Leveraging AI to Improve Key Metrics and KPIs in Customer Support

An AI-driven solution can improve how you track the metrics, drive better decisions to improve KPIs, and help agents approach customer interactions in a better way.
Priyanshu Anand
October 9, 2023

Leveraging AI to Improve Key Metrics and KPIs in Customer Support

An AI-driven solution can improve how you track the metrics, drive better decisions to improve KPIs, and help agents approach customer interactions in a better way.
Priyanshu Anand
October 9, 2023

Leveraging AI to Improve Key Metrics and KPIs in Customer Support

An AI-driven solution can improve how you track the metrics, drive better decisions to improve KPIs, and help agents approach customer interactions in a better way.
Priyanshu Anand
September 28, 2023
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In today's cutthroat business environment, customer support is more than a department—it's a competitive edge. Key Performance Indicators (KPIs) serve as the navigational beacons for this essential function. However, the age-old methods of tracking these performance metrics are becoming increasingly cumbersome and ineffective. This is where Artificial Intelligence (AI) steps in, offering a transformative approach that not only tracks but also actively improves customer support KPIs, thereby boosting both productivity and efficiency.

The Importance of KPIs in Customer Support

KPIs are not just statistical data; they are the vital signs of your customer support ecosystem. They offer quantifiable measures that reflect the quality, efficiency, and effectiveness of your support team. In a world where customer expectations are soaring, failing to keep an eye on the right KPIs can lead to missed opportunities and, worse, customer attrition. Therefore, the need for accurate, real-time tracking is more critical than ever. It's not just about collecting data; it's about collecting the right data and acting on it swiftly.

The Limitations of Traditional Methods

The conventional way of tracking customer support KPIs is not just outdated; it's downright inefficient. Here's why:

  • Manual Data Entry: Support agents often have to manually log data, which is not only time-consuming but also prone to errors.
  • Lack of Real-Time Insights: By the time the data is collected, analyzed, and reports are generated, the information is often outdated. This lag makes it challenging to make quick, informed decisions.
  • Limited Scalability: As your business grows, so does the data. Manual methods simply can't scale to meet the demands of a growing customer base, leading to bottlenecks and reduced productivity.
  • Human Error: The risk of inaccuracies due to human error can lead to misguided strategies, affecting both customer satisfaction and the bottom line.

Transforming KPIs through AI Integration

In the SaaS landscape, Key Performance Indicators (KPIs) stand as the pillars that hold the structure of customer support operations. The integration of Artificial Intelligence (AI) brings a transformative approach to managing and optimizing these KPIs, steering customer support operations towards unprecedented levels of efficiency and effectiveness. AI isn't just a technological upgrade; it's a strategic advantage.

AI-led processes can elevate how you can measure your team’s performance and track effectiveness. Automated data collection can eliminate the need for manual entry, real-time analytics can provide instant insights and better decisions, predictive analytics can help address issues swiftly and proactively, and customization can help tailor personalized interactions.

First Response Time (FRT)

First Response Time (FRT) is the time taken to respond to a customer's query for the first time. The shorter the response time,  the higher will be the customer satisfaction as it shows that their concerns are being addressed promptly. AI-based solutions can be used to not just respond promptly but also monitor how quickly the responses are all together which highlights your team’s performance overall.

AI tools can also assist support agents by providing instant access to information and suggesting responses, reducing the time taken to handle each query. This means quicker resolutions, leading to higher customer satisfaction and more efficient operations. Implement AI-powered chatbots to handle routine queries autonomously, and use AI analytics to identify areas for further time optimization.

Customer Satisfaction (CSAT) and Net Promoter Scores (NPS)

In terms of providing instant access to information and suggesting responses to support professionals, more accurate answers can equip your support team with better answers and improve your team’s effectiveness in terms of customer satisfaction which can be measured through the Customer Satisfaction Score (CSAT).

Implement feedback surveys post-interaction to gather data and calculate CSAT regularly to monitor performance. Make sure you compare your score regularly and ensure that customer satisfaction improves over time.

Similarly, you can also get an NPS (Net Promoter Score). These are the most commonly used metrics to seek customer loyalty by asking how likely customers are to recommend the service to others. NPS helps in understanding customer loyalty and advocacy, which can be pivotal in business growth. Conduct periodic NPS surveys and track the scoring trend over time to understand customer perception and loyalty.

