Visualization & Analyzing
Telecom Usage Patterns and Improving Services
Background
Work for a telecom company that provides mobile and data services to its customers. The company aims to enhance customer satisfaction and optimize its services based on data-driven insights. You have access to a comprehensive dataset containing customer data, call data, SMS data, data usage, network quality, and billing information.
Objective
The objective of this case study is to analyze the visual charts derived from the dataset and extract meaningful insights to improve services, customer experience, and optimize costs.
Dataset Information
The dataset used in this case study comprises various aspects of the telecom industry, providing valuable insights into customer behavior, usage patterns, network quality, and billing information. The dataset is structured and organized to facilitate comprehensive analysis and decision-making. It is stored in a JSON file format, allowing easy accessibility and compatibility with various data analysis tools and programming languages. The dataset contains customer data, including customer ID, name, age, gender, contact details, and subscription dates. Additionally, it includes call data, SMS data, data usage, network quality metrics such as signal strength and call drop rates, and billing information encompassing total amounts, itemized charges, and billing periods. This rich dataset empowers telecom companies to gain a holistic understanding of their customers, optimize services, and drive business growth.
Example dataset
| ID | Name | Age | Gender | Address | Contact Number | Email Address | Subscription Start Date | Subscription End Date | Monthly Payment |
| Call ID | Caller ID | Receiver ID | Call Start Time | Call End Time | Call Duration | Call Type (local, international, roaming) | Call Cost |
| SMS ID | Sender ID | Receiver ID | SMS Sent Time | SMS Received Time | SMS Cost |
| Customer ID | Data Usage ID | Date | Usage (in MB) | Data Cost |
| Customer ID | Location | Signal Strength | Call Drop Rate | Data Speed |
| Customer ID | Billing Period | Total Amount | Itemized Charges (Call charges, SMS charges, Data charges, etc.) |
Analysis
- Customer Demographics: By analyzing the bar chart and pie chart, you observe the following:
- The majority of customers fall into the 46-60 age group, indicating a potential target segment for marketing and promotional activities.
- The gender distribution shows an equal representation of male and female customers, suggesting a balanced customer base.
- Usage Patterns: From the line chart depicting monthly data usage trends, you identify the following:
- Data usage is steadily increasing over time, indicating a growing demand for data services.
- Identifying peak usage months can help in capacity planning and infrastructure optimization.
- Call Analysis: By examining the stacked bar chart and scatter plot, you discover:
- Local calls are the most common call type, followed by international and roaming calls. This information can guide pricing strategies and offer tailored plans.
- The scatter plot reveals a correlation between signal strength and call drop rate. Locations with lower signal strength tend to have a higher call drop rate, highlighting areas for network improvement.
- Billing Analysis: Utilizing the box plot and treemap, you uncover the following insights:
- The distribution of monthly payments shows a range of values, indicating the presence of different customer segments with varying preferences and budgets.
- Analyzing itemized charges in the treemap can help identify the primary contributors to billing costs, such as call charges, SMS charges, or data charges. This information can aid in optimizing pricing and offering customized plans.
- Network Quality: By examining the heatmap and geographic map, you gain the following insights:
- The heatmap showcases the peak call hours during the day, helping identify periods of high network congestion and plan network maintenance activities accordingly.
- The geographic map highlights areas with varying signal strengths, enabling targeted infrastructure improvements to enhance coverage and customer experience.
Conclusion
Based on the analysis of the visual charts derived from the dataset, the following recommendations can be made to improve services and customer experience:
- Focus on targeted marketing and promotional activities to attract and retain customers in the 46-60 age group, a significant customer segment.
- Optimize network infrastructure in areas with lower signal strength to reduce call drop rates and enhance call quality.
- Offer tailored plans and pricing strategies based on usage patterns, such as data-heavy plans for customers with increasing data usage.
- Regularly analyze billing information to identify cost optimization opportunities and offer value-added services based on itemized charges.
- Continuously monitor network quality metrics and conduct periodic maintenance to address peak call hours and improve overall network performance.
Implementing these recommendations will enable the telecom company to provide better services, enhance customer satisfaction, and stay competitive in the market.
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