Three reasons why Thematic is the best alternative to Medallia for leveraging customer feedback

If you’re looking for an alternative to Medallia’s built-in analytics, you’ve come to the right place. Here’s why Thematic is the best option for turning your customer feedback into accurate, actionable insights:

01.

Thematic’s theme taxonomy is more flexible and specific

With Medallia, you start with building your own topic model (topic list and structure) by creating rules. Sometime in the future, you may be able to train the AI with examples. Their approach works well for companies in industries where issues are likely to stay the same, but it’s time-consuming to set up. To make changes, you need to instruct a Medallia specialist to do this, which adds time and cost.

If you have a new or unfamiliar dataset, you can’t simply pipe it into Medallia for analysis - the set up steps have to be completed again. Once this is complete, new themes can be identified in your data.Thematic uses a unique bottom-up approach to discover all themes in text. The AI automatically builds a hierarchical code frame from text, which you can easily adjust to make more relevant. Themes are custom, industry specific, and can be refined on the spot. If you have a new dataset, you can connect it to Thematic and dive into the analysis with very little set up time.

02.

Thematic makes is easy to understand what your customers are saying - without reading thousands of comments

Medallia is great at organizing your data into themes (called topics in their tool) and indicating which issues have the highest volume. But it doesn’t provide an easy way of understanding each theme. Because findings are often generic, you have to read through your customer comments to understand what's going on, and why it matters.

In Thematic, it's easy to filter your feedback by what matters to you, so you can get specific, actionable insights at your fingertips. Even better, Thematic's LLM-powered Theme Summarizer feature provides an instant summary of any theme, so you can get the gist without having to trawl through all the comments.

03.

Thematic is easier to use, set up and administer - without the hefty price tag!

Because Medallia uses rule-based theme modeling it takes considerable time to get set up. And any required changes or customizations can’t be actioned directly. A Medallia specialist needs to configure any changes for you, which again takes time.

With Thematic, you’re in control. You’re up and running within days, and you can make changes to themes and applied sentiment without contacting support. If you do need help, your CS representative is there. Looking at comparisons between Medallia and Thematic on G2, reviewers found Thematic much easier to use, and preferred working with Thematic overall.

Medallia works with you to develop a custom package of products suited to your company’s needs. Text analytics are the core offering, but you’ll have significant costs for implementation and support in your package.

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.

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