Project Info

Objective:

Develop a predictive analytics dashboard using Tableau, integrated with machine learning models to predict and visualize customer churn patterns for a business.

Key Features:

1. Data Collection and Cleaning:

    • Gather historical customer data, including demographics, transaction history and customer interactions.
    • Clean and preprocess the data for analysis.

2. Exploratory Data Analysis (EDA):

    • Use Tableau to perform EDA and visualize key metrics related to customer behavior, engagement and satisfaction.
    • Identify patterns and correlations in the data.

3. Feature Engineering:

      • Create relevant features for predicting customer churn, such as customer tenure, frequency of purchases, customer support interactions, etc.
  2.  
      • Use Tableau to visualize the distribution of these features.

4. Machine Learning Model Training:

      • Develop machine learning models (e.g. logistic regression, random forest or gradient boosting) to predict customer churn based on historical data.
  2.  
      • Train the models using a portion of the dataset.

5. Integration with Tableau:

      • Export the trained machine learning model and integrate it into Tableau using Tableau’s External Services Integration.
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      • Create calculated fields in Tableau to invoke the ML model for predictions.

6. Churn Prediction Dashboard:

      • Build a Tableau dashboard that provides an overview of customer churn predictions.
  2.  
      • Include visualizations like customer churn rate trends, demographic breakdowns and feature importance.

7. Real-time Predictions:

      • Implement real-time prediction capabilities by integrating the Tableau dashboard with a backend system that continuously updates predictions as new data comes in.

8. Customer Segmentation:

      • Use clustering algorithms to segment customers based on behavior and preferences.
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      • Visualize customer segments and their churn probability in Tableau.

9. What-If Analysis:

      • Allow users to perform what-if analysis by adjusting different features (e.g. increasing customer support interactions) and observing the impact on predicted churn.

10. Alerts and Notifications:

      • Implement alerts or notifications within Tableau to notify stakeholders when specific customer segments show an increased likelihood of churn.

 

Technologies and Tools:

    • Tableau for data visualization and dashboard creation.
  2.  
    • Python or R for machine learning model development.
  3.  
    • Integration tools like Tableau’s External Services Integration.

Challenges:

    • Model Interpretability: Ensuring that the machine learning model’s predictions are interpretable and understandable to business stakeholders.
    • Data Quality: Handling missing or inconsistent data to improve model accuracy.
    • Scalability: Designing the solution to handle a growing dataset and real-time predictions efficiently.