E-Commerce Customer Segmentation and Behavior Prediction: A Data-Driven Strategy

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E-commerce Customer Segmentation and Prediction

Agenda
• Understanding the problem Statement and the data
• Data Cleaning and Preprocessing
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Customer Segmentation
• Predicting Model and Training
• Results and Performance Evaluation
• Conclusion

Understanding the problem statement
• E-commerce Customer Segmentation and Prediction is a data-driven
approach to understanding and predicting customer behavior in online
retail. It involves dividing customers into distinct groups based on shared
characteristics, such as demographics, purchase history, and browsing
behavior. This segmentation allows businesses to tailor marketing
campaigns, personalize product recommendations, and optimize customer
experiences.
• By leveraging predictive modeling techniques, businesses can forecast
future customer behavior, such as purchase likelihood, churn probability,
or product preferences. This enables them to proactively identify at-risk
customers, implement targeted retention strategies, and drive sales growth.

•Customer Segmentation: Dividing customers into distinct groups based on shared
characteristics.
•Predictive Modeling: Using data to forecast future customer behavior.
•Benefits:
•Personalized Marketing
•Improved Customer Retention
•Enhanced Customer Experience
•Optimized Inventory Management
•Increased Sales and Revenue

Data Cleaning and Preprocessing
• Handle missing values.
• Outlier detection and treatment.
For removing Outliers Z-Scores is used.

• Data normalization and standardization.
Standard scalar is used to normalize the RFM values
• Feature engineering (creating new features from existing ones).

Exploratory Data Analysis (EDA)

Feature Engineering
Extracting RFM Values

Recency (R)
• Definition: Measures how recently a customer made a purchase.
• Inference: Customers with low recency values (i.e., recent
purchases) are more engaged and likely to respond to marketing
efforts. High recency values indicate customers who haven't
purchased in a while and may need re-engagement strategies.

Frequency (F)
• Definition: Measures how often a customer makes a purchase.
• Inference: High frequency indicates loyal customers who make
repeated purchases. These customers are valuable and should be
nurtured. Low frequency suggests customers who may need
incentives to increase their purchase frequency.

Monetary (M)
• Definition: Measures the total amount of money a customer has
spent.
• Inference: High monetary values indicate high-value customers
who contribute significantly to revenue. These customers are
crucial for profitability and should receive special attention. Low
monetary values suggest customers who might need targeted
promotions to increase their spending.

Customer Lifetime Value (CLV)
• Definition: Estimates the total revenue a business can expect from
a customer over the entire relationship.
• Inference: High CLV indicates customers who are expected to
generate substantial revenue over time. These customers are the
most valuable and should be prioritized for retention efforts. Low
CLV suggests customers who may not be as profitable, and
businesses might need to evaluate the cost of retaining them
versus acquiring new customers.

Pair plots of customer ID with respect to RFM

What do we infer from RFM?
• Customer 1: Hasn't purchased recently (Recency = 29), but has a moderate
frequency and monetary value. They might need re-engagement strategies.
• Customer 2: Very recent purchase (Recency = 1), moderate frequency, and
high monetary value. They are highly engaged and valuable.
• Customer 3: Recent purchase (Recency = 10), moderate frequency, and
very high monetary value. They are also highly valuable and should be
prioritized for retention.

Customer Segmentation
• Different clustering methods are
used like K-means, Hierarchical
clustering, DBSCAN clustering
etc.
• From the above I chose k-means
for segmenting the customers
because its simple,efficient and
scales well to large datasets

• To find the optimal number of
clusters ( K value), I have used
the elbow method.
• A function is written to return
the K value

Click to edit
Master title
style
Model Prediction
• For prediction we can use different models,
here I have used Logistic regression,
DecisionTree, Random Forest and Support
Vector Machine(SVM).
• The accuracy for the models:
• Logistic Regression = 0.998476 (99.84%)
• Decision Tree = 0.997713 (99.73%)
• Random Forest = 0.994665 (99.46%)
• Support Vector Machine (SVM) = 0.813262

Result
With help of K-Means clustering we got 4
clusters, which is
• [0] - High-value customers
• [1] - Recent but low-frequency customers
• [2] - Frequent but low-monetary customers
• [3] - Inactive customers

Here the model predicts on the values of
Recency, Frequency and Monetary.
input from user,
Recency = 310
Frequency = 17
Monetary = 394
The output cluster is mapped to [1] which is
Recent but low frequency customer.

Insights for each cluster to improve the business,
[0] - High-value customers - Offer loyalty programs and exclusive discounts.
[1] - Recent but low-frequency customers - Send personalized recommendations.
[2] - Frequent but low-monetary customers - Upsell and cross-sell products.
[3] - Inactive customers - Re-engage with special offers and reminders.

Metrics
Confusion Matrix Classification Report

Visualizing the decision tree

Conclusion
1. Customer Value Segmentation
• We can identify high-value customers who contribute significantly to
revenue and should be targeted with loyalty programs and exclusive offers.
• Medium-value customers can be encouraged to increase their spending
through personalized recommendations and upselling strategies.
• Low-value customers may need re-engagement campaigns to boost their
purchasing frequency.
• Additionally, understanding purchasing patterns helps in optimizing
inventory management and planning targeted marketing campaigns,
ultimately enhancing customer satisfaction and retention.

2. Behavioral Patterns: Understanding the recency, frequency, and monetary
value of purchases helps in identifying active, loyal, and high-spending
customers. This allows for more effective and targeted marketing efforts.
3. Predictive Insights: The