Self-help can reduce Customer Effort Score (CES)

AI can streamline processes and offer personalized solutions, reducing the effort customers need to exert to get their issues resolved. Simply making your support bot more accessible such as embedding it in your product or installing it on support channels on your Slack or Discord workspace - can immensely reduce the effort a customer needs to put in.

A lower CES indicates a smoother customer experience, fostering loyalty and satisfaction. Leverage AI to bridge existing gaps and offer intuitive self-service options, aiming to reduce customer effort in the support journey.

Higher Response Rate or Responsiveness

Response rate determines how many questions in the community have been answered. With AI auto-responding to messages, the response rates can tremendously be increased where most (if not all) questions are answered.

While AI can respond to most questions, your team can monitor the conversations and manually respond to questions that might not have been answered. This further improves responsiveness and this way, you can utilize both AI and your team to achieve higher response rates.

Improve Resolution Time Cycles

The complete resolution time is the total time taken to resolve a customer’s issue completely. This cycle directly correlates to improved customer satisfaction and product experience. With the help of AI, you can resolve problems faster through quicker and more accurate responses.

Leverage support software or tools to track the time taken from ticket creation to closure, aiming to reduce resolution time through continuous improvements.

Ticket Volume and Resolution

Ticket volume helps identify and keep track of customer queries being raised in a specific timeframe. Monitoring ticket volume helps in understanding the workload of the support team and in identifying recurring issues. Recurring tickets can be utilized to make improvements, and constantly focus on reducing ticket volume. Monitoring ticket volume helps in understanding the workload of the support team and in identifying recurring issues.

Essentially, AI can reduce the volume of support tickets raised by offering efficient self-service options and automating routine tasks, reducing the overall cost-per-ticket. A lower cost-per-ticket indicates a more efficient support system, allowing for resource optimization and cost savings.

Team performance

Measure individual team member performance by keeping track of their FRT, Response Rate, and more. Threado’s dashboard gives you a complete breakdown of team members and their metrics. With AI, the team can improve individual support metrics and contribute to an overall better team performance.

AI-Driven Solutions for Improved Metrics

An AI-driven solution can improve how you track the metrics and drive better decisions to improve KPIs. AI can help support agents improve how they approach customer interactions and help coach them to improve their responses. This will eventually improve important customer metrics like CSAT and NPS.

Ticket prioritization and segmentation using AI can improve how your team approaches customer concerns based on urgency, relevancy, and customer value. Workload distribution is a two-street that benefits your team and ensures great customer experience. Not to mention the self-service options that become extremely easy with AI and improve critical metrics like response rate and resolution time.

In today's cutthroat business environment, customer support is more than a department—it's a competitive edge. Key Performance Indicators (KPIs) serve as the navigational beacons for this essential function. However, the age-old methods of tracking these performance metrics are becoming increasingly cumbersome and ineffective. This is where Artificial Intelligence (AI) steps in, offering a transformative approach that not only tracks but also actively improves customer support KPIs, thereby boosting both productivity and efficiency.

The Importance of KPIs in Customer Support

KPIs are not just statistical data; they are the vital signs of your customer support ecosystem. They offer quantifiable measures that reflect the quality, efficiency, and effectiveness of your support team. In a world where customer expectations are soaring, failing to keep an eye on the right KPIs can lead to missed opportunities and, worse, customer attrition. Therefore, the need for accurate, real-time tracking is more critical than ever. It's not just about collecting data; it's about collecting the right data and acting on it swiftly.

The Limitations of Traditional Methods

The conventional way of tracking customer support KPIs is not just outdated; it's downright inefficient. Here's why:

  • Manual Data Entry: Support agents often have to manually log data, which is not only time-consuming but also prone to errors.
  • Lack of Real-Time Insights: By the time the data is collected, analyzed, and reports are generated, the information is often outdated. This lag makes it challenging to make quick, informed decisions.
  • Limited Scalability: As your business grows, so does the data. Manual methods simply can't scale to meet the demands of a growing customer base, leading to bottlenecks and reduced productivity.
  • Human Error: The risk of inaccuracies due to human error can lead to misguided strategies, affecting both customer satisfaction and the bottom line.

