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Predict CSAT scores with
Tethr's CSATai. 

Tethr’s CSATai analyzes your contact center calls and chats to give you a complete view of the voice of the customer – and what your business can do to impact satisfaction.

  Get Predictive CSAT scores using an AI language model  Track CSAT & VOC trends in a customizable dashboard
  Get a satisfaction score on 100% of customer conversations Identify the top factors impacting
customer satisfaction 

Schedule a demo of Tethr's CSATai.

Trusted by leaders like you...

Say goodbye to low-response CSAT surveys.

AI IconCSATai is trained on millions of CSAT surveys and their related interactions, enabling it to predict satisfaction based on what your customers say.

9Get a positive, negative, or neutral CSAT score for every customer conversation. Eliminate survey bias and increase your sample size to 100%.
11Use the customizable CSATai dashboard to monitor satisfaction and track trends. Gain voice of the customer insights like never before.  12Pinpoint the top factors affecting customer satisfaction so you can make impactful changes in your contact center. 

Gain a deeper understanding of customer satisfaction.

CSATai uses machine learning to predict each customer’s satisfaction score based on words used in their voice or chat interactions, allowing your business to learn from the voice of every customer.

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Top Reasons Driving CSAT

Identify the factors impacting the customer experience.

Tethr lets you drill into conversations with negative or positive CSAT scores so you can uncover the factors that have the biggest influence on customer satisfaction and identify opportunities to improve the customer experience.

“Predictive CSAT uses data and past feedback to predict customer satisfaction levels. It helps the call center anticipate problems, offer personalized suggestions, and even anticipate returns or exchanges. This not only boosts the customer experience but also prevents negative reviews or returns, protecting the business's reputation and profits.”

-Arnel Deguito, Customer Experience Analyst 

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Why traditional CSAT surveys fall short

  • Only a small percentage of customers complete a survey after a service interaction.
  • Customers most likely to complete a survey are the ones who had either an extremely positive or negative experience.
  • The combination of low response rates and sample bias makes it difficult to gauge customer satisfaction accurately.
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Rethink CSAT

How CSATai fills the gaps

  • Tethr’s machine learning model measures satisfaction based on the words and phrases in customer conversations.
  • CSATai predicts a positive or negative CSAT score for every customer, not just those who complete surveys.
  • The model continues to learn as it ingests more conversation data, enabling it to deliver CSAT scores and insights with a high level of accuracy.

Frequently asked questions

How were the CSATai models trained?
Tethr’s CSATai models were trained on millions of customer survey results combined with their preceding interaction, either voice or chat. Using machine learning, the models capture the mathematical relationships between words and phrases in the conversation and the survey responses provided by the customer. The models are fine-tuned to ensure equal accuracy for both good and bad survey responses, thereby eliminating some of the affirmation bias present in the survey data. During the tuning process, the model parameters were varied and tested against untrained data to ensure they would generalize effectively to new conversations. While the test data is never used directly in training the model, tuning helps to identify information in the training set that is most important for capturing customer satisfaction. The resulting models are able to predict, with a high degree of accuracy, how a customer would most likely respond to a satisfaction survey.
How does CSATai determine satisfaction?

Tethr’s CSATai calculates customer satisfaction using a proprietary large language model (LLM) specifically tuned to match customer satisfaction surveys. The model identifies words and phrases within the full context of the customer conversation that are likely to indicate satisfaction or dissatisfaction. For example, a customer saying, “This is so frustrating” is likely to be dissatisfied. CSATai scores each conversation as positive, negative, or neutral based on all the text in the conversation in context.

Can I fine-tune CSATai to my business?

Yes–the more data CSATai ingests, the better it becomes at predicting CSAT scores for your customer conversations. If you disagree with how CSATai scores a conversation, you can provide feedback within the Tethr platform, which helps the model learn and improve.