across both your brand and competitor content) according to predefined equity issue categories. This helps uncover the predominant themes or complaints fans are discussing, so you can more effectively address key concerns and refine your brand strategy. | No | No | No | |
Competitive Insights and Benchmarking | Benchmarking Themes | Streamlines the process of creating benchmarking themes and keyword queries with AI-generated suggestions. Sprinklr AI+ helps users maximize data coverage, reduce manual effort, and improve accuracy by automatically proposing theme names, Boolean keyword groups, and keyword variations (including hashtags, phrases, and translations). Users can then refine and select the suggestions that best suit their needs, ensuring comprehensive and precise monitoring of brands, campaigns, and industry trends. | No | Yes | Yes | |
Competitive Insights and Benchmarking | Content Themes Dimensions | Content Themes Dimensions uses generative AI to automatically categorize your outbound content—and your competitors’—by relevant emotional themes. It provides immediate performance insights on each theme, helping you quickly identify top-performing strategies, benchmark against the industry, and optimize your brand’s messaging for stronger engagement. | No | No | No | |
Competitive Insights and Benchmarking | Customer Journey Stages Dimensions | Uses generative AI to automatically categorize outbound content, including competitor posts, based on predefined customer journey stages. Automatically labels and analyzes content performance to save time and provide insights into which messaging resonates most at each stage, helping marketers benchmark their strategies and optimize engagement. | No | No | No | |
Competitive Insights and Benchmarking | Key Objectives Dimensions | Automatically labels outbound brand and competitor content with AI-generated “key objectives” categories based on text content, so you can quickly gauge the emotional appeal and stage of each piece of content. This categorization, paired with performance analytics, helps you identify top-performing posts, spot emerging trends, and ensure consistent, informed outbound messaging. | No | No | No | |
Competitive Insights and Benchmarking | Content Tones Dimensions | Uses AI-generated categorization of outbound content—both your brand’s and competitors’—into predefined emotional tones. By automatically labeling messages and presenting performance metrics associated with each tone, you can efficiently identify which content resonates best with your audience and benchmark against industry best practices. | No | No | No | |
Competitive Insights and Benchmarking | Care and Non Care Dimension | The Care and Non-Care Dimension uses AI-generated categorization to label each inbound fan message as either a customer care message or a non-customer care message. By detecting the broad theme or call-driver in the message, teams can more quickly identify issues that need attention, escalate them to the right internal groups, and gain a clearer picture of improvement areas, competitive benchmarks, and best practices. | No | No | No | |
Visual Insights | Logo Detection | Logo Detection identifies and classifies brand logos within images, providing enhanced brand visibility and consistent brand representation. By automating the process of discovering where and how your logo is used, it helps track marketing campaigns, detect unauthorized usage, and protect brand integrity. | No | No | No | |
Visual Insights | Similar and Exact Image Search | Similar and Exact Image Search lets you register images to an Elasticsearch index, then quickly retrieve visually similar or exact matches. By combining advanced visual analytics with multimodal (text and image) search capabilities, it helps you monitor brand and ambassador reach, analyze social media content at scale, and gain deeper insights into brand visibility and consumer sentiment. | No | Yes | No | |
Visual Insights | Similar and Exact Video Search | Similar and Exact Video Search helps you identify and compare similar or identical videos by extracting frames at regular intervals, transforming them into a shared vector space, and measuring their similarity. It efficiently handles different aspect ratios and video qualities, enabling rapid, large-scale detection of near-duplicate or exact video content. | No | Yes | No | |
Visual Insights | NSFW Detection | NSFW Detection automatically scans for and flags explicit or objectionable image content, then blurs or removes it according to your brand’s sensitivity thresholds. This proactive moderation approach helps protect both employees and audiences from exposure to disturbing or inappropriate media, and frees moderators to focus on higher-value tasks. | No | Yes | No | |
Visual Insights | OCR | Extracts and analyzes text from within images and videos, making it searchable and actionable. By using OCR, you can create specific “Listening Topics” to catch brand mentions, keywords, or phrases buried in visual media. This broadens your monitoring and analytics capabilities beyond traditional text-based channels, providing deeper insights and a more comprehensive data-driven strategy. | No | No | No | |
Visual Insights | Object Detection | Uses AI-powered object detection to automatically identify and categorize images or videos based on their content, reducing search time and clutter. By recognizing thousands of object classes, it provides accurate tagging and filtering so users can quickly locate relevant assets. | No | No | No | |
Visual Insights | Activities Detection | Automatically identifies and tags the activities shown within images or videos so teams can easily find and filter assets based on their actual content. By detecting up to three levels of detail on activities, the feature reduces clutter, speeds up discovery, and ensures more precise search results—saving time and providing deeper insights into where and how products or brands are appearing. | No | No | No | |
Visual Insights | Scene Detection | Automatically identifies and tags the contents of images or videos, enabling you to quickly locate and filter relevant assets. This feature reduces search times by employing advanced scene and object detection, minimizes clutter with accurate content-based filtering, and supports more precise, tiered tagging for tailored asset management. | No | No | No | |
Visual Insights | Age Detection | Leverages a two-step AI-driven process—first detecting any faces in social media images, then classifying each detected face into an age group. This automated approach provides deeper demographic insights and helps businesses tailor content, campaigns, and strategies to resonate more effectively with specific ages. | No | No | No | |
