Three reasons why Thematic is the best alternative to Kapiche for accurate, in-depth text analytics

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:

01.

Thematic’s theme taxonomy is more flexible and specific

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.

02.

Thematic makes it easier to discover what matters most

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!

03.

Thematic offers more bang for your buck without user and support limitations

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.

04.

Thematic compared to other solutions

Key strength

How it arrives at themes

Speed

Consistency

Transparency

Ease of getting answers

Thematic

(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

Compared with
Rule-based

(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

LLMs

(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

Supervised learning

(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.

Rule-based

(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

LLMs

(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

Supervised learning

(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.

Doordash logo
Lending tree logoElectronic arts logo

Get started with a guided trial on your data

Complete the form to get in touch with one of our experts & see the Thematic platform in action.

  • Free trial on your dataset
  • Learn how Thematic can solve your specific pain points
  • Discover & review Thematic’s full functionality
Please enter a work email address