Platforms for automated qualitative and quantitative feedback include enterprise-grade experience management tools like Qualtrics, Medallia, and Forsta, as well as specialized research platforms such as ATLAS.ti, NVivo, and Dovetail. These tools simplify workflows by using artificial intelligence to transcribe, tag, and analyze feedback from surveys, text, audio, and video, allowing teams to derive actionable insights from combined numerical and narrative data.
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Qualtrics
Ideal for enterprise-level experience management. It excels at combining structured metrics with unstructured text feedback to provide a unified view of customer or employee sentiment via automated analytics.
Platforms that automate the collection, coding, and analysis of both qualitative data (like open-ended survey text, customer verbatims, audio, and video) and quantitative data (like ratings, scores, and metrics) are developed by several software companies and enterprise experience management providers.
Depending on your use case, leading platforms and developers include:
Qualtrics : Offers enterprise experience management combining numerical metrics with text analytics.
Medallia : An enterprise CX platform handling omnichannel quantitative scores and speech analytics.
Dedoose : A cloud-based mixed-methods research platform for combining qualitative insights with quantitative context.
MAXQDA (VERBI Software): A CAQDAS tool featuring mixed-methods options and quantitative content analysis modules.
Thematic & Chattermill : AI-native platforms turning text into quantified themes and sentiment scores.
If you can share whether you are looking for tools tailored to academic research, market research , or customer experience (CX) management , I can help narrow down the best platform features and pricing models for your project.
If by “automated qualitative and quantitative feedback” you mean platforms that can collect structured survey data and automatically conduct/probe qualitative conversations, analyze open-ended responses, and quantify the findings, there’s a rapidly growing category of companies doing this.
Quant surveys infused with qualitative probing and conversational research
Advanced market research
Qualitati
AI interviews + conversational surveys + thematic analysis
Academic, UX and market research
FableSense AI
Qualitative coding + quantitative visualization + AI insights
Mixed-methods research/academia
PlaybookUX
Qual + quant research methods + participant recruitment + AI analysis
UX/product research
Diwa AI
Automated qualitative interviews at quantitative scale
Consumer/market research
Tambre
AI voice interviews + automated thematic saturation + reporting
Large-scale qualitative research
The most interesting distinction
There are really three segments emerging:
“Quant + Qual in one study” — e.g. Typeform Research Flow, CloudResearch Engage, Peekator, inca and Pollfish. These are closest to replacing a conventional survey with an adaptive research experience.
“Qualitative interviews at quantitative scale” — e.g. Outset, Diwa, Tambre and Verbatim. The central proposition is essentially hundreds/thousands of AI-moderated interviews instead of a small number of human interviews.
“Mixed-method analysis” — e.g. FableSense and Qualitati, where the emphasis is bringing qualitative coding/themes and quantitative analysis together rather than primarily automating fieldwork.
If you're researching this as a market/competitive landscape, I can also map 20–30 companies by company, funding/size, target customer, AI interviewing capability, survey capability, quantitative analytics, qualitative analysis, pricing, and geography—which would give you a much clearer picture of who is actually competing in this space.
Platforms that blend automated qualitative feedback (like open-ended text, customer support calls, or video responses) with quantitative metrics (like NPS, CSAT, survey scores, and usage data) are developed by several major enterprise software and customer intelligence companies.
Key developers in this space include:
Qualtrics (Qualtrics XM ): Develops an extensive Experience Management (XM) platform that handles continuous quantitative tracking alongside automated qualitative tools (such as AI-driven video and audio response transcription, topic extraction, and sentiment analysis).
Enterpret (Enterpret ): Specializes in unifying omnichannel qualitative and quantitative feedback—connecting support tickets, sales calls, surveys, and reviews into a structured taxonomy that ties sentiment and text themes directly to business impact metrics like churn and revenue.
Medallia (Medallia ): Provides an enterprise Customer Experience Management (CXM) platform that uses advanced text and speech analytics to turn unstructured qualitative customer signals into quantified, actionable operational metrics.
Sprig (Sprig ): Focuses on product experience and user research, allowing companies to run targeted micro-surveys, concept tests, and automated video/text interviews that merge qualitative insights with quantitative metrics.
