Monthly Customer Service Level Analysis - Specialty Retail

Track and analyze customer service performance metrics across channels, focusing on response times, resolution rates, and customer satisfaction to optimize service delivery and identify improvement opportunities.

Report Objective

Monitor and evaluate customer service performance metrics on a monthly basis, identifying trends in service levels, channel effectiveness, and areas requiring attention to maintain high-quality customer support in the specialty retail environment.

Service Level Performance Metrics

Track core service level indicators including response times, resolution rates, and customer satisfaction scores.

Questions to Consider:

Mar 2024Apr 2024May 2024month1.02.03.04.0first_response_time vs. resolution_ratefirst_response_timeresolution_rateHow are our core service metrics trending?Monthly tracking of response time, resolution rate, and CSAT score
  • What is the trend in first response time month-over-month?

  • Are there seasonal patterns in service performance?

  • How do resolution rates correlate with response times?

  • How stable are our customer satisfaction scores?

  • What factors might be influencing CSAT variations?

  • Is there a correlation between CSAT and resolution rates?

Mar 2024Apr 2024May 2024month0.01.02.03.04.0csat_scorecsat_scoreWhat is our customer satisfaction trend?Monthly CSAT scores with resolution rate overlay

Channel Distribution and Volume Analysis

Analyze ticket volumes and distribution across different support channels.

Questions to Consider:

Mar 2024Apr 2024May 2024month05001,0001,500sum(ticket_count)PhoneEmailChatSocialHow is ticket volume distributed across channels?Monthly ticket volume by support channel
  • Which channels are handling the most volume?

  • Are there significant shifts in channel usage?

  • How does channel distribution impact service levels?

Issue Categories and Resolution Times

Examine the distribution of issues and their respective resolution times.

Questions to Consider:

  • Which issues are most frequent and time-consuming?

  • Are there opportunities to optimize resolution processes?

  • How can we better allocate resources based on issue types?

Product QueryShippingReturnsTechnicalissue_type0.0%100.0%200.0%300.0%sum(ticket_percentage)sum(ticket_percentage)What are our most common issue types and resolution times?Distribution of tickets by issue type with average resolution time