Customer Segmentation Report for Arvato Financial Services

Table of Contents

• 1. Installation
• 2. Introduction
• 3. File Descriptions
• 4. Results
  • 4.1. Conclusions
  • 4.2. Future Improvements
  • 4.3. Technical Improvements
• 5. Acknowledgemnts

1. Installation

I developed this project using Python 3.11.3 and list libraries that I've been used are:

- numpy
- pandas
- matplotlib.pyplot
- seaborn
- %matplotlib inline
- openpyxl
- load_workbook
- psutil
- stats from scipy
- MinMaxScaler from sklearn.preprocessing
- PCA from sklearn.decomposition
- tqdm
- KMeans from sklearn.cluster
- MiniBatchKMeans from sklearn.cluster
- silhouette_score from sklearn.metrics
- train_test_split from sklearn.model_selection
- andomForestClassifier from sklearn.ensemble
- GridSearchCV from sklearn.model_selection
- pickle
- confusion_matrix from sklearn.metrics
- roc_auc_score from sklearn.metrics
- LogisticRegression from sklearn.linear_model
- warnings
- xgb from xgboost
- roc_curve, roc_auc_score from sklearn.metrics

These libraries have been used for various purposes throughout the project, including data manipulation, visualization, machine learning, evaluation, and more.

2. Introduction

In this project, we'll analyze demographics data for customers of a mail-order sales company in Germany, comparing it against demographics information for the general population. Then, we use unsupervised learning techniques to perform customer segmentation, and identifying the parts of the population that best describe the core customer base of the company. Last, we'll use the informations for target marketing campaign, and use model to predict which individuals are most likely to become customers.

3. File Descriptions

• Jupyter Notebook that contain work related for this project was uploaded using .ipynb file.
• If you would like to access the datasets to explore more analysis, you can find data folder contains DIAS Attributes - Values 2017.xlsx , DIAS Information Levels - Attributes 2017.xlsx , random_forest_model.pkl , and logreg_model.pkl.
• To access all details about project workspace and datasets that I mentioned above, you can find Arvato_Project_Workbook.ipynb.

4. Results

These conclusions and recommendations provide valuable insights to improve marketing and services strategy. Data-driven approcah helps us make informed decisions, it's lead to sustainable growth & maximized Business ROI (Return on Investment).

4.1. Conclusions

While all models show strong accuracy, the Logistic Regression model stands out for it's balanced performance between accuracy and ROC AUC. It effectively identifies potential customers while minimizing the risk of false positives, making it a suitable choice for targeted marketing efforts. However, continuous monitoring and model refinement are crucial to ensure the highest accuracy in predicting customer behavior.

Here's summarized conclusions that can support decision-making for the business:

• Understand Customer Behavior: We've discover into customers behaviour and uncover specific patterns. This helps for precise segmentation, understand diverse unique needs, and build specific strategies for marketing.
• Predict and Prevent Problems: Our models can predict potential issues like customers left or not responds well. This allow us take proactive steps by stop problems before they become significant. It's like have early 'warning system' that keep engagement level and customers satisfaction.
• Optimize Resource Allocation: Use our resources wisely. We can know where to invest marketing and sales efforts for the best results. Smart allocation ensure that every penny spent worth significant result, and impact to operational efficiency.
• Continuous Improvement & Adaptability: We continuously learn from new data and making strategies sharper. By keep the methods up-to-date, we stay in-sync with what customers wants. This flexibility allows to adapt on changing trends and keeps us ahead of the game.
• Enhanced Customer Experience: From the insights we can create personalized experience for customers. We know what they like, when they need assistance, and what products or services suit them best. This approach lead to 'happy customers' and improve loyalty, and loyal customers are key for long-term business.

These conclusions and recommendations provide valuable insights to improve offer strategy, customer engagement, and marketing effectiveness in offer's utilization. These will leading to maximize business ROI (Return of Investments).

4.2. Future Improvements

• Keep Updated Real-Time Data: By incorporate real-time data, we can flow with the latest trend. This helps us respond fast and align our actions with what customers want right now.
• Enchance Data Knowledge: Encourage our team to understand and use data effectively. When feels comfortable with data, we can create workplace where decisions are made based on solid information.
• Explore Advanced Analytics: Techniques like predictive analytics, sentiment analysis, and mapping out customer journey reveal detailed information. These insights help us see hidden opportunities and potential challenges.
• Collaboration is Key: Insights from marketing can shape product development, and customer service data can guide marketing strategies. Combine data cross-department are essential, also lead to see bigger picture of our business.

In simple-terms, using data helps improve our business. We can make smart decisions to stay ahead of the competition.

4.3. Technical Improvements

• Review Data Quality
• Feature Engineering
• Model Improvement
• Class Imbalance
• Domain Expert Consultation
• Regular Model Evaluation
• Explore External Factors
• Customer Segmentation
• Experimentation and Prototyping

By focusing on these areas, the business can develop reliable predictive models, provide solid foundation for data-driven decision-making, and improve overall business results.

5. Acknowledgemnts

• The Datasets used in this project's provided by Bertelsmann Arvato. Thankyou for their contribution for making this data available for analysis.
• Thankyou for Udacity "Data Science Nanodegree Program" which granted access to this dataset, also provided valuable knowledge and resources.
• This project's completed by Bernhard A. Alphama.