If you’re looking for alternatives to Kapiche to help you understand your customers, you’re in the right place! Here’s why Thematic is the best solution to turn your feedback overload into actionable insights:
Unlike other solutions, Kapiche doesn’t use code frames or taxonomies at all. They discover prominent words in text and then use contextual links between these words for users to discover themes of interest.
This makes for fast analysis, but makes it harder to dig into your data and understand what’s most important and why. Instead of themes, the most frequently mentioned keywords are presented in a word cloud format, with lines connecting them to related words.
Thematic uses a unique bottom-up approach to discover all themes in text. We automatically build a hierarchical code frame from text, and you can easily adjust it to make it more relevant. Themes are custom, industry specific, and can be refined quickly and easily. This means you get the best of both worlds: analysis is specific to your dataset, occurs in real time, and is organized in a way that makes it easy to understand exactly what’s happening and changing in your data.
With Kapiche, you need to build out queries to better understand the data. It takes time to learn to create queries that are useful, and the available filters are limited. On the surface, Kapiche is very much plug and play, but this applies mostly to surface insights. To achieve sophisticated results takes considerable skill and support.
Thematic has several native features that make it easy to pull insights from your data, no matter your skill level. An example is our Theme Summarizer feature, which is powered by Generative AI and provides straightforward, accurate summaries of what your customers are saying. Large Language Models are a huge leap forward in AI, and Thematic is committed to securely embedding this new tech throughout the platform.
Need to slice and dice your data in multiple ways? Thematic's filters are fast and easy to use. Filter by date, score, impact, theme, sentiment, demographic data, or comments containing certain keywords - to name just a few! Filters have universal search enabled, so it's easy to find what you need. Use the comparison filter to pit different segments against each other and make new discoveries!
Unless you’re an Enterprise customer with a custom pricing plan, Kapiche puts strict limits in place - and yet it still costs more than Thematic’s entry level plan. Support is limited to live chat only and projects are restricted to ten columns of data.
All Thematic customers have access to ongoing support, and plans are set by the number of feedback comments, which makes it far more flexible and applicable to more project types. Overall, Thematic is more budget friendly, and delivers higher quality analytics and insights.
Key strength
How it arrives at themes
Speed
Consistency
Transparency
Ease of getting answers
(combines proprietary AI with LLMs)
Key strength
Discovers & quantifies what matters in minutes with readable summaries
How it arrives at themes
Discovers themes in data and adapts to new feedback
Speed
It takes 30 min to several hours to review themes
Consistency
Analyzes all feedback consistently
Transparency
White box, can trace themes to context
Ease of getting answers
Alerts you to new themes and delivers instant, readable and verifiable anwers
(like Medallia, Qualtrics Text IQ and XM Discover)
Key strength
Works well for industries with little changes in feedback, e.g. hotels
How it arrives at themes
Users create rules or professional services adjust pre-existing taxonomies
Speed
It takes weeks to months to create new rules or to re-configure a pre-canned taxonomy
Consistency
Analyzes all feedback consistently
Transparency
White box, can trace topic to rules
Ease of getting answers
Good at quantifying known and well-defined rules, struggles with complex language and unknowns
(and wrappers, like Viable, Interpret, Kraftful)
Key strength
Creates readable summaries of feedback, understands nuance
How it arrives at themes
Discovers theme in batches of data
Speed
Instant sample analysis
Consistency
Consistency is difficult to achieve because model guesses each time
Transparency
Black box, requires prompt engineering to trace
Ease of getting answers
Delivers instant readable answers that are difficult to verify
(like Chattermill, Medallia Athena)
Key strength
Scalable and accurate when using a limited set of categories it’s trained on
How it arrives at themes
Users or professional services curate examples (training data) for each category
Speed
It takes weeks to months to curate data
Consistency
Analyzes all feedback consistently
Transparency
Black box, difficult to adjust a model that’s inaccurate
Ease of getting answers
Good at quantifying known and well-defined rules, but will miss any emerging themes.
(like Medallia, Qualtrics Text IQ and XM Discover)
Key strength
Works well for industries with little changes in feedback, e.g. hotels
How it arrives at themes
Users create rules or professional services adjust pre-existing taxonomies
Speed
It takes weeks to months to create new rules or to re-configure a pre-canned taxonomy
Consistency
Analyzes all feedback consistently
Transparency
White box, can trace topic to rules
Ease of getting answers
Good at quantifying known and well-defined rules, struggles with complex language and unknowns
(and wrappers, like Viable, Interpret, Kraftful)
Key strength
Creates readable summaries of feedback, understands nuance
How it arrives at themes
Discovers theme in batches of data
Speed
Instant sample analysis
Consistency
Consistency is difficult to achieve because model guesses each time
Transparency
Black box, requires prompt engineering to trace
Ease of getting answers
Delivers instant readable answers that are difficult to verify
(like Chattermill, Medallia Athena)
Key strength
Scalable and accurate when using a limited set of categories it’s trained on
How it arrives at themes
Users or professional services curate examples (training data) for each category
Speed
It takes weeks to months to curate data
Consistency
Analyzes all feedback consistently
Transparency
Black box, difficult to adjust a model that’s inaccurate
Ease of getting answers
Good at quantifying known and well-defined rules, but will miss any emerging themes.
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