CSATai is a proprietary AI model that measures satisfaction by analyzing the words customers use in context.
Get a satisfied, dissatisfied, or neutral CSAT score for every customer conversation. Eliminate survey bias and increase your sample size to 100%.
Use the customizable CSATai dashboard as your home base for monitoring satisfaction and tracking trends.
Pinpoint the top factors affecting customer satisfaction so you can make impactful changes in your contact center.
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.
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.
Customer satisfaction impacts your brand reputation, loyalty, and revenue. It’s far too important a metric to leave up to surveys with a 5-10% response rate.
“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, Thrasio
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.
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.
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.
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