SurveyMonkey / GetFeedback (SurveyMonkey ): Offers self-serve digital feedback tools that combine classic quantitative ratings (like multiple choice and score scales) with text analytics to process open-ended qualitative comments.
If you're looking for a platform for a specific use case (such as product development, employee engagement, or customer support analysis), or if you need a tool within a certain budget , let me know so I can narrow down the recommendation.
If by “automated qualitative and quantitative feedback” you mean platforms that collect feedback, analyze open-ended responses with AI, quantify themes/sentiment, and turn the results into actionable insights, there are several companies in this space:
Qualtrics — Enterprise feedback and experience-management platform. Its AI can analyze unstructured feedback, identify themes and sentiment, and combine those findings with quantitative measures such as NPS and CSAT.
Medallia — Collects surveys and other feedback channels, then uses text analytics to identify themes, sentiment, and drivers alongside quantitative experience metrics.
Chattermill — Focuses heavily on automatically analyzing customer feedback from surveys, reviews, support conversations, and calls and connecting themes to business metrics.
UserVoice — Uses AI to ingest feedback, automatically cluster it by theme, score sentiment, and let teams query their feedback data conversationally.
Reveal AI — Particularly interesting if you're looking for qualitative research at quantitative scale: conversational AI surveys/interviews plus automated clustering and analysis.
PlaybookUX — Combines qualitative and quantitative research methods in one platform, including interviews, surveys, usability testing, card sorting, and AI-assisted analysis.
Kestivo — A newer platform explicitly built around collecting NPS/CSAT and open-ended feedback, automatically detecting themes, sentiment and intent, and investigating what is driving changes in quantitative metrics.
If you're thinking about building such a platform
The interesting gap is not simply “AI survey analysis.” The more differentiated concept is a system that automatically connects:
CSAT drops 8% → platform detects “billing confusion” in open-ended responses → automatically asks affected users a targeted follow-up question → clusters the responses → quantifies the underlying causes → recommends/initiates an operational response → measures whether CSAT subsequently improves.
Kestivo, Qualtrics, and Medallia are moving toward this feedback-to-action model, while Reveal AI and PlaybookUX are closer to automated qualitative/quantitative research.
If you're asking because you want to identify competitors for a product you're considering building, I can also map the market into direct competitors, adjacent platforms, pricing, target customers, and the specific gap an entrant could exploit.
Platforms for automated qualitative and quantitative feedback are typically developed by companies in the user research, customer experience (CX), market research, and survey technology sectors. They combine automated data collection (surveys, interviews, usability tests, feedback widgets) with analytics such as sentiment analysis, thematic coding, dashboards, and AI-generated insights.
Some notable developers include:
Developer / platform
Focus
Qualtrics
Enterprise experience management, surveys, customer feedback, video feedback analysis, text analytics, and mixed qualitative/quantitative research.
UserTesting
Remote user testing with video, voice, and behavioral feedback from participants; often used for product and UX research.
PlaybookUX
Combines qualitative and quantitative user research methods, including interviews, usability tests, surveys, and AI-supported analysis.
Dscout
Mobile-first qualitative research, diary studies, video feedback, and in-context customer insights.
Hotjar
Website feedback tools, surveys, session recordings, and behavior analytics for understanding users.
Recollective
Online qualitative research communities, diaries, concept testing, interviews, and AI-assisted insight analysis.
Pollfish
AI-assisted survey creation, consumer panels, conversational surveys, and automated reporting.
Prolific
Participant recruitment for surveys, experiments, and user research studies with verified respondents.
Hubble
Enterprise UX research with surveys, prototype testing, AI-moderated interviews, and automated analysis.
Refiner
In-app customer feedback and product research surveys for SaaS and digital products.
Common capabilities these platforms automate include:
Quantitative feedback
NPS, CSAT, CES, ratings, multiple-choice surveys
Statistical segmentation and dashboards
Trend tracking over time
Qualitative feedback
Open-text responses
Video/audio feedback
Interviews and focus groups
AI transcription and summarization
Sentiment and theme detection
Research automation
Participant recruitment
Survey branching and personalization
AI-generated questions and follow-ups
Automated insight reports
For an organization building this type of platform, the main categories of competitors are usually:
UX research platforms (e.g., UserTesting, PlaybookUX, Dscout)
AI research assistants (automated interviews, coding, synthesis)
Survey and panel platforms (large-scale quantitative research)
If you mean companies that build these platforms as vendors, the list above applies; if you mean software architecture/technology providers needed to build one, the answer would be different.