Transforming KPIs through AI Integration

In the SaaS landscape, Key Performance Indicators (KPIs) stand as the pillars that hold the structure of customer support operations. The integration of Artificial Intelligence (AI) brings a transformative approach to managing and optimizing these KPIs, steering customer support operations towards unprecedented levels of efficiency and effectiveness. AI isn't just a technological upgrade; it's a strategic advantage.

AI-led processes can elevate how you can measure your team’s performance and track effectiveness. Automated data collection can eliminate the need for manual entry, real-time analytics can provide instant insights and better decisions, predictive analytics can help address issues swiftly and proactively, and customization can help tailor personalized interactions.

First Response Time (FRT)

First Response Time (FRT) is the time taken to respond to a customer's query for the first time. The shorter the response time,  the higher will be the customer satisfaction as it shows that their concerns are being addressed promptly. AI-based solutions can be used to not just respond promptly but also monitor how quickly the responses are all together which highlights your team’s performance overall.

AI tools can also assist support agents by providing instant access to information and suggesting responses, reducing the time taken to handle each query. This means quicker resolutions, leading to higher customer satisfaction and more efficient operations. Implement AI-powered chatbots to handle routine queries autonomously, and use AI analytics to identify areas for further time optimization.

Customer Satisfaction (CSAT) and Net Promoter Scores (NPS)

In terms of providing instant access to information and suggesting responses to support professionals, more accurate answers can equip your support team with better answers and improve your team’s effectiveness in terms of customer satisfaction which can be measured through the Customer Satisfaction Score (CSAT).

Implement feedback surveys post-interaction to gather data and calculate CSAT regularly to monitor performance. Make sure you compare your score regularly and ensure that customer satisfaction improves over time.

Similarly, you can also get an NPS (Net Promoter Score). These are the most commonly used metrics to seek customer loyalty by asking how likely customers are to recommend the service to others. NPS helps in understanding customer loyalty and advocacy, which can be pivotal in business growth. Conduct periodic NPS surveys and track the scoring trend over time to understand customer perception and loyalty.

Self-help can reduce Customer Effort Score (CES)

AI can streamline processes and offer personalized solutions, reducing the effort customers need to exert to get their issues resolved. Simply making your support bot more accessible such as embedding it in your product or installing it on support channels on your Slack or Discord workspace - can immensely reduce the effort a customer needs to put in.

A lower CES indicates a smoother customer experience, fostering loyalty and satisfaction. Leverage AI to bridge existing gaps and offer intuitive self-service options, aiming to reduce customer effort in the support journey.

Higher Response Rate or Responsiveness

Response rate determines how many questions in the community have been answered. With AI auto-responding to messages, the response rates can tremendously be increased where most (if not all) questions are answered.

While AI can respond to most questions, your team can monitor the conversations and manually respond to questions that might not have been answered. This further improves responsiveness and this way, you can utilize both AI and your team to achieve higher response rates.

Improve Resolution Time Cycles

The complete resolution time is the total time taken to resolve a customer’s issue completely. This cycle directly correlates to improved customer satisfaction and product experience. With the help of AI, you can resolve problems faster through quicker and more accurate responses.

Leverage support software or tools to track the time taken from ticket creation to closure, aiming to reduce resolution time through continuous improvements.

Ticket Volume and Resolution

Ticket volume helps identify and keep track of customer queries being raised in a specific timeframe. Monitoring ticket volume helps in understanding the workload of the support team and in identifying recurring issues. Recurring tickets can be utilized to make improvements, and constantly focus on reducing ticket volume. Monitoring ticket volume helps in understanding the workload of the support team and in identifying recurring issues.

Essentially, AI can reduce the volume of support tickets raised by offering efficient self-service options and automating routine tasks, reducing the overall cost-per-ticket. A lower cost-per-ticket indicates a more efficient support system, allowing for resource optimization and cost savings.

Team performance

Measure individual team member performance by keeping track of their FRT, Response Rate, and more. Threado’s dashboard gives you a complete breakdown of team members and their metrics. With AI, the team can improve individual support metrics and contribute to an overall better team performance.

AI-Driven Solutions for Improved Metrics

An AI-driven solution can improve how you track the metrics and drive better decisions to improve KPIs. AI can help support agents improve how they approach customer interactions and help coach them to improve their responses. This will eventually improve important customer metrics like CSAT and NPS.

