Customer Retention Dashboard

In the fast-paced telecom industry, customer retention plays a pivotal role in achieving sustained growth and success.

In this blog post, we will explore how Power BI, a leading data visualization tool, can assist a virtual telecom company in defining key performance indicators (KPIs) and creating an insightful dashboard to gain a deeper understanding of customer retention. 

 METHODOLOGY

1. Defining the Project Objective: The first step is to clearly articulate the objective of the project, which is to create a customer retention visualization dashboard using Power BI for a virtual telecommunications company. The goal is to gain insights into customer retention and understand their preferences and behavior.

2. Identify Key Performance Indicators (KPIs): KPIs that align with the company's customer retention goals were identified. Examples of such KPIs are customer churn rate, customer satisfaction, demographics and customer information.

3. Gather and Prepare Data: Collect relevant data from various sources within the virtual telecommunications company, such as customer records, call logs, billing information, and customer support interactions. Cleanse and transform the data as needed to ensure its accuracy and consistency. 

4. Design the Power BI Dashboard: Utilize the Power BI to design and develop the customer retention dashboard. Begin by selecting appropriate visualizations, such as line charts, bar graphs, and tables, to represent the KPIs effectively. 

5. Create Visualizations: Using the prepared data, visualizations were created in Power BI that highlight the identified KPIs. For example, the payment method and contract type were visualized using bar charts and customer demographics were mainly represented by pie charts. The visualizations are intuitive, informative, and aligned with the project's objectives.

6. Implementation of Interactive Features: Power BI's interactive features were leveraged to enhance the usability and user experience of the dashboard. Slicers and filters were incorporated to enable users to explore the data and dive deeper into specific customer segments or time periods. This interactivity allows stakeholders to gain deeper insights and make data-driven decisions. 

7. Validation and Iteration: The dashboard will be validated by sharing it with the retention team and other key stakeholders. The visualization can be continuously refined to ensure that the dashboard effectively addresses the information needs of the virtual telecommunications company.  

 ANALYSIS AND VISUALIZATION

After importing the dataset into Power BI, the data was cleaned, shaped, and organized for analysis using Power Query. Tasks such as removing duplicates, filtering rows and splitting columns were accomplished through Power Query.

To enrich the analysis, calculated measures were created using DAX (Data Analysis Expressions) to perform specific calculations required for insights. For instance, to determine the percentage of customers subscribing to 'Tech Support' services, the following DAX calculation was employed: Tech Support = [Number of customers that used tech support]/'01 Churn-Dataset'[Customer Count].

Once the KPIs were defined, they were incorporated into the visualizations. Appropriate visualizations were carefully selected based on the data's characteristics and the intended insights. Additionally, filters and slicers were added to the visualizations, enabling users to interact with the data. For example, a slicer was included to allow users to select and view information specifically related to the type of customers who left the company.

To enhance the user experience and facilitate seamless exploration of the data, interactions were defined between different visualizations. This enabled cross-filtering, where selecting specific data points in one visualization influenced the data displayed in other connected visualizations.

By leveraging Power BI's data preparation capabilities, utilizing DAX calculations, and designing interactive visualizations, the analysis becomes more robust and user-friendly. These features empower stakeholders to interact with the data, gain deeper insights, and make informed decisions regarding customer retention strategies.

Upon conducting further analysis and visualization, the following key insights were deduced:

• Monthly Contracts: A significant portion of the customers who left the company were on a monthly contract. This indicates that customers on shorter-term commitments may have a higher tendency to churn compared to those on longer-term contracts.

• Payment Method: The analysis revealed that electronic check was the most common form of payment among customers who left the company. This finding suggests that the payment method could be a contributing factor to customer attrition.

• Demographic Profile: Among the demographics, it was observed that females without partners constituted the largest group of customers who chose to terminate their relationship with the company. This demographic insight may indicate specific challenges or preferences that this group of customers encountered during their association.

• Phone Services Subscribers: The analysis also highlighted that the highest number of customers who decided to leave the company were subscribers of Phone Services. This observation indicates a potential issue or dissatisfaction related to the phone services offered by the company, which may have influenced their decision to switch providers.