Text analytics with AI

Text analytics with AI turns your customers’ open-ended comments (text or transcribed voice notes) into structured, actionable information. It works on the responses from woku, NPS, CSAT and CES and combines three capabilities: automatic classification of each comment, keywords and summaries generated by AI, and discovery of emerging topics across the set of responses.

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Automatic classification of comments

The AI engine classifies each comment, whether written text or a transcribed voice note, into one of two categories:

• Recognition: the customer highlights something that worked well.
• Improvement: the customer points out something that can be fixed.

This classification is applied on its own as each response comes in, across the four tools (woku, NPS, CSAT and CES), and lets you immediately separate what your customers celebrate from what bothers them, and quantify the proportion of each one. To organize responses by your business attributes (branch, campaign, order, agent), you use external trackers, which travel attached to each response. The alerts notify you by email about the status of your goals and the response volume, and the escalation of negative feedback to customer support tickets is automatic.

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Sentiment

Each response falls into one of three sentiment levels, critical, neutral or positive, derived from the rating the customer left. These levels feed the analysis counts: each discovered topic shows its distribution of critical, neutral and positive responses, which lets you see at a glance whether a topic concentrates frustration or praise.

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Our own research

woku combines market-leading language models with its own research in sentiment analysis for Latin American Spanish, developed in collaboration with the PhD in AI of the University of Concepción (UdeC), the Federico Santa María Technical University (USM), the University of Bío-Bío (UBB) and the Catholic University of the Most Holy Conception (UCSC), the first PhD in Artificial Intelligence in Latin America.

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Keywords and summaries

Keywords are extracted automatically from each piece of feedback. These words feed the word cloud in the woku, folder and NPS reports, which also include an AI-generated summary and suggested actions based on what customers say. CSAT and CES have their own AI Summary, which synthesizes in natural language the responses of each measurement.

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Emerging topics

Beyond the categories you already know, woku’s semantic engine groups responses by meaning and discovers emerging topics that were not defined in advance, with exact counts: each figure corresponds to real responses you can review. Topics are explored in the reviews space, a 3D universe where each response is a point and responses that talk about the same thing end up close together. Selecting a topic opens a panel with:

• Label and summary of the topic.
• Number of responses that make it up.
• Sentiment distribution (critical, neutral, positive).
• Trend and first and last appearance over time.
• Evidence: the real responses that support the topic.

The analysis is unified: a single topic can bring together responses from woku, NPS, CSAT and CES.

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Where it is displayed

Text analytics results appear in several places across the platform:

• woku, folder and NPS reports: word cloud, AI summary and suggested actions.
• Report builder: the feedback type (recognition or improvement) is available as a dimension to cross it with other metrics. See the report builder.
• Reviews space: visual exploration of the emerging topics and their sentiment distribution.
• Data Studio: qualitative questions, such as what my customers are talking about, are also answered with reports whose numerical evidence is verified against the data. See Data Studio.