Ticket prioritization and segmentation using AI can improve how your team approaches customer concerns based on urgency, relevancy, and customer value. Workload distribution is a two-street that benefits your team and ensures great customer experience. Not to mention the self-service options that become extremely easy with AI and improve critical metrics like response rate and resolution time.

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Heading

In today's cutthroat business environment, customer support is more than a department—it's a competitive edge. Key Performance Indicators (KPIs) serve as the navigational beacons for this essential function. However, the age-old methods of tracking these performance metrics are becoming increasingly cumbersome and ineffective. This is where Artificial Intelligence (AI) steps in, offering a transformative approach that not only tracks but also actively improves customer support KPIs, thereby boosting both productivity and efficiency.

The Importance of KPIs in Customer Support

KPIs are not just statistical data; they are the vital signs of your customer support ecosystem. They offer quantifiable measures that reflect the quality, efficiency, and effectiveness of your support team. In a world where customer expectations are soaring, failing to keep an eye on the right KPIs can lead to missed opportunities and, worse, customer attrition. Therefore, the need for accurate, real-time tracking is more critical than ever. It's not just about collecting data; it's about collecting the right data and acting on it swiftly.

The Limitations of Traditional Methods

The conventional way of tracking customer support KPIs is not just outdated; it's downright inefficient. Here's why:

  • Manual Data Entry: Support agents often have to manually log data, which is not only time-consuming but also prone to errors.
  • Lack of Real-Time Insights: By the time the data is collected, analyzed, and reports are generated, the information is often outdated. This lag makes it challenging to make quick, informed decisions.
  • Limited Scalability: As your business grows, so does the data. Manual methods simply can't scale to meet the demands of a growing customer base, leading to bottlenecks and reduced productivity.
  • Human Error: The risk of inaccuracies due to human error can lead to misguided strategies, affecting both customer satisfaction and the bottom line.

Transforming KPIs through AI Integration

In the SaaS landscape, Key Performance Indicators (KPIs) stand as the pillars that hold the structure of customer support operations. The integration of Artificial Intelligence (AI) brings a transformative approach to managing and optimizing these KPIs, steering customer support operations towards unprecedented levels of efficiency and effectiveness. AI isn't just a technological upgrade; it's a strategic advantage.

AI-led processes can elevate how you can measure your team’s performance and track effectiveness. Automated data collection can eliminate the need for manual entry, real-time analytics can provide instant insights and better decisions, predictive analytics can help address issues swiftly and proactively, and customization can help tailor personalized interactions.

First Response Time (FRT)

First Response Time (FRT) is the time taken to respond to a customer's query for the first time. The shorter the response time,  the higher will be the customer satisfaction as it shows that their concerns are being addressed promptly. AI-based solutions can be used to not just respond promptly but also monitor how quickly the responses are all together which highlights your team’s performance overall.

AI tools can also assist support agents by providing instant access to information and suggesting responses, reducing the time taken to handle each query. This means quicker resolutions, leading to higher customer satisfaction and more efficient operations. Implement AI-powered chatbots to handle routine queries autonomously, and use AI analytics to identify areas for further time optimization.

Customer Satisfaction (CSAT) and Net Promoter Scores (NPS)

In terms of providing instant access to information and suggesting responses to support professionals, more accurate answers can equip your support team with better answers and improve your team’s effectiveness in terms of customer satisfaction which can be measured through the Customer Satisfaction Score (CSAT).

Implement feedback surveys post-interaction to gather data and calculate CSAT regularly to monitor performance. Make sure you compare your score regularly and ensure that customer satisfaction improves over time.

Similarly, you can also get an NPS (Net Promoter Score). These are the most commonly used metrics to seek customer loyalty by asking how likely customers are to recommend the service to others. NPS helps in understanding customer loyalty and advocacy, which can be pivotal in business growth. Conduct periodic NPS surveys and track the scoring trend over time to understand customer perception and loyalty.

Self-help can reduce Customer Effort Score (CES)

AI can streamline processes and offer personalized solutions, reducing the effort customers need to exert to get their issues resolved. Simply making your support bot more accessible such as embedding it in your product or installing it on support channels on your Slack or Discord workspace - can immensely reduce the effort a customer needs to put in.

