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Conversation dashboard

Explore the conversations dashboard for key metrics tied to conversations. The default range filter allows you to view metrics for a specific timeframe. Additional filters can be added to obtain more specific analytics.

  • Resolution rate: Provides information about the percentage of conversations resolved within the selected period. This metric is crucial as it enables the team's ability to address user concerns. Monitoring this metric is essential for maintaining high customer satisfaction, optimizing support workflows, and identifying areas for improvement in conversation resolution processes.

  • SLA compliance rate: Provides information about the percentage of conversations that comply with the SLA policy out of all the conversations to which the SLA policy was applied. Further drilling through helps identify the conversations that breached the SLA and a more in-depth investigation of possible root causes can be conducted.

  • CSAT score: Provides information about the percentage of positive survey responses out of all survey responses received. It helps identify the overall percentage of satisfied customers with the conversation resolution experience. Further drilling helps to pinpoint the conversations contributing to the CSAT score calculation.

  • Median first response time: Signifies the middle value in the distribution of the time taken to provide the first response to the customer in a conversation. Knowing this metric helps enable better resource allocation, setting realistic customer expectations, and enhancing overall service efficiency. 

Customer and product impact

Customers and products constitute the two key pillars of effective support processes and experiences. This section helps in analyzing conversations from the perspectives of customers and product parts, aiming to enhance customer satisfaction, guide product development, and inform strategic decisions for continuous improvement.

  • Active conversations per customer: Displays customers alongside the number of conversations they have created. Customers are ranked based on the number of conversations they have created, with the customer who created the most conversations appearing first. Understanding the number of conversations per customer offers insights into individual customer support needs, helping in prioritization decisions.

  • Active conversations per product part: Provides a view of the product parts and the conversations raised for these product parts. Product parts on this list are ranked by the associated number of conversations raised, with the product with the highest number of associated conversations appearing first. Understanding the number of conversations per product part helps identify areas for product improvement and guides development efforts.

Service level agreements

Service level agreements (SLAs) are formal commitments defining response and resolution times. Understanding SLA-related metrics enables teams to assess performance, ensure timely issue resolution, and maintain high customer satisfaction. Monitoring various metrics aids in resource management, proactive issue resolution, and continuous improvement, allowing customer experience teams to optimize processes and deliver efficient and effective service. These metrics help customer experience teams meet or exceed customer expectations and build strong, positive relationships.

  • SLA breaches: Provides the trend of SLA breaches for the metrics applied to conversations. Identifying trends in SLA breaches reveals patterns in meeting or missing commitments. This insight enables customer experience teams to address recurring challenges, enhance overall service efficiency, and implement strategic measures to consistently meet SLAs.

  • Conversation distribution across SLA stages: Provides the trend and breakdown of conversations across different SLA stages. The SLA stages are color-coded for identification and analysis. Drilling through any of the SLA stages shows a list of conversations that belong to that SLA stage at the selected point in time.

Customer satisfaction (CSAT)

Customer satisfaction (CSAT) measures customers' satisfaction with the support they receive. Analyzing CSAT-related metrics empowers teams to determine overall customer satisfaction and identify areas for improvement. Monitoring various CSAT metrics is crucial for fostering continuous improvement, ensuring that customer experience teams consistently deliver experiences that align with customer expectations and enhance satisfaction.

  • CSAT response rate: Displays the trend of CSAT surveys sent and responses received. Understanding the response rate ensures that the collected CSAT data is reliable, allowing customer experience teams to make informed decisions based on a comprehensive and representative set of customer feedback. Additionally, a low response rate may signal potential issues with the survey distribution or the need for adjustments to encourage more customer participation. This improves the overall effectiveness of the CSAT measurement process.

  • CSAT score distribution: Provides information about the distribution of CSAT scores across the received responses. Analyzing the distribution of CSAT scores offers valuable insights into focus areas for improvement. Additionally, drilling through to identify conversations with low CSAT scores is beneficial for targeted efforts to enhance service quality and address customer concerns.


Responsiveness measures the customer experience engineer's promptness in addressing customer requests. Monitoring engineer responsiveness metrics allows organizations to optimize support workflows, allocate resources effectively, and enhance overall responsiveness to customer needs.

  • Median first response time: Shows the trend of the middle value in the distribution of time taken to provide the first response to the customer once they have raised a query or a request through a conversation. It provides a reliable benchmark for evaluating customer experience efficiency in addressing initial user queries and is important for guiding resource allocation strategies to ensure timely and responsive customer interactions.

Conversation distribution

Identifying the various distributions of conversations is instrumental in understanding the center of workload and pinpointing areas that may need focus. This insight guides teams in allocating resources effectively. Additionally, it assists in strategic planning by allowing teams to adapt their workflows and provide a more responsive customer experience.

  • Conversations linked to tickets: Provides information about how many conversations are resolved by the customer experience engineer without the requirement of creating tickets and how many needed tickets to be created for further resolution. Useful insights for both customers and service engineers can be derived from this chart.

  • Conversations per channel: Provides information about the channel-wise distribution of conversations initiated by customers. It helps to understand which channel customers prefer when raising a request. This information assists in identifying popular channels, enabling organizations to prioritize and enhance support on those platforms, improving overall customer engagement and satisfaction.

  • Active conversations per owner: Provides information about the number of active conversations assigned to the customer experience engineer. This information aids in workload management, enabling teams to ensure equitable distribution, prioritize tasks effectively, and optimize customer experience operations.

  • Conversations created vs closed: Provides information about the trend number of closed and created conversations within the specified time range. It enables the tracking of the balance between incoming conversation volume and successfully resolved conversations, facilitating informed resource allocation, workflow adjustments, and continuous improvement in conversation management processes.

Turing efficiency

The Turing bot is an AI-powered chatbot designed to deflect user queries in conversation. When enabled in auto-response mode, it references the knowledge base to generate answers. Measuring the efficiency of the Turing bot is crucial for assessing its performance, enhancing user experience, optimizing resource allocation, improving the knowledge base, and ensuring a cost-effective and streamlined customer experience.

  • Bot deflection: Analyze the proportion of customer queries effectively handled by the Turing bot out of all customer queries raised. The bot deflection metric is useful for assessing the effectiveness of the Turing bot at handling customer queries. Drill-throughs can be used to further analyze the conversations that Turing is unable to handle, and updates to the knowledge base can be made accordingly.

  • Bot resolution: Analyze the proportion of customer queries effectively resolved by the Turing bot out of all the customer queries raised. The bot resolution metric provides insights into the proportion of queries the bot successfully manages, offering a clear indication of its impact on reducing human agents' workload.