SayPro Information and Targets Needed for the Quarter Customer Support Data Insights into the number and types of technical issues customers experience

SayPro Information and Targets Needed for the Quarter Customer Support Data Insights into the number and types of technical issues customers experience to prioritize improvements from SayPro Monthly January SCMR-17 SayPro Monthly IT Services: Software development, cybersecurity, and IT support by SayPro Online Marketplace Office under SayPro Marketing Royalty SCMR

The Customer Support Data section is an essential component for understanding the nature of technical issues faced by SayPro’s customers. By gathering and analyzing this data, SayPro can prioritize improvements in products and services, enhance user experience, and improve the overall quality of support. This section provides detailed insights into the types and frequency of technical issues reported by customers, how they are being resolved, and areas where improvements are needed.

Below is a breakdown of the information and targets needed for the Customer Support Data section for the quarter.

1. Customer Support Issue Categories

Definition: Customer support issues are typically categorized by their nature, such as product issues, technical bugs, service outages, account problems, or user errors. Classifying issues helps to identify patterns, prioritize common problems, and address recurring technical challenges.

• Target:
  • Categorize 100% of customer support tickets within 24 hours of submission.
  • Achieve accurate classification of issues into defined categories: Product Bugs, Service Outages, Performance Issues, Account Issues, User Errors, and Security Concerns.
• Data Needed:
  • Issue Categories: List of categories such as:
    • Product Bugs: Errors or malfunctions within the software or marketplace.
    • Service Outages: Instances where a system or service becomes unavailable or nonfunctional.
    • Performance Issues: Slow system response, delays in loading, or other performance-related problems.
    • Account Issues: Problems with login, user authentication, payment, or account settings.
    • User Errors: Customer mistakes or confusion while using the system, such as incorrect setup or configuration.
    • Security Concerns: Issues related to potential data breaches, vulnerabilities, or account security.
• Data Collection Method:
  • Support Ticketing Systems: Use platforms such as Zendesk, Freshdesk, or Jira Service Desk to automatically categorize and tag issues.
  • Customer Feedback Surveys: Collect customer feedback to ensure that the categorization reflects real customer concerns.

2. Frequency and Volume of Customer Support Issues

Definition: Tracking the frequency and volume of customer support issues gives insight into which problems are most prevalent and need urgent attention. This metric is crucial for resource allocation and identifying trends over time.

• Target:
  • Track all customer issues with a goal to decrease the overall volume of tickets by 10% compared to the previous quarter.
  • Identify and address the top 3 most frequent issues within the quarter.
• Data Needed:
  • Total Number of Support Tickets: The total number of support tickets or inquiries received from customers each month.
  • Ticket Volume by Category: The number of tickets within each identified category (e.g., Product Bugs, Service Outages, Performance Issues, etc.).
  • Trends Over Time: Trends that show if certain issues are rising or declining in frequency month over month.
• Data Collection Method:
  • Ticket Management Systems: Use reports generated from platforms like Zendesk, Salesforce Service Cloud, or Freshdesk to gather ticket volume and categorize them effectively.
  • Dashboard Analytics: Visualize trends and patterns using tools like Power BI, Tableau, or the reporting features in ticketing systems.

3. Resolution Time and Response Time

Definition: The time taken to respond to and resolve customer issues is a critical metric for customer satisfaction. Longer resolution times often lead to dissatisfaction and potential loss of customers.

• Target:
  • First Response Time: Achieve an average first response time of under 1 hour for critical issues.
  • Resolution Time: Resolve 90% of non-critical issues within 24 hours and 95% of critical issues within 4 hours.
• Data Needed:
  • Average First Response Time: The average time taken to respond to a customer’s first request for assistance.
  • Average Resolution Time: The average time taken to resolve an issue from the time the ticket is opened.
  • SLA Compliance: Percentage of tickets resolved within agreed service-level agreements (SLAs).
  • Escalation Rate: The percentage of issues that were escalated to higher levels of support or management.
• Data Collection Method:
  • Support Ticketing Reports: Ticketing platforms like Zendesk or Freshdesk will automatically track response and resolution times.
  • Service Level Management Tools: Use tools like Freshservice, Jira Service Desk, or ServiceNow that have built-in SLA tracking to monitor resolution times.