Visual Insights | Gender Detection | Gender Detection analyzes images to identify faces and determine each face’s gender (male or female) and sentiment (positive, negative, or neutral). By automating this two-step classification, it streamlines demographic and emotional analysis of social media content at scale, helping businesses tailor their engagement strategies, refine content, and gain deeper audience insights. | No | No | No | |
Visual Insights | Visual Sentiment Detection | Uses advanced image analysis to detect faces in social media content and classify each one by both gender and sentiment (positive, negative, or neutral). With these insights, teams can refine content strategies, track demographic and emotional trends, and more effectively tailor engagement efforts. | No | No | No | |
Customer Feedback Management | Survey Builder | AI Builder is a conversational survey builder that simplifies survey creation and management. Users provide high-level objectives and goals, and AI automatically generates comprehensive surveys without navigating multiple clicks or screens. AI Builder supports all question types, including multiple choice, CSAT, rank order, and open-ended text, streamlining the entire survey process in one interactive conversation. | No | No | Yes | |
Customer Feedback Management | Survey Quality | Provides a detailed evaluation of survey effectiveness, including brand consistency, clarity, and user engagement. Identifies potential issues—such as unclear language, overly long questions, and lack of brand compliance—and recommends improvements to create user-friendly surveys that yield higher-quality data. | No | No | No | |
Customer Feedback Management | Conversational Survey | Transforms traditional surveys into interactive dialogues for a more engaging user experience. Questions adapt to previous responses, allowing follow-up prompts that gather deeper insights. This conversational flow yields richer feedback and more personalized interactions than static surveys. | No | No | No | |
Customer Feedback Management | Response Quality | Uses AI to evaluate the quality and reliability of open-text survey responses. Flags profanity and nonsensical or gibberish entries to help maintain professional tone and ensure that only meaningful, accurate data flows into your analytics. The result is more trustworthy insights and a streamlined way to filter out low-quality responses so you can quickly assess and act on valid feedback. | No | No | No | |
Customer Feedback Management | Text Analytics | Text Analytics helps you quantify and understand open-ended text by extracting key phrases, assigning them to a hierarchical set of categories (taxonomy), and analyzing sentiment for each category. This structured approach streamlines comparative analysis, surfaces deeper insights about customer emotions, and simplifies identifying trends or areas of concern. Because the taxonomy is generated automatically from sample data, ensuring high-quality source text is critical for best accuracy and results. | No | No | No | |
Customer Feedback Management | Survey Insights | Transforms survey data into actionable insights by automatically analyzing MCQs, numeric, CSAT, text, and social feedback. It uses advanced analytical techniques—such as correlation, time series, and root cause analysis—to uncover hidden trends, and text thematic analysis to highlight key themes and sentiments. The result is faster, deeper understanding of survey results with minimal manual effort. | No | No | No | |
Product Insights | Executive View | Provides a comprehensive, panoramic insight into product and brand performance by identifying your top and low performers, the key drivers influencing results, and potential areas for improvement. Combines a broad range of data sources (product series, countries, and more) and uses generative AI to conduct multi-level root-cause analysis, surfacing actionable insights for strategic decision-making. | No | Yes | No | |
Product Insights | Insights Assistant | Uses generative AI to highlight critical insights from large volumes of textual feedback and product data. Insights Assistant identifies anomalies, surfaces trends, and performs multilevel root cause analysis. It ranks the highest-impact issues, ties them back to specific products or brands, and provides timely, actionable recommendations for improvement. | No | Yes | No | |
Product Insights | Product Insights | Product Insights uses AI to capture and analyze real-time customer feedback from multiple platforms, turning that feedback into actionable insights for product improvements. It helps businesses detect crises early through continuous monitoring and instant notifications, assigns alerts for quick resolution, and provides competitor monitoring and robust reporting. | No | |||
Location Insights | Location Insights | Location Insights uses AI to gather, analyze, and contextualize feedback from digital, social, and physical locations—giving businesses a 360° view of how customers feel about their experiences at local, regional, or global levels. Brands can understand what customers value most, pinpoint opportunities to boost satisfaction, and quickly detect and resolve location-centric issues by routing them to the right teams. | No | |||
Location Insights | Media Stories | Media Monitoring & Analytics (Media Insights) uses AI-powered clustering of news and social media content to give PR professionals a consolidated view of every event’s impact, helping them measure critical metrics like EMV, Reach, and Sentiment. By reducing manual effort and duplication, it provides customizable dashboards that track engagement across paywalled, online, and broadcast sources, enabling teams to benchmark performance against competitors and industry leaders. | No | No | Yes | |
Social Listening | Query Creation and Classification | Sprinklr AI+ Query Creation & Classification simplifies the process of developing themes, topics, and keyword queries to efficiently monitor brands, campaigns, or industry trends. By utilizing AI-generated suggestions, it enhances data coverage, reduces manual effort, and boosts accuracy. Users can create AI-generated topic queries, configure multiple themes, and generate keyword variations, hashtags, and translations with ease, selecting the best options from AI suggestions to optimize their monitoring strategies. | Yes | Yes | No |