The Predicted Rating and Predicted CSAT scores, two metrics in the suite of advanced analytics known as Dimensions, are used to quantify customer experience. These scores are derived from the raw text of conversational data to provide scores without the need for surveys. As a result, predicted scores may serves as a proxy for direct customer feedback, enabling continuous monitoring across a wider range of customer feedback channels.

Predicted Rating

The "Predicted Rating" in Dashbot is part of a suite of analytics known as dimensions.

It assigns a rating from 1-10 that a customer might give based on the analysis of their conversation. It's used for gauging customer experience.

👉 Examples : A conversation with where the customer’s issue was resolved might be assigned a high predicted rating, indicating a good customer service experience.

Understanding the Predicted Rating Dimension

The Predicted Rating analyzes the content and context of conversations using dashbot's AI models. The system is able to predict a rating that reflects the customer’s feeling about the conversation. This rating may be compared with scores like NPS.

Calculation of Predicted Rating

The Predicted Rating is calculated at the session-level, analyzing the full text of a conversation to predict how a users would rate their experience on a scale, from 1 to 10, with 10 being the highest. The overall Predicted Rating Score is then averaged across all sessions.

💡 Prompt: An estimation of the success in handling the user's requests, rated on a scale of 1 to 10 (with 10 being the best).

Implications of Predicted Rating

The Predicted Rating offers valuable insights into customer experience. It helps in understanding how customers perceive their interactions and the quality of service they receive. This can be especially useful in scenarios where explicit customer feedback (like survey responses) is limited or unavailable.

Using Predicted Rating for Improvement

This score can be used to identify areas of concern ranging from agent performance to customer frustration with various products and services. The Predicted Rating serves as a proxy for direct customer feedback, offering a continuous stream of insights derived from everyday customer interactions.

Predicted CSAT

The "Predicted Rating" in Dashbot is part of a suite of analytics known as dimensions.

It assigns a satisfaction score between -2 and +2 (Very/Somewhat Dissatisfied, Neither, Somewhat/Very Satisfied) that a customer might give based on the analysis of their conversation.

👉 Examples: A conversation with where the customer was provided with prompt service might be assigned a high Predicted CSAT rating.

Understanding the Predicted Rating Dimension:

Predicted CSAT is calculated by assigning a satisfaction rating to every interaction or piece of content, regardless of its source. Because the score does not rely on customer surveys it may provide a holistic view of satisfaction that extend VoC analytics across more data sources.

Calculation of Predicted Rating

The Predicted CSAT is calculated at the session-level, analyzing the full text of a conversation to predict how a users would rate their satisfaction: very satisfied (+2), somewhat satisfied (+1), neither satisfied or dissatisfied (0), somewhat dissatisfied (-1), very dissatisfied (-2). The overall Predicted CSAT Score is then averaged across all sessions.

💡 Prompt: An estimation of the user's satisfaction, rated on a scale of -2 to +2 (with +2 being very satisfied, -2 being very dissatisfied, and 0 being neither satisfied or dissatisfied).
Lense: Session

Implications of Predicted Rating

The Predicted CSAT score provides businesses with insights that would traditionally require extensive survey research, but without the associated limitations like low response rates. This metric is particularly valuable in scenarios where direct customer feedback is limited or challenging to obtain.

Using Predicted Rating for Improvement

This score is instrumental in identifying areas for customer experience enhancement. By analyzing reasons and categories derived from customer feedback, businesses can pinpoint specific aspects of their service that impact customer satisfaction.