4. Customer Satisfaction and Feedback

Definition: Measuring customer satisfaction is essential for understanding the effectiveness of the support team and the overall customer experience. Gathering feedback also helps identify areas for improvement in both support processes and product features.

• Target:
  • Achieve a Customer Satisfaction (CSAT) score of 85% or higher for all resolved tickets.
  • Collect feedback from at least 30% of customers who submit support tickets.
• Data Needed:
  • Customer Satisfaction Scores: Collect CSAT scores from customers after ticket resolution (e.g., 1-5 rating).
  • Net Promoter Score (NPS): Track NPS to measure customer loyalty and their likelihood of recommending SayPro’s services to others.
  • Customer Feedback Comments: Qualitative feedback from customers that provides insights into pain points and areas of improvement.
• Data Collection Method:
  • Survey Tools: Send post-resolution surveys via email or within the support portal (using tools like SurveyMonkey, Zendesk CSAT, or Google Forms).
  • Ticket System Surveys: Many ticketing systems have built-in survey features to collect CSAT and NPS directly after issue resolution.

5. Recurring and Unresolved Issues

Definition: Identifying recurring issues helps prioritize areas for improvement in the product or system. It is important to track issues that are unresolved or need follow-up, as they can lead to frustration and a negative customer experience.

• Target:
  • Reduce the rate of recurring issues by 15% from the previous quarter.
  • Achieve an overall unresolved ticket rate of under 5% by the end of the quarter.
• Data Needed:
  • Recurring Issues: Track the frequency of issues that customers are reporting multiple times (e.g., the same issue reported by multiple customers or repeatedly by the same customer).
  • Unresolved Tickets: The number of open or unresolved tickets by the end of the quarter, including those that are being escalated.
  • Escalated Tickets: Track tickets that were escalated to higher support tiers and their resolution status.
• Data Collection Method:
  • Ticket Management System: Automated reports and advanced filtering features in systems like Zendesk or Freshdesk can identify recurring issues and unresolved tickets.
  • Root Cause Analysis (RCA): Perform RCA on recurring or unresolved issues to identify the underlying causes and address them in product or process improvements.

6. Targets for the Quarter

1. Issue Categorization:
   • Ensure 100% of tickets are categorized accurately within 24 hours of submission.
   • Achieve clear and consistent classification for issues (e.g., product bugs, performance issues, account problems).
2. Ticket Volume:
   • Reduce the overall volume of support tickets by 10% compared to the previous quarter.
   • Identify and address the top 3 most frequent customer support issues.
3. Resolution Timeliness:
   • First response time: Achieve an average first response time of under 1 hour for critical issues.
   • Resolution time: Resolve 90% of non-critical issues within 24 hours and 95% of critical issues within 4 hours.
4. Customer Satisfaction:
   • Achieve a Customer Satisfaction (CSAT) score of 85% or higher for resolved tickets.
   • Collect feedback from at least 30% of customers submitting support tickets.
5. Recurring and Unresolved Issues:
   • Reduce the rate of recurring issues by 15%.
   • Ensure an unresolved ticket rate under 5% by the end of the quarter.

Conclusion

The Customer Support Data section is vital for tracking and analyzing customer-reported issues, improving the overall customer experience, and identifying areas for improvement in SayPro’s products and services. By setting clear targets for issue categorization, resolution times, and customer satisfaction, SayPro can ensure efficient support processes, enhance system reliability, and ultimately drive customer loyalty. Continuous tracking and analysis of support data will also help prioritize future improvements and proactively address any recurring problems.