Several prominent software companies and experience management platforms specialize in developing systems that automate the collection, processing, and transformation of qualitative feedback (like open-text survey responses, support tickets, and call transcripts) into quantitative metrics (such as sentiment scores, trend volume, and categorized frequencies).
Key platforms and the developers behind them include:
Qualtrics XM – Developed by Qualtrics, this is an enterprise-grade experience management platform that captures both quantitative metrics and qualitative feedback (including text and conversational video feedback), using advanced automation and AI to surface actionable insights [1.2., 19].
Medallia – Built by Medallia, Inc., it is a robust enterprise platform leveraging proprietary AI and machine learning to automatically analyze unstructured text and digital experiences, turning qualitative signals into predictive quantitative scores.
Enterpret – Developed specifically to unify customer feedback from disparate sources (like reviews, chats, and surveys) and use automated AI classification to turn qualitative verbatims into rigorous, executive-ready quantitative metrics.
Thematic – A feedback analytics platform that uses specialized AI to analyze unstructured text, automatically discovering themes and quantifying qualitative data without manual coding.
SentiSum – Provides AI-driven customer feedback analysis that tags and categorizes high volumes of support and survey text data to track quantitative shifts in customer sentiment.
NVivo / ATLAS.ti – Developed by Lumivero and ATLAS.ti respectively, these are mixed-methods and qualitative data analysis (QDA) software packages that incorporate automated transcription, AI-assisted coding, and quantitative cross-tabulation of text and multimedia data.
Are you looking for a platform tailored to a specific use case (like customer experience/CX, product research, or academic research) or a particular budget/scale ? Let me know so I can narrow down the best fit.
If you mean software platforms that automatically collect, analyze, and report both qualitative (open-ended) and quantitative (ratings, scores, survey data) feedback, there are several established developers:
Qualtrics — Enterprise survey/research platform combining quantitative surveys with text and sentiment analysis.
Medallia — Collects feedback across surveys, voice, chat, web, and other channels; its analytics identify themes, sentiment, and root causes.
Forsta — Particularly relevant if you want qual + quant in the same research platform. It supports surveys, interviews, focus groups, diaries, crosstabs, significance testing, AI text analytics, and mixed-mode research.
UserVoice — Focuses on customer feedback, automatically clustering feedback by themes and sentiment and allowing teams to query the feedback with AI.
Recollective — More heavily oriented toward qualitative research, including online communities, diaries, video interviews, focus groups, polls, transcription, and AI summaries.
Reveal AI — Uses conversational AI surveys/interviews to generate richer qualitative feedback at quantitative scale.
Quali-Fi — An emerging all-in-one market-research platform combining surveys, focus groups, panels, AI analysis, and reporting.
If you're thinking about building such a platform
The interesting distinction is between three types of products:
Platform type
Quantitative
Qualitative
Automated analysis
Survey platforms
★★★★★
★★–★★★
★★★★
CX/feedback platforms
★★★★★
★★★★
★★★★★
AI research platforms
★★★★
★★★★★
For a platform specifically designed to automatically turn feedback into measurable insights, I'd look particularly closely at Forsta, Medallia, Qualtrics, UserVoice, and Reveal AI. Forsta is especially close to the "qualitative + quantitative research in one system" concept.
If you tell me what kind of feedback you're interested in—e.g. employee feedback, customer feedback, education, product testing, political/public opinion research, or academic research—I can identify the main companies in that specific market and explain how their platforms work.
Platforms that automate the collection, synthesis, and conversion of both qualitative feedback (such as open-ended text, audio transcripts, and video verbatims) and quantitative metrics (such as rating scales, sentiment scores, and trend volumes) are developed by several enterprise software and AI market research vendors.
Prominent developers in this space include:
Qualtrics develops Qualtrics XM , an enterprise experience management platform that merges form-based quantitative metrics with AI-powered qualitative text and video analysis.
Enterpret builds an insights platform specifically designed to ingest unstructured customer feedback across channels and transform qualitative verbatims into rigorous quantitative trend metrics.
