2. Ascribe
Traditional tool used for coding open-ended responses, particularly by teams that are comfortable managing workflows manually.
Best for: Research teams that are working on smaller or lower-volume studies where hands-on management and structured frameworks are acceptable.
Pros
- Well known: Established presence in traditional market research environments.
- Structured frameworks: Supports predefined coding approaches.
- Smaller datasets: Can work for limited response volumes.
Cons
- Slower results: Longer turnaround compared to modern software.
- Manual setup: Requires ongoing configuration and maintenance.
- Time-consuming updates: Refining themes takes additional effort.
- Limited scalability: Not built for fast iteration or large-scale analysis.
- Dated interface: Less intuitive than newer platforms.
“Very not user-friendly. There's no good way to navigate and you find yourself having to go back and forth all the time just to do the simplest things.”
Source: G2
3. ATLAS.ti
A long-standing qualitative data analysis software focused on manual coding and academic research.
Best for: Qualitative projects that require extensive manual review and researchers who are trained in traditional qualitative methodologies.
Pros
- Detailed workflows: Strong support for manual coding.
- High-volume support: Can handle large datasets.
- Theme control: Full flexibility to edit and manage themes.
Cons
- Steep learning curve: Requires time to onboard and train.
- Slower at scale: Not ideal for large survey datasets.
- Manual effort: Significant hands-on work required.
- Outdated interface: Feels less modern for today’s research teams.
4. MAXQDA
A comprehensive qualitative and mixed-methods analysis platform.
Best for: Academic or institutional research projects and teams that combine qualitative and quantitative data within a single platform.
Pros
- Manual coding depth: Robust tools for detailed qualitative analysis.
- Research rigor: Built with academic and enterprise use cases in mind.
- Enterprise-ready: Designed to support large organizations with complex needs.
Cons
- Limited automation: Falls behind newer platforms focused on speed.
- High cost: Pricing can be steep for smaller teams.
- Complex workflows: Interface and setup take time to learn.
- Slower output: Not ideal for teams that need fast turnaround.
- Stability issues: Some users report glitches and functionality problems.
“There have been instances of the software freezing or slowing down, even on small datasets.”
Source: G2
5. Caplena
An AI-assisted text analytics tool focused on survey feedback.
Best for: Organizations analyzing customer feedback at scale and teams looking for semi-automated coding with a strong reporting focus.
Pros
- AI-supported coding: Assists with organizing large volumes of feedback.
- Reporting focus: Strong dashboards and visualization options.
- Scalable use cases: Suitable for ongoing customer feedback programs.
Cons
- Setup required: Themes need configuration and ongoing refinement.
- Partial automation: Coding often requires additional manual work to complete.
- Learning curve: Teams need time to adapt workflows and tune outputs.
6. Qualtrics (Text iQ)
An open-ended text analysis component within the Qualtrics survey ecosystem.
Best for: Teams already fully embedded in Qualtrics that need basic sentiment and theme analysis directly within their surveys.
Pros
- Native integration: Works directly inside Qualtrics surveys.
- Automated detection: Provides basic topic and sentiment analysis.
- Built-in dashboards: Quick summaries for themes and sentiment.
Cons
- Slow and labor-intensive: Requires a high level of manual work.
- Platform-dependent: Limited flexibility outside Qualtrics.
- Lower-quality coding: Relies heavily on keyword matching, which misses nuance.
- Outdated UI: Interface feels dated and can slow down workflows.
- Enterprise-level pricing: Requires larger budgets that may not fit smaller research teams
7. Canvs AI
Canvs AI specializes in analyzing open-ended responses with a strong focus on emotional and sentiment patterns.
Best for: Teams that want AI-assisted analysis across surveys, reviews, and social data, and are comfortable with higher-level outputs rather than detailed codeframes.
Pros
- Useful visualizations: Clear charts and summaries for exploration.
- CX and brand focus: Works well for customer experience and brand feedback studies.
- Emotion analysis: Offers nuanced emotion-level insights.
Cons
- Requires significant manual effort: While the tool applies AI to assign codes, many responses still go uncategorized or are coded inaccurately, requiring researchers to step in, review, and manually code a substantial portion of the data.
- Lower usability: Less intuitive for researchers seeking simple, rapid insights.
“Sometimes the codes are inaccurate and a lot of our verbatims were not assigned a code, so we had to manually code a good amount of the responses as well as make our own codes.”
Source: G2
8. Medallia
An enterprise customer experience platform with text analytics capabilities.
Best for: Large organizations tracking ongoing customer feedback and teams with dedicated coders who manage complex CX programs.
Pros
- CX-focused design: Built primarily for customer experience programs.
- Some automation: Includes automated features to assist with analysis.
- Feature-rich: Broad functionality for enterprise feedback workflows.
Cons
- Manual coding work required
- Complex interface: Can feel heavy and difficult to navigate.
- Training required: Setup and onboarding take time.
- Too much for small teams: Overkill for teams focused mainly on fast, straightforward coding.
9. Forsta
A market research platform offering survey creation, data collection, and analysis.
Best for: End-to-end research programs and organizations running large-scale tracking studies.
Pros
- All-in-one environment: Combines survey creation and analysis in a single platform.
- Clear dashboards: Easy to build and share visual summaries.
Cons
- Keyword-driven coding: Relies heavily on keyword-based logic, missing nuance.
- Constant refinement: Updating and adjusting themes slows down projects.
- Slow turnaround: Not optimized for fast, coding-focused workflows.
10. NVivo
A qualitative data analysis tool widely used in academic and institutional research.
Best for: Manual qualitative verbatim coding with detailed documentation and long-form qualitative research projects.
Pros
- Manual depth: Advanced structures for detailed qualitative coding.
- High-volume support: Some automation to assist with larger datasets.
Cons
- Slow at scale: Time-consuming for large datasets.
- Steep learning curve: Requires significant onboarding.
- Limited automation: Automated features are restricted.
- Speed constraints: Not designed for fast turnaround.
11. Codeit
A tool focused on coding and organizing open-ended responses, with an emphasis on structured categorization and review.
Best for: Researchers who prefer hands-on control over categories and code assignments rather than full automation.
Pros
- Insight summaries: Provides dashboards that summarize key findings.
- Assistive AI: Includes an AI Codeframe Builder to support, not replace, manual workflows.
Cons
- Limited automation: Not fully automated end-to-end.
- Manual review required: Ongoing human oversight is needed.
- Slower workflows: Takes longer than AI-native platforms.
- Learning curve: Less intuitive for new users.
