Call Center Dashboard Power BI Report

1. Project Overview

The Call Center Dashboard provides a comprehensive analysis of call center operations, tracking key metrics such as ticket volume, resolution time, SLA compliance, and monthly performance trends. This dashboard helps call center managers improve efficiency and customer support performance.

2. Objectives

• Monitor call, chat, and email volumes over time.
• Track average resolution time (days) per channel.
• Analyze SLA compliance rates.
• Identify ticket volume distribution by issue category.
• Provide trend analysis for performance improvements.

3. Data Sources & Structure

Tables and Fields

1. Call Center Metrics Table
   • Date
   • Channel (Call, Chat, Email)
   • Total Volume
   • MoM Change (%)
   • Avg. Resolution Time (Days)
   • SLA Compliance Rate (%)
2. Ticket Categories Table
   • Category Name
   • Total Tickets
   • Channel (Call, Chat, Email)
   • Resolution Time (Days)
3. Time Series Data Table
   • Year
   • Month
   • Total Calls, Chats, Emails
   • SLA Compliance %
4. Filters & Slicers
   • Year and Month Filter
   • Channel Type (Call, Chat, Email)
   • Ticket Category Filter

4. Data Processing & Transformations

SQL Queries for Data Extraction

SELECT 
    Date, Channel, SUM(Total_Volume) AS Total_Volume,
    AVG(Resolution_Time) AS Avg_Resolution_Time,
    AVG(SLA_Compliance) AS SLA_Compliance_Rate,
    (SUM(Total_Volume) - LAG(SUM(Total_Volume), 1) OVER (PARTITION BY Channel ORDER BY Date)) * 100 / NULLIF(LAG(SUM(Total_Volume), 1) OVER (PARTITION BY Channel ORDER BY Date), 0) AS MoM_Change
FROM Call_Center_Data
GROUP BY Date, Channel;

Power Query Transformations

• Data Cleaning: Handle null values, ensure correct data types.
• Merging Tables: Combine ticket category and call center metrics.
• Creating Time Intelligence Columns: Add MoM percentage change for ticket volumes.

5. DAX Measures for Power BI

Month-over-Month Change (%)

MoM Change % = 
VAR PrevMonth = CALCULATE(SUM(Call_Center_Data[Total_Volume]), PREVIOUSMONTH(Call_Center_Data[Date]))
RETURN IF(PrevMonth = 0, BLANK(), (SUM(Call_Center_Data[Total_Volume]) - PrevMonth) / PrevMonth * 100)

SLA Compliance Rate (%)

SLA Compliance Rate % = 
DIVIDE(
    SUM(Call_Center_Data[SLA_Compliance]),
    COUNT(Call_Center_Data[Date]),
    0
)

Avg. Resolution Time (Days)

Avg Resolution Time = 
DIVIDE(
    SUM(Call_Center_Data[Resolution_Time]),
    COUNT(Call_Center_Data[Resolution_Time]),
    0
)

6. Visuals & Dashboard Elements

1. KPI Cards:
   • Total Call, Chat, and Email Volumes
   • Avg. Resolution Time (Days)
   • SLA Compliance Rate (%)
   • Month-over-Month Change (%)
2. Bar Charts:
   • Ticket Volume by Category
   • Resolution Time by Category
3. Line Charts:
   • Monthly Trend of Calls, Chats, and Emails
   • SLA Compliance Rate Over Time
4. Tables:
   • Ticket Volume Breakdown by Channel
   • Resolution Time by Ticket Category
5. Slicers:
   • Year, Month, Channel, and Ticket Category filters

7. Deployment & Performance Optimization

• Scheduled Data Refresh: Refresh call center data weekly.
• Optimized DAX Queries: Use pre-aggregated calculations for efficiency.
• Aggregated Tables: Improve dashboard performance by summarizing key metrics.

8. Security & Access Control

• Row-Level Security (RLS): Restrict data visibility based on user roles.
• Role-based Access: Define different access levels for call center managers, team leads, and analysts.

9. Project Completion Method

Step 1: Data Extraction (SQL Queries)

• Extract call, chat, and email volume data.
• Aggregate SLA compliance and resolution times.

Step 2: Data Transformation (Power Query)

• Clean, merge, and shape the data.
• Create calculated columns for MoM changes.

Step 3: Data Modeling & Relationships

• Establish relationships between call center metrics, ticket categories, and time series data.
• Optimize performance by indexing key fields.

Step 4: DAX Measures Development

• Create calculations for SLA compliance, resolution time, and MoM percentage change.
• Implement time-based calculations for trend analysis.

Step 5: Visualization & Dashboard Building

• Design KPI cards, bar charts, line graphs, and tables.
• Optimize user experience with dynamic filters.

Step 6: Testing & Validation

• Validate SQL outputs against Power BI visuals.
• Ensure data accuracy and slicer functionality.

Step 7: Deployment & Optimization

• Publish the report to Power BI Service.
• Set up scheduled refresh and security access controls.

Step 8: User Training & Documentation

• Provide training for end-users on dashboard functionalities.
• Document key metrics, definitions, and best practices.

10. Expected Outcomes

• Enhanced visibility into call center operations.
• Improved efficiency in tracking ticket resolution and SLA compliance.
• Actionable insights for optimizing customer support workflows.
• Data-driven decision-making for call center management.

sample data files for your Power BI Call Center Dashboard:

1. Download Call_Center_Sample_Data
2. Download Ticket_Category_Sample_Data

Developed by Muhammad Zahid