🔎 Chapter 1: Data Preparation for Analysis
The success of customer behavior analysis hinges on the quality and preparation of the data. First, we will examine the types of data used for analysis and the importance of preprocessing.
1.1. Key Customer Behavior Data
The main types of data used for analysis include:
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Transaction Data: Purchase history, order date/time, purchase amount, product category, etc.
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Web/App Log Data: Page view history, time spent on pages, clicks, search keywords, etc.
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CRM Data: Customer attributes (age, gender, location), inquiry history, etc.
1.2. Steps in Data Preprocessing
To improve analysis accuracy, all raw data requires the following preprocessing steps:
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Data Cleaning: Handling missing values and outliers, and removing duplicate data.
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Data Integration: Merging multiple data sources (e.g., transactions, web logs) using the Customer ID as a key.
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Feature Engineering: Creating new variables suitable for analysis (e.g., days since last purchase, purchase frequency).
🔬 Chapter 2: Key Analytical Methods for Discovering Customer Insights
Once the data is ready, we use statistics and machine learning techniques to analyze customer behavior.
2.1. Customer Segmentation (Clustering)
This method classifies customers into groups with similar behavioral patterns. This allows for marketing strategies optimized for each specific group.
| Method | Purpose | Primary Algorithm |
| RFM Analysis | Evaluating customer value | Recency (days since last purchase), Frequency (purchase frequency), Monetary (total spend) |
| K-means Clustering | Statistical grouping of customers | Automatically forms clusters based on customer behavior patterns |
| LTV Analysis | Calculating Customer Lifetime Value | Predicting the future profit a customer will generate for the company |
2.2. Association Analysis (Market Basket Analysis)
This method discovers “which products are frequently bought together” and is used for product placement and recommendation systems.
Example Finding: “Customers who buy beer tend to also purchase snacks and diapers.”
2.3. Churn Prediction Analysis
Machine learning models (e.g., Decision Trees, Logistic Regression) are used to predict which customers are likely to leave the service soon. This enables early intervention for at-risk customers (e.g., offering a special discount).
🚀 Chapter 3: Applying Analytical Results to Business
Data analysis should not end merely with reporting; it must be tied to concrete business actions.
3.1. Personalization Initiatives (List)
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Product recommendations tailored to the preferences of each segment.
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Special coupon distribution to customers identified by the churn prediction model.
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Optimization of website content display (verified through A/B testing).
3.2. Strategic Decision Making
Analysis is also utilized for more macro-level management decisions, such as optimizing pricing, prioritizing new product development, and optimizing store layouts.
🔗 Resources and Data Science Learning Materials
Resources to help you deepen your understanding of customer behavior analysis.
Useful Links
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Introduction to Data Science (Japan Data Science Society) – Society information and learning guides
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Analysis Tutorials Using Python / R – (Fictitious link. Please change as appropriate)
Recommended Tools
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Programming Languages: Python (Pandas, Scikit-learn) or R
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Database: SQL (Structured Query Language)
✒️ Conclusion
Customer behavior analysis using data science serves as a compass for companies to make data-driven decisions and maximize customer satisfaction and revenue. By combining proper data preparation with advanced analytical methods, you can uncover the “why” behind customer actions and translate insights into the next strategic steps.
If you have any further questions about this customer behavior analysis, such as specific model building procedures or how to interpret analytical results, please feel free to ask.