A lower CES indicates a smoother customer experience, fostering loyalty and satisfaction. Leverage AI to bridge existing gaps and offer intuitive self-service options, aiming to reduce customer effort in the support journey.

Higher Response Rate or Responsiveness

Response rate determines how many questions in the community have been answered. With AI auto-responding to messages, the response rates can tremendously be increased where most (if not all) questions are answered.

While AI can respond to most questions, your team can monitor the conversations and manually respond to questions that might not have been answered. This further improves responsiveness and this way, you can utilize both AI and your team to achieve higher response rates.

Improve Resolution Time Cycles

The complete resolution time is the total time taken to resolve a customer’s issue completely. This cycle directly correlates to improved customer satisfaction and product experience. With the help of AI, you can resolve problems faster through quicker and more accurate responses.

Leverage support software or tools to track the time taken from ticket creation to closure, aiming to reduce resolution time through continuous improvements.

Ticket Volume and Resolution

Ticket volume helps identify and keep track of customer queries being raised in a specific timeframe. Monitoring ticket volume helps in understanding the workload of the support team and in identifying recurring issues. Recurring tickets can be utilized to make improvements, and constantly focus on reducing ticket volume. Monitoring ticket volume helps in understanding the workload of the support team and in identifying recurring issues.

Essentially, AI can reduce the volume of support tickets raised by offering efficient self-service options and automating routine tasks, reducing the overall cost-per-ticket. A lower cost-per-ticket indicates a more efficient support system, allowing for resource optimization and cost savings.

Team performance

Measure individual team member performance by keeping track of their FRT, Response Rate, and more. Threado’s dashboard gives you a complete breakdown of team members and their metrics. With AI, the team can improve individual support metrics and contribute to an overall better team performance.

AI-Driven Solutions for Improved Metrics

An AI-driven solution can improve how you track the metrics and drive better decisions to improve KPIs. AI can help support agents improve how they approach customer interactions and help coach them to improve their responses. This will eventually improve important customer metrics like CSAT and NPS.

Ticket prioritization and segmentation using AI can improve how your team approaches customer concerns based on urgency, relevancy, and customer value. Workload distribution is a two-street that benefits your team and ensures great customer experience. Not to mention the self-service options that become extremely easy with AI and improve critical metrics like response rate and resolution time.

In today's cutthroat business environment, customer support is more than a department—it's a competitive edge. Key Performance Indicators (KPIs) serve as the navigational beacons for this essential function. However, the age-old methods of tracking these performance metrics are becoming increasingly cumbersome and ineffective. This is where Artificial Intelligence (AI) steps in, offering a transformative approach that not only tracks but also actively improves customer support KPIs, thereby boosting both productivity and efficiency.

The Importance of KPIs in Customer Support

KPIs are not just statistical data; they are the vital signs of your customer support ecosystem. They offer quantifiable measures that reflect the quality, efficiency, and effectiveness of your support team. In a world where customer expectations are soaring, failing to keep an eye on the right KPIs can lead to missed opportunities and, worse, customer attrition. Therefore, the need for accurate, real-time tracking is more critical than ever. It's not just about collecting data; it's about collecting the right data and acting on it swiftly.

The Limitations of Traditional Methods

The conventional way of tracking customer support KPIs is not just outdated; it's downright inefficient. Here's why:

  • Manual Data Entry: Support agents often have to manually log data, which is not only time-consuming but also prone to errors.
  • Lack of Real-Time Insights: By the time the data is collected, analyzed, and reports are generated, the information is often outdated. This lag makes it challenging to make quick, informed decisions.
  • Limited Scalability: As your business grows, so does the data. Manual methods simply can't scale to meet the demands of a growing customer base, leading to bottlenecks and reduced productivity.
  • Human Error: The risk of inaccuracies due to human error can lead to misguided strategies, affecting both customer satisfaction and the bottom line.

Transforming KPIs through AI Integration

In the SaaS landscape, Key Performance Indicators (KPIs) stand as the pillars that hold the structure of customer support operations. The integration of Artificial Intelligence (AI) brings a transformative approach to managing and optimizing these KPIs, steering customer support operations towards unprecedented levels of efficiency and effectiveness. AI isn't just a technological upgrade; it's a strategic advantage.