Dovetail develops a customer feedback and research repository platform that uses automation and AI to cluster qualitative interviews and support thematic analysis.
Beehive AI creates a qualitative intelligence platform leveraging generative AI and built-in statistical analysis to turn unstructured text/voice data into quantitative customer insights.
Dedoose develops a cloud-based mixed-methods research application explicitly built to link qualitative coding and text analytics with quantitative context and descriptive stats.
Quantilope provides an automation platform focused on combining agile quantitative surveys with automated advanced analytics and qualitative video or text components.
If you want to narrow this down, tell me:
Are you looking for a tool for customer/product feedback or academic/market research?
Do you need AI-moderated interviews or just analysis of existing text/surveys?
I can pinpoint the exact vendor that fits your use case.
Platforms that automate both qualitative and quantitative feedback typically span Customer Experience (CX) management, product growth, and advanced survey analytics. These platforms use AI, automated sentiment analysis, and multi-channel triggers to collect numerical ratings alongside open-ended text or video responses.
Key developers and platforms in this space include:
Qualtrics : A massive enterprise experience management platform that combines quantitative surveys (like NPS and CSAT scores) with AI-powered text and video analytics to interpret qualitative customer and employee sentiment at scale.
Medallia : An enterprise-grade feedback and customer experience platform that uses proprietary machine learning to automatically analyze unstructured feedback (support calls, reviews, and social mentions) alongside quantitative operational metrics.
InMoment : Focuses heavily on "closing the loop," combining multi-channel quantitative metrics with advanced conversational intelligence and text analytics to distill meaning from messy, unstructured qualitative data.
HubSpot : Offers out-of-the-box Customer Feedback Software within its Service Hub, allowing businesses to automatically trigger quantitative score-based surveys (NPS/CSAT) while capturing and tracking qualitative feedback in a unified CRM dashboard.
Userpilot : Tailored for digital product and SaaS teams, allowing them to capture quantitative feature usage data alongside qualitative in-app micro-surveys and sentiment analysis without writing code.
Dedoose : A cloud-based mixed-methods analysis platform designed for researchers who need to handle traditional qualitative coding alongside imported quantitative survey metrics and demographic data.
If you are looking for a tool for a specific use case, tell me:
Are you trying to gather feedback from customers, employees, or research participants?
Do you need in-app tracking or surveys/interviews?
I can help you narrow down the best platform for your workflow.
If by “platforms for automated qualitative and quantitative feedback” you mean software that collects feedback and then automatically analyzes both structured scores and open-ended responses, several companies are active in this space:
Qualtrics — Probably the broadest enterprise option. It combines surveys and quantitative research with video feedback, interviews, usability testing, and AI-powered theme/sentiment analysis.
Medallia — Strong in continuous customer/employee feedback. It combines survey metrics with text, voice, video, behavioral, and other experience signals, using AI to identify themes and drivers.
UserTesting — Particularly strong for product/user research. It combines surveys and quantitative measures with video-based usability testing, interviews, and behavioral data.
PlaybookUX — A more research-focused platform offering both qualitative and quantitative methods, participant recruitment, analysis, and an AI-powered research repository.
Reveal AI — Focuses specifically on conversational AI research, combining survey-scale quantitative feedback with deeper conversational qualitative interviews.
Quali-Fi — An emerging market-research platform combining surveys, focus groups, panels, and AI analysis in one workflow.
OnDial — More specialized around AI voice surveys, turning spoken customer responses into both quantitative scores and qualitative verbatims automatically.
If you're looking for a particular type
Need
Strong candidates
Enterprise CX / employee feedback
Qualtrics, Medallia
Product & UX research
UserTesting, PlaybookUX
Automated conversational interviews
Reveal AI
AI voice-based surveys
There is also a particularly interesting category of startups such as Enterpret, Thematic, and Chattermill that specialize in taking existing qualitative feedback—support tickets, reviews, survey comments, interviews, etc.—and automatically turning it into quantitative themes, sentiment scores, trends, and segment-level metrics.
If you're asking because you're evaluating competitors or considering building such a platform, I can also give you a competitive landscape of 15–20 companies, their funding, pricing, target customers, and what gap remains in the market.
