Text analytics platforms rely on Natural Language Processing (NLP) to make sense of unstructured data captured from a range of sources: verbatim comments in surveys, reviews posted in forums, posts in social media, comments transcribed in call centres etc. Key features include sentiment; categorisation or topic analysis; entity extraction (eg to identify brands); and visualisation - usually in an online dashboard. Text analytics capabilities are also built into some platforms in other categories (Social Listening, Surveys).
Caplena is online text analysis software for coding open-end responses to surveys.
The tool enables a researcher to code responses or categorise text using a streamlined interface. The intelligent assistant continually learns how to predict codes for responses based on previous examples, and improves its effectiveness over time.
Relative Insight is a language analytics platform.
Using NLP to conduct comparative analysis of two different text-based data sets (eg competing websites), it identifies significant similarities and differences in word choice, sentiment and grammar.
It can be used to refine communication briefs; narrow the gap between consumer and brand language; or to enhance existing social listening, text anaytics and customer data platforms.
The solution can be accessed on a software-only basis, or as a managed service.
Luminoso is a CX analytics platform for identifying themes in any open-ended text data – support tickets, open-ended survey responses or reviews.
Using artificial intelligence and natural language understanding, the platform automatically analyses feedback to discover hot topics, identify complaints and track trends over time.
Scope AI is an NLP based analytics platform that analyses customer feedback from any channel (social, CRM, complaints, surveys, reviews); combines it with user and product data (through Segment, Mixpanel or other API feed); and identifies insights for product development or customer experience improvement.
CodeIt is an automated support tool for coding any unstructured text. Designed by market research and coding professionals, the technology helps enhance coding productivity and quality using a blend of advanced artificial intelligence, machine learning and text analytics techniques.
Applications include market research verbatim responses; customer reviews; voice of the customer comments; customer satisfaction comments; and customer complaints.
Canvs analyses and categorizes “Emotional Reactions” in dialogue. The technology is embedded in a range of products with specific focus areas: TV (understanding audiences); Surveys (processing open-ended responses); Watch (focused on Facebook video); Campaigns (for analysing advertising and branded content campaigns); Movies (more audience insight, with predictive analytics tying emotional responses to box office sales forecasts); and Publishers (for analysing reader feedback.
An API is also available for custom integrations.
ipiphany applies text analytics to unstructured Customer Experience data (from social media, call transcripts, surveys, email, chat conversations and CRM).
Analysis and dashboard features include a key drivers tool to understand movements in NPS, CSAT and Customer Effort scores; a relationships tool to find patterns connecting different topics; a trends tool to know why patterns have changed over time (e.g. “Why has my NPS declined?”); and a comparison tool for contrasting two or more sub-groups (teams, competitors, segments).
Smartcoder provides automatic analysis of answers to open-ended survey questions. The tool performs topic analytics and automatically generates a list of categories (codes) and assigns appropriate codes to answers. The self-service platform involves 4 steps: uploading survey responses; automatic analysis; manual refinement; and download of results.
VOZIQ applies text analytics to “voice of customer” data sources to predict churn risk, identify drivers of dissatisfaction and understand high-value customers.
Wonderflow applies text analytics to customer experience feedback data. Sentiment analysis tools determine what customers like or dislike; a proprietary “Relevance” algorithm reveals what drives customers to buy products; and trends tools monitor price and rating fluctuations.
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Text analytics platforms rely on Natural Language Processing (NLP) to make sense of unstructured data captured from a range of sources: verbatim comments in surveys, reviews posted in forums, posts in social media, comments transcribed in call centres etc. Key features include sentiment; categorisation or topic analysis; entity extraction (eg to identify brands); and visualisation - usually in an online dashboard. Text analytics capabilities are also built into some platforms in other categories (Social Listening, Surveys).
