Tools used in this project
Call Center Trend Analysis

Microsoft Dashboard

About this project

Project Overview

This project focuses on analyzing call center performance metrics to understand how well calls are handled, how satisfied customers are, and overall efficiency. We looked at key metrics like total calls answered, total calls abandoned, average satisfaction rating, average speed of answer, and more. We also used reference values on card visuals to compare our performance against target goals.

Key Metrics and DAX Formulas

  1. Total Calls Answered
    • DAX Formula:Total Calls Answered = COUNTROWS(FILTER(TableName, TableName[Answered] = "Yes"))
    • Explanation: This formula counts how many calls were answered by looking at rows in the data where the "Answered" column says "Yes".
  2. Total Calls Abandoned
    • DAX Formula:Total Calls Abandoned = COUNTROWS(FILTER(TableName, TableName[Abandoned] = "Yes"))
    • Explanation: This formula counts how many calls were abandoned by looking at rows in the data where the "Abandoned" column says "Yes".
  3. Average Satisfaction Rating
    • DAX Formula:Average Satisfaction Rating = AVERAGE(TableName[SatisfactionRating])
    • Explanation: This formula calculates the average rating given by customers for their satisfaction.
  4. Average Speed of Answer (ASA)
    • DAX Formula:Average Speed of Answer = AVERAGE(TableName[SpeedOfAnswer])
    • Explanation: This formula calculates the average amount of time it takes to answer calls.

Key Findings

  1. Call Volume and Resolution:
    • Total Answered Calls: 4,054
    • Total Abandoned Calls: 946
    • Resolved Calls: 3,646 (72.92% of answered calls)
    • Unresolved but Answered Calls: 408
  2. Call Timing Analysis:
    • Peak Answer Times: 9 AM to 1 PM
    • Lowest Answer Times: 6 PM
  3. Topic Analysis:
    • Top Call-Generating Topics: Streaming, Technical Support, Payment-related, Admin Support, Contract-related
    • Most Resolved Topic: Streaming
  4. Agent Performance:
    • Top Performing Agents: Becky, Stuart, and Diane (in terms of answered and resolved calls)
  5. Daily Call Progress:
    • Highest Call Volume Day: Monday
    • Lowest Call Volume Day: Friday

Reference Metrics on Card Visuals

  1. Service Level Agreement (SLA):
    • Reference Value: 0.90
    • Explanation: SLA is a target rate showing how well we meet our promised service standards. A value of 0.90 means we aim to meet our service promises 90% of the time. It helps us understand if we're providing the level of service we promised to our customers.
  2. Total Answered Calls (TAnC):
    • Reference Value: 500
    • Explanation: TAnC shows the target number of calls we want to answer. The goal of 500 calls helps us gauge if we're handling enough calls to meet demand.
  3. Total Abandoned Calls (TAC):
    • Reference Value: 100
    • Explanation: This metric shows the target number of calls that get abandoned. A goal of 100 means we want to keep the number of abandoned calls as low as possible to ensure customers aren't left hanging.
  4. Abandonment Rate (AR):
    • Reference Value: 0.19
    • Explanation: This rate shows the percentage of calls that get abandoned. A target of 0.19 means we aim to have less than 19% of calls abandoned, indicating we're doing well in handling calls promptly.
  5. Target Customer Satisfaction (TCS):
    • Reference Value: 4.50
    • Explanation: TCS is the target average rating we want from customers for their satisfaction. A value of 4.50 out of 5 indicates we're striving for high customer satisfaction.
  6. First Call Resolution (FCR):
    • Reference Value: 0.90
    • Explanation: FCR shows how often we resolve customer issues on the first call. A target of 0.90 means we aim to resolve issues in the first call 90% of the time, which is critical for customer satisfaction.
  7. Average Speed of Answer (ASA):
    • Reference Value: 20.00 seconds
    • Explanation: ASA shows how quickly we answer calls. A target of 20 seconds means we aim to answer calls within 20 seconds on average, ensuring customers aren't waiting too long.

Conclusion and Recommendations

  • The analysis of the call center data shows several areas for improvement.
  • Calls are mostly answered promptly in the morning and early afternoon, but there are fewer calls answered later in the day.
  • We need to focus on handling calls more efficiently throughout the day to reduce the number of abandoned calls.
  • Training agents better and optimizing how calls are distributed can help improve performance. Additionally, addressing the most common issues quickly will help increase the resolution rate and customer satisfaction.
  • Using the reference values on card visuals allows us to continuously monitor and compare our performance against these targets, ensuring we strive towards better service quality and operational efficiency.
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