AI-led processes can elevate how you can measure your team’s performance and track effectiveness. Automated data collection can eliminate the need for manual entry, real-time analytics can provide instant insights and better decisions, predictive analytics can help address issues swiftly and proactively, and customization can help tailor personalized interactions.

First Response Time (FRT)

First Response Time (FRT) is the time taken to respond to a customer's query for the first time. The shorter the response time,  the higher will be the customer satisfaction as it shows that their concerns are being addressed promptly. AI-based solutions can be used to not just respond promptly but also monitor how quickly the responses are all together which highlights your team’s performance overall.

AI tools can also assist support agents by providing instant access to information and suggesting responses, reducing the time taken to handle each query. This means quicker resolutions, leading to higher customer satisfaction and more efficient operations. Implement AI-powered chatbots to handle routine queries autonomously, and use AI analytics to identify areas for further time optimization.

Customer Satisfaction (CSAT) and Net Promoter Scores (NPS)

In terms of providing instant access to information and suggesting responses to support professionals, more accurate answers can equip your support team with better answers and improve your team’s effectiveness in terms of customer satisfaction which can be measured through the Customer Satisfaction Score (CSAT).

Implement feedback surveys post-interaction to gather data and calculate CSAT regularly to monitor performance. Make sure you compare your score regularly and ensure that customer satisfaction improves over time.

Similarly, you can also get an NPS (Net Promoter Score). These are the most commonly used metrics to seek customer loyalty by asking how likely customers are to recommend the service to others. NPS helps in understanding customer loyalty and advocacy, which can be pivotal in business growth. Conduct periodic NPS surveys and track the scoring trend over time to understand customer perception and loyalty.

Self-help can reduce Customer Effort Score (CES)

AI can streamline processes and offer personalized solutions, reducing the effort customers need to exert to get their issues resolved. Simply making your support bot more accessible such as embedding it in your product or installing it on support channels on your Slack or Discord workspace - can immensely reduce the effort a customer needs to put in.

A lower CES indicates a smoother customer experience, fostering loyalty and satisfaction. Leverage AI to bridge existing gaps and offer intuitive self-service options, aiming to reduce customer effort in the support journey.

Higher Response Rate or Responsiveness

Response rate determines how many questions in the community have been answered. With AI auto-responding to messages, the response rates can tremendously be increased where most (if not all) questions are answered.

While AI can respond to most questions, your team can monitor the conversations and manually respond to questions that might not have been answered. This further improves responsiveness and this way, you can utilize both AI and your team to achieve higher response rates.

Improve Resolution Time Cycles

The complete resolution time is the total time taken to resolve a customer’s issue completely. This cycle directly correlates to improved customer satisfaction and product experience. With the help of AI, you can resolve problems faster through quicker and more accurate responses.

Leverage support software or tools to track the time taken from ticket creation to closure, aiming to reduce resolution time through continuous improvements.

Ticket Volume and Resolution

Ticket volume helps identify and keep track of customer queries being raised in a specific timeframe. Monitoring ticket volume helps in understanding the workload of the support team and in identifying recurring issues. Recurring tickets can be utilized to make improvements, and constantly focus on reducing ticket volume. Monitoring ticket volume helps in understanding the workload of the support team and in identifying recurring issues.

Essentially, AI can reduce the volume of support tickets raised by offering efficient self-service options and automating routine tasks, reducing the overall cost-per-ticket. A lower cost-per-ticket indicates a more efficient support system, allowing for resource optimization and cost savings.

Team performance

Measure individual team member performance by keeping track of their FRT, Response Rate, and more. Threado’s dashboard gives you a complete breakdown of team members and their metrics. With AI, the team can improve individual support metrics and contribute to an overall better team performance.

AI-Driven Solutions for Improved Metrics

An AI-driven solution can improve how you track the metrics and drive better decisions to improve KPIs. AI can help support agents improve how they approach customer interactions and help coach them to improve their responses. This will eventually improve important customer metrics like CSAT and NPS.

Ticket prioritization and segmentation using AI can improve how your team approaches customer concerns based on urgency, relevancy, and customer value. Workload distribution is a two-street that benefits your team and ensures great customer experience. Not to mention the self-service options that become extremely easy with AI and improve critical metrics like response rate and resolution time.

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