Accurate and actionable insights: Why Thematic is a superior alternative to Qualtrics XM Discover

Looking for an alternative to Qualtrics XM Discover (formerly Clarabridge)? Thematic is the answer. Here’s why VoC and insights managers prefer to use Thematic to analyze customer feedback at scale:

01.

It’s easier to discover themes and get actionable answers in Thematic

Despite the name, Discover does not discover things for you. In Discover, just like in TextIQ, you have to manually set up any new topics that aren't covered by its industry specific predefined models. Because the models use predefined dictionaries based on keywords, they miss unexpected themes in the data. Changes to the model are time-consuming and rely on support from Qualtrics.

But discovery is part of Thematic's DNA! Powered by AI, it 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. As you add more data, Thematic also keeps your taxonomy consistent. 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 a lot faster.

02.

It’s much faster to get set up and start analyzing with Thematic

Because Thematic's AI automatically discovers the unique themes in your feedback, you can be up and running within days. There’s no need for pre-coding or training, and you can be confident your insights are relevant to your business.

With XM Discover, if your dataset closely matches one of the taxonomies they offer, it will take a couple of weeks to get started. However, an implementation that requires a lot of customization can take up to six months. This typically involves a XM Discover expert creating complex rules to capture themes of interest in close collaboration with business stakeholders.

03.

It’s easier to understand and share data in Thematic

Thematic is designed to be easy for anyone at your company to use, not just insights specialists and data scientists. This makes it possible for everyone to quickly understand data. Product teams can filter data by issues, questions and feature requests to know where to focus and what to build next. Marketing teams can laser in on comments that mention recent ad campaigns. Customer support can spot the common (and not so common) bottleneck issues consuming valuable time.

Permissions can be set at the team or user level, so that everyone can dive in at the level they need and view relevant data. You can also set up workflows to send notifications based on specific themes or changes in your data. The right people receive the right information in real time so they can act swiftly.

XM Discover’s capabilities for text analytics are great, but the user interface can be challenging to navigate, especially for new users. You’ll also need to have staff regularly using the platform to stay on top of ongoing maintenance.

Automations and workflows for XM Discover are tied into the Qualtrics suite. Unless all teams are using the Qualtrics-related product for their niche information can’t be easily disseminated.

04.

Thematic is more affordable without compromising on quality

XM Discover has always targeted Fortune 100 companies, with a price tag to match. Do you have budget of >$250K or more? If not, they won’t give you much attention.

If you’re dealing with feedback at scale then Thematic is the best option, offering Enterprise level text analytics that’s affordable.

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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