Project Info

A predictive analytics dashboard for customer segmentation can be used across various industries where understanding and predicting customer behavior is crucial. Predicting customer buying patterns, optimizing inventory, and personalizing marketing strategies based on customer segment Enhancing user experience, reducing cart abandonment, and recommending personalized products to increase sales
  • Industry Retail
  • Location United States
  • Date 11 January 2023
  • Size 50-100

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.
    •  
      • 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.
    •  
      • 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.