Three reasons why Thematic is a better option than TextIQ for analyzing your customer feedback

Looking for an alternative to Qualtrics' TextIQ? Here’s why analysts and insights managers prefer Thematic for in-depth analysis and insights you can act on:

01.

Thematic provides deeper, more actionable insights

TextIQ provides a surface-level understanding of feedback through simple text analysis. It's an okay starting point, but deeper insights are often missed. And you have to manually set up any new topics that aren't covered by its industry specific pre-defined models. These limitations mean that TextIQ can't satisfy advanced analysis needs.

Thematic's powerful AI-driven software uses a unique bottom-up approach to discover all themes in text, and creates a taxonomy custom to your data. You can easily adjust it to make it more relevant. When new themes are discovered, Thematic alerts you, so you can review and decide whether to add them to your taxonomy. This means we provide meaningful and actionable answers to your questions - fast.

02.

Thematic's trusted results make it easy to defend your data and methodology

TextIQ lacks customization options that Thematic offers, and many users find its pre-defined topics miss the mark when it comes to understanding their feedback. If important issues are being overlooked, results are hard to trust - not what you want when analyzing data!

Thematic is the only solution that works with you to produce transparent and trusted results. It's incredibly easy to trace Thematic's AI-powered themes and summaries back to the source, so you can defend your data and methodology every time. This means your team can trust the insights Thematic delivers.

03.


Thematic offers seamless integrations and impactful visualizations

TextIQ primarily functions with Qualtrics dashboards, making it less compatible with other platforms, such as PowerBI or Tableau. It offers word clouds as a primary means of visualization, which look pretty, but don't deliver the clarity your team needs to make impactful decisions.

Thematic can be easily plugged into your existing analytics workflows, and you can pipe analyzed data to PowerBI or Tableau. Several one-click integrations are available, including one for Qualtrics surveys! Thematic's native reporting tools include dynamic waterfall and impact charts, to help you understand and share insights in a clear and concise manner. With Thematic, you won't struggle with confusing word clouds or challenging visualizations.

04.

Thematic compared to TextIQ and Discover/Clarabridge

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.

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