An interactive dashboard to visualize sales performance, product categories, regional performance, and key business KPIs.

📌 Description This project analyzes online sales data using Power BI, focusing on performance metrics such as Total Sales Amount, Profit, Quantity Sold, and Payment Modes. The dashboard provides detailed visualizations to identify top-performing categories, sub-categories, and locations. It aims to deliver actionable insights for business strategy, marketing decisions, and operational improvements.

The dataset is split across two CSV files:

• Orders.csv – contains customer and order metadata (date, name, location)

• Details.csv – contains order-level details (profit, quantity, payment mode, category)

🧩 Key Features - KPI Cards: Total Amount, Total Profit, Total Quantity, Profit Margin

• Pie Charts: Sales by Category, Sales by Payment Mode

• Donut Chart: Sales by State

• Bar Chart: Sales by Sub-Category

• Map: Quantity sold across Indian States

• Interactive Slicers and Filters

⚒️ Tools & Techniques Power BI Desktop

DAX Calculations

Custom Visual Design for Clean UI/UX

From 2016 to 2018, we surveyed the world’s largest natural history museum collections to begin mapping this globally distributed scientific infrastructure. The resulting dataset includes 73 institutions across the globe. It has:

• Basic institution data for the 73 contributing institutions, including estimated total collection sizes, geographic locations (to the city) and latitude/longitude, and Research Organization Registry (ROR) identifiers where available.

• Resourcing information, covering the numbers of research, collections and volunteer staff in each institution.

• Indicators of the presence and size of collections within each institution broken down into a grid of 19 collection disciplines and 16 geographic regions.

• Measures of the depth and breadth of individual researcher experience across the same disciplines and geographic regions.

This dataset contains the data (raw and processed) collected for the survey, and specifications for the schema used to store the data. It includes:

1. A diagram of the MySQL database schema.
2. A SQL dump of the MySQL database schema, excluding the data.
3. A SQL dump of the MySQL database schema with all data. This may be imported into an instance of MySQL Server to create a complete reconstruction of the database.
4. Raw data from each database table in CSV format.
5. A set of more human-readable views of the data in CSV format. These correspond to the database tables, but foreign keys are substituted for values from the linked tables to make the data easier to read and analyse.
6. A text file containing the definitions of the size categories used in the collection_unit table.

The global collections data may also be accessed at https://rebrand.ly/global-collections. This is a preliminary dashboard, constructed and published using Microsoft Power BI, that enables the exploration of the data through a set of visualisations and filters. The dashboard consists of three pages:

Institutional profile: Enables the selection of a specific institution and provides summary information on the institution and its location, staffing, total collection size, collection breakdown and researcher expertise.

Overall heatmap: Supports an interactive exploration of the global picture, including a heatmap of collection distribution across the discipline and geographic categories, and visualisations that demonstrate the relative breadth of collections across institutions and correlations between collection size and breadth. Various filters allow the focus to be refined to specific regions and collection sizes.

Browse: Provides some alternative methods of filtering and visualising the global dataset to look at patterns in the distribution and size of different types of collections across the global view.

In this project, I conducted a comprehensive analysis of customer data using Power BI. The objective was to visualize and gain insights from the data, focusing on customer demographics and product categories.

📈The analysis includes the following key visualizations:

Customer Distribution by Age: illustrates the number of customers across different age groups, providing insights into the demographic distribution.

Customer Distribution by Time: This visualization shows the count of customers segmented by year, quarter, month, and day, helping identify trends over time.

Customer Distribution by Gender: displays the distribution of customers by gender, highlighting any significant differences.

Total Amount by Product Category: depicts the total revenue generated by each product category, allowing for easy comparison.

Quantity by Product Category: shows the total quantity of products sold in each category, helping to identify popular items.

The cards display key metrics:

Average Age: 41.39 Total Customers: 1000 Total Quantity Sold: 2514 Total Amount Sold: 465 000$ Total Transactions: 1000 Additionally, I implemented filters for product category, date, gender, quantity, and age, providing users with the ability to refine their analysis.

Findings:

The analysis of customer distribution by age reveals no specific relationship between age and the quantity of products sold. This indicates that purchasing behavior may not be strongly influenced by the customer’s age. There are notable peaks in the quantity sold on May 20, 2023, and again in July, suggesting higher purchasing activity during these periods. The customer distribution by gender shows that 49% of customers are female, while 51% are male. In terms of total amount sold by product category, electronics is the top category, generating the highest revenue, followed by clothing, with beauty ranking last. Similarly, when looking at quantity sold by product category, electronics makes up 33.77%, clothing is slightly higher at 35.56%, and beauty is the smallest category at 3.67%. This project demonstrates the power of Power BI in analyzing customer data and deriving actionable insights. The visualizations created provide a clear understanding of customer behavior and preferences, which can help businesses make informed decisions.

This dataset provides a snapshot of prices for various products across different stores over a span of several months. Here's a summary of the data:

Products: The dataset includes prices for a range of products including rice, dried beans, bottled water, canned vegetables, milk, rope, flashlight, duct tape, and water filters.

Stores: Prices are recorded from three different stores: Walmart, Costco, and Target.

Price Variation: Prices vary across stores and over time

Date: Prices are recorded on specific dates, allowing for the analysis of price trends over time.

This data can be analyzed to identify patterns in pricing, compare prices between stores, and assess how prices change over time for different products. Such analysis can inform purchasing decisions and provide insights into consumer behavior and market dynamics.

This dataset is obtained from https://github.com/AlexTheAnalyst/Power-BI/blob/main/Apocolypse%20Food%20Prep.xlsx. This is a guided project by @AlexTheAnalyst

1. Introduction

This Power BI dashboard provides a comprehensive analysis of call center performance, tracking key metrics such as customer satisfaction, call handling efficiency, agent performance, and response times. The interactive nature of the dashboard allows users to explore call trends, identify bottlenecks, and optimize customer service operations.

Why This Dashboard is Useful

Customer Service Metrics: Tracks answered vs. abandoned calls, resolution rates, and customer satisfaction.

Operational Efficiency: Analyzes response time, average handling time, and call distribution across agents.

Agent Performance: Identifies top-performing and underperforming agents based on resolution rates and talk duration.

Time-based Trends: Breaks down call volumes by month to detect patterns and peak periods.

Business Insights: Helps optimize staffing, reduce call abandonment rates, and improve service quality.

1. How to Use This Dashboard for Decision-Making

Use the Agent Filter → Compare agent performance based on answered calls and resolution rates.

Use the Topic Filter → Analyze call categories (e.g., Technical Support, Payment Issues).

Track Monthly Call Volume → Identify peak call periods and optimize staffing levels.

Monitor Customer Satisfaction Trends → Understand factors impacting call ratings.

Analyze Speed of Answer Metrics → Reduce wait times to improve customer experience.

1. Dataset and Key Measures Used

This dataset integrates various call center performance metrics and enables interactive visualization within Power BI.

Key Features and Columns

Column Name

Description

Call ID

Unique identifier for each call

Agent

Name of the call center agent handling the call

Date & Time

Timestamp when the call was received

Topic

Call category (e.g., Contract-related, Technical Support)

Answered (Y/N)

Indicates whether the call was answered

Resolved (Y/N)

Shows if the customer’s issue was resolved

Speed of Answer (seconds)

Time taken to answer the call

Average Talk Duration

Duration of the conversation

Satisfaction Rating

Customer feedback score (1-5)

Technical Features and Filters

Agent Filter → Compare individual agent performance.

Topic Filter → View call distribution across different topics.

Date Range Selector → Analyze trends over different periods.

Key Performance Indicators (KPIs)

Call Handling Efficiency:

Call Answer Rate: Percentage of calls answered vs. abandoned.

Call Resolution Rate: Percentage of calls successfully resolved.

Average Speed of Answer: Overall average 67.52 seconds.

Customer Satisfaction:

Average Satisfaction Score: 3.40 out of 5.

Time-based Analysis:

Monthly Call Volume Trends: (January: 1455, February: 1298, March: 1301).

Answered vs. Abandoned Calls Breakdown.

Agent Performance:

Top-performing and underperforming agents based on satisfaction scores and efficiency.

1. Key Insights and Business Implications

This dashboard offers interactive insights that help optimize call center performance. Below are some key findings:

A. Call Handling & Customer Satisfaction Trends

Findings:

81.08% of calls are answered, while 18.92% are abandoned.

Resolution rate is 72.92%, meaning some issues remain unresolved.

Average customer satisfaction score is 3.40 (out of 5).

Business Implications:

Improving resolution rates can enhance customer satisfaction.

Reducing abandoned calls by optimizing staffing and response times can improve service quality.

Training agents on issue resolution may boost overall ratings.

B. Monthly Call Volume & Peak Trends

Findings:

Call volume fluctuates, with peaks in January (1455 calls), February (1298), and March (1301).

Business Implications:

Peak call months require additional staffing to prevent high wait times.

Trend analysis helps plan for seasonal fluctuations.

C. Agent Performance Comparison

Findings:

Agent Dan has the highest average satisfaction rate (3.45), while Joe has the lowest (3.33).

Speed of answer varies across agents (Joe takes 70.99s, Becky takes 65.33s).

Business Implications:

Identifying top agents can help in training others.

Faster response times generally correlate with higher satisfaction.

1. Final Business Recommendations

Improve Training Programs: Focus on resolution techniques to boost satisfaction scores.

Optimize Call Routing: Reduce wait times by distributing calls efficiently among agents.

Monitor Peak Times: Adjust staffing based on historical call volume trends.

Analyze Agent Performance: Reward high performers and support underperforming agents.

1. Next Steps

Predictive Analytics: Use machine learning to forecast future call trends.

Customer Segmentation: Identify common issues faced by different customer groups.

Automated Support Solutions: Explore chatbots and AI to reduce call center workload.

1. Conclusion

This Call Center Performance Datase...