Thematic vs Competitors

Why Thematic is different

Thematic is a truly new approach that combines the advances in Deep Learning with state of the art concept extraction research. Here is how we solve common issues of competitive solutions:

Competitor Issues Thematic’s solution
Reading samples
  • Difficult to cut through the noise
  • Difficult to make sense of patterns
  • Prone to bias
  • Thematic provides a complete analysis
  • Thematic detects patterns between related themes
Manual coding
  • Time-consuming and expensive
  • Focus on high-level only
  • Prone to bias
  • Thematic can code survey comments in real-time
  • Thematic provides in-depth anaysis
  • Thematic removes the bias
Word clouds like Data Cracker, Qualtrics, Cloudcherry and Keatext Sift, Etuma and many more
  • Difficult to interpret
  • Some are unable to capture multi-word concepts
  • Most are noisy
  • Thematic extracts themes that are easy to interpret
  • Thematic captures similar multi-word concepts
  • Thematic does not return noise
Supervised categorization like Medallia, Maritz CX and Clarabridge, Meaningcloud
  • Time-consuming set up that requires annotated data
  • Focus on high-level themes
  • A difficult to maintain black box solution
  • Thematic just needs raw data
  • Thematic detects specific themes
  • Thematic is transparent and results can be easily adjusted
Rules-based systems like SPSS and Medallia
  • Time-consuming set up requiring thinking up rules
  • Manual input required to capture emerging themes
  • Thematic just needs raw data
  • Thematic captures emerging themes
Word relationship extractors like Leximancer and WordyUp Difficult to interpret Thematic extracts multi-word concepts that are easy to interpret
Topic modeling, LDA approaches, a go-to tool by many data scientists
  • Difficult to interpret
  • Black box technology
  • It’s a technology, not a solution
  • Thematic’s themes and visualisations are easily understood
  • Thematic is transparent and results can be easily adjusted