Sprinklr AI+ in Customer Feedback Management delivers a powerful suite of AI-driven capabilities designed to streamline survey creation, enhance feedback quality, and generate actionable insights. Key features include Survey AI+ Builder, Survey Response Quality, Survey Quality, Text Analytics (AI+), Survey Insights, Hypothesis Validation, and Conversational Surveys. Each module leverages large language models (LLMs) and predictive analytics to automate workflows, improve data accuracy, and enable confident, data-driven decision-making.

Key Features

Let us have a look at the key features:

• Conversational Survey: Conversational surveys let you engage your users through chat-like interfaces that boost completion rates and improve data quality. This interactive format turns questions into natural, conversational prompts, thus making the experience feel more approachable and less formal, especially on mobile and digital platforms.

  You can use this for post-interaction feedback, exit surveys, and checkout abandonment studies. The simplified, context-aware flow helps you boost engagement and encourages honest responses from users who might otherwise ignore traditional forms. You can refer to this article for further details.

• Ask AI+ (Survey Builder): The Smart Survey Reviewer assigns a Quality Score and highlights issues like unclear wording, excessive length, or too many questions. Each issue is labeled by severity (Passed, Low, Medium, or Severe), with actionable suggestions to improve clarity and increase response rates. This helps you reduce fatigue, improve feedback quality, and create a better survey experience.

  The Smart Survey Reviewer helps you ensure high-quality feedback and reduce drop-off rates. It lets you refine surveys for your target audience and maintain consistent data quality across all touchpoints.
  You can refer to this article for further details.

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• Question-level Suggestions: Question-level Suggestions automatically detect potential issues in your survey design, such as excessive length, unclear phrasing, or question overload. Issues are flagged inline within the survey builder alongside each question, with actionable suggestions to enhance clarity and boost response rates. This structured review helps improve feedback quality, reduce survey fatigue, and optimize the overall experience.

  Question-level Suggestions are particularly valuable for ensuring high-quality feedback and reducing drop-off rates. Users can refine surveys for target audiences and ensure consistent data quality across touchpoints.
  Refer to this article for more details.

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• Response Quality: The Response Quality module uses AI to assess how authentic and reliable each survey response is. It checks factors like completion time, relevance of open-text answers, logical consistency, and signs of bot activity. Based on these, each response is labeled as High, Medium, Low, or Bot, helping you trust the data you collect.

  You also get access to the Response Quality Manager dashboard, where you can track trends over time, identify potential data quality issues, and adjust response evaluation settings. This helps ensure cleaner data for analysis, so you can make confident, accurate decisions. Refer to this article for more details.

  

• Text Analytics (AI+): AI Text Analytics helps you turn unstructured open-text responses into structured, hierarchical categories (L1/L2 taxonomy) using generative AI. It identifies key phrases, assigns sentiment, and groups feedback into themes, making it easier for you to uncover nuanced insights across large datasets.

  Once you collect 500 responses, the model generates a base taxonomy tailored to your survey. You can then explore sentiment-tagged themes through visual dashboards like bar charts, word clouds, and response streams.

  This feature is especially valuable after CSAT or NPS surveys, helping you surface customer sentiment, uncover unexpected issues, and gather actionable suggestions. Refer to this article for more details.

  

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• Survey Insights: Survey Insights automatically generates easy-to-read, AI-powered summaries from both structured and open-text survey data. It uses techniques like regression, correlation, text analysis, and descriptive analytics to highlight key drivers, trends, and improvement opportunities. Each insight card provides you with a clear summary, data-backed findings, and next-step recommendations, and it gets refreshed every 24 hours.

  Insights are especially helpful if you're an executive, product lead, or CX manager looking to cut through large volumes of data and focus on what matters, without having to sift through raw metrics manually. Refer to this article for more details.

  

• Hypothesis Validation: Hypothesis Validation lets you compare survey responses with external social data to spot alignment, misalignment, or gaps in insights. Using Sprinklr’s Research Assistant, it helps you confirm whether your structured feedback truly reflects broader sentiment. Insights are grouped into three categories: aligned findings, contradictory data, and unique external insights not captured in your survey.

  This feature is perfect if you’re in CX, Product, or Marketing and want to validate assumptions, uncover feedback gaps, and base decisions on both solicited and unsolicited input. Hypothesis cards are generated automatically and updated every 7 days to match the slower refresh cycle of external data. Refer to this article for more details.

  

Business Use Cases

• Rapid Survey Creation: When you need to create a feedback survey quickly, an AI-powered builder helps you get it done in minutes. Just type a simple prompt, and it generates a ready-to-use survey with relevant questions, tone, and structure, so you can focus on listening to your customers, not building forms.

• Ensuring Feedback Relevance:If you're experiencing an overload of irrelevant or nonsensical answers in your surveys, the Survey Response Quality feature is here to help. It automatically eliminates low-quality responses, allowing you to concentrate on the feedback that genuinely counts, thereby providing cleaner data and more precise insights.

• Validating NPS Drivers with Social Chatter: If customer satisfaction scores drop, Hypothesis Validation lets you compare survey feedback with external data to uncover missed issues, revealing hidden concerns and enabling smarter decisions.

With AI-powered workflows, organizations can dramatically reduce the time required to create surveys and generate insights. Built-in evaluators help maintain high-quality responses and survey design standards, ensuring reliability at every stage. Leveraging LLMs and correlation models, teams can extract accurate, unbiased, and data-backed insights. Finally, by grounding internal feedback with external social sentiment, businesses can achieve stronger alignment across customer and employee perspectives.

Permissions

You must have access to the CFM Persona App inorder to access AI+ Capabilities.