Customer Classification

Project Title: Customer Classification and Incentivization for AE Corp Stock Trading Facility

Project Description:

Introduction:

AE Corp, a fictional retail bank, is poised to revolutionize its customer experience by launching a stock trading facility. To enhance customer engagement and profitability, AE Corp aims to classify customers into high net worth and low net worth groups. The company seeks to identify the top 10% of customers who engage in enough trades to make discounted offerings profitable. As a data science consultant, your role is pivotal in developing a robust classification model to achieve this objective and supplement AE Corp’s marketing campaigns with a proactive approach.

Objective:

The primary goal of this project is to create a data-driven classification model that distinguishes customers falling into Revenue Grid 1 (high net worth) and Revenue Grid 2 (low net worth). By identifying the high-value customers, AE Corp can selectively offer them discounts, incentivizing increased engagement and trading activity.

Project Scope:

- Analyzing past customer data and trading behavior.

- Building a classification model to categorize customers into high net worth and low net worth groups.

- Implementing proactive marketing strategies based on the model’s insights.

- Evaluating the effectiveness of the incentivization program through iterative analysis.

Approach:

1. Data Collection and Preprocessing:

- Gather historical data on 10,000+ customers and their trading behavior.

- Cleanse and preprocess the data, handling missing values and outliers.

2. Feature Selection and Engineering:

- Identify relevant features such as trading frequency, transaction volume, trading history, and customer demographics.

- Engineer new features to enhance model accuracy and relevance.

3. Model Development:

- Choose appropriate machine learning algorithms (e.g., logistic regression, random forest, or gradient boosting) for classification.

- Train the model using the preprocessed data, iteratively optimizing its performance.

4. Model Evaluation and Validation:

- Split the dataset into training and testing sets to evaluate the model’s accuracy, precision, recall, and F1-score.

- Utilize cross-validation techniques to ensure the model’s robustness and generalizability.

5. Proactive Marketing Strategy Implementation:

- Identify the top 10% of customers classified as high net worth.

- Design targeted marketing campaigns and discounts to incentivize this segment, encouraging increased trading activity.

6. Performance Monitoring and Optimization:

- Continuously monitor the impact of the incentivization program on customer engagement and profitability.

- Fine-tune the model and marketing strategies based on real-time feedback and customer responses.

Deliverables:

- Classification Model: A well-documented machine learning model capable of accurately categorizing customers into high net worth and low net worth groups.

- Proactive Marketing Strategy: Detailed insights and recommendations for targeted marketing campaigns and discount structures.

- Performance Reports: Regular reports showcasing the impact of the incentivization program on customer behavior and revenue generation.

Conclusion:

By leveraging data-driven insights and a proactive marketing approach, AE Corp aims to enhance customer satisfaction, increase trading activity, and maximize profitability. Your role as a data science consultant is instrumental in achieving these objectives, ultimately shaping AE Corp’s future success in the competitive retail banking industry.

You can view the project file at :- https://github.com/roshni-1/Customer-classificiation-