This project contains data of a call center, we are given this dataset so that we can analyze the call pattern that the center gets and analyze it for making the center more efficient for work and tell the stakeholders where the call center is lagging and the scope of improvement for it. In this process 2 pages are provided as a dashboard, where in first dashboard all KPI's are visualized for understanding the data and in second page is the grid for the data set with some important columns used for visualization.
Problem Statement:
1.Total number of calls: We need to track and display the total number of calls received by our call center over a specified period.
2.Total call duration in hours: It is crucial to understand the total amount of time our call center staff spends on calls in hours, which can help us in resourse allocation and capacity planning.
3.Total call duration in minutes: Similar to the total call duration in hours, this KPI provides the total call time but in minutes, offering a more granular view of call durations.
4.Average call duration in minutes: To assess the efficiency of our agents, we need to calculate and display the average call duration in minutes. This metric can help identify trends in call handling.
5.Response Time Percentage: Response time is a critical factor in customer satisfaction. This KPI should display the percentage of calls answered within a predefined time frame, helping us gauge our ability to provide prompt service.
Charts Requirement:
1.Total call by day (Column Chart): Display a column chart that shows the total number of calls on each day over a specified time period.
2.Total calls by state(Filled Map Chart): Create a filled map chart that visualizes the total number of calls recieved from different states or regions.
3.Top reson for calls(Tree Map): Implement a tree map chart to display the top reasons for calls. Each box in the tree map represents a call reason.
4.Total calls by channel(Donut Chart): Create a donut chart to showcase the distribution of calls by different communication channels.
5.Total calls by sentiment(Column Chart): Utilize a column chart to illustrate the distribution of calls by sentiment(e.g,positive, negative, neutral).
6.Total calls by call center(Bar Chart): Create a bar chart that presents the total number of calls handeled by each call center or department.
Analysis steps:
1.Create a Problem Statement.
2.Identify the data you want to analyze.
3.Explore and clean the data.
4.Analyze the data to get useful insights.
5.Present the data interms of reports or dashboards using visualization.
Findings:
- Fridays and thursdays are the most called day as per the data from LA mostly for billing questions.
- Sundays and Mondays are fairly active, with lesser call duration and good response time.
- Most cities from where we got most calls are mostly LA, requesting for billing and service outage.
Sugesstions:
1.Response time can be more efficient so that callers would have positive sentiments after talking which would be good for the call center. 2.We can say that total calls that are coming to the call center are from Los Angeles so we can also make other clients get solutions in lesser time so that we can get more activity from other cities too.