Project: Analyses of Customer Service Data using Microsoft Excel
👉Real-time monitoring dashboard for tracking all the Project Goals:- 
👉iVision is a well-known analytics firm. iVision does analytics on the data shared by their clients. Recently, iVision collaborated with Nile, an E-commerce company. Nile intends to improve their customer service, but before doing so, Nile wants to get some insights on their customer service request data. To help Nile make better business decisions and improve their services, iVision is engaged to provide Nile access to the analytics dashboard and report.
👉Nile has shared its customer service data with iVision. As an employee of iVision, you are responsible for creating this analytics report.
Business Objective: The analysis aims to leverage data-driven approaches to optimize customer service processes, enhance customer experience, and drive overall business growth. By examining historical customer service data, the project seeks to identify patterns, trends, and opportunities for improvement, ultimately leading to enhanced customer loyalty and increased operational efficiency.
Project Goals:-👉
• Customer Sentiment Analysis: Perform sentiment analysis on customer interactions. Identify positive, negative, and neutral sentiments expressed by customers to understand overall satisfaction levels.
• Root Cause Analysis: Investigate common customer complaints. Pinpoint recurring problems to address them proactively and prevent future escalations.
• Service Response Time Analysis: Analyze response times for customer queries and support requests to assess the efficiency of the customer service team.
• Customer Segmentation: Segment customers based on their demographics, behavior, and preferences. Understand different customer segments' needs and pain points to tailor services and communications accordingly.
• Trends and Patterns Identification: Identify patterns and trends in customer service data to uncover opportunities for process improvements and innovative service offerings.
Customer Sentiments Analysis
Root Cause Analysis
Service Response Time Analysis
Customer Segmentation
Trends and Patterns Identification
Other Charts:-👉
Data Description:👉
• Id: Unique Customer id
• customer_name: Name of the customer
• sentiment: Sentiment of the customer (Neutral, Positive, Very Positive, Negative, Very Negative)
• csat_score: Customer Satisfaction Score (Scale of 1 to 10, 1 being lowest and 10 being Highest)
• call_timestamp: Date on which the call was made by the customer.
• call_day: Day of the Call (1 represents call was made on 1st of the month, similarly for other numbers)
• reason: Reason why the customer called (Billing Question, Service Outage & Payments)
• city: City to which the customer belongs
• state: State to which the customer belongs
• channel: Mode of communication that customer used (Call-center, Chatbot, Email, Web)
• response_time: How fast the customer request was serviced (SLA level, Above SLA, Below SLA, Within SLA)
• call duration in minutes: Duration of the call
• call_center: Location of the call center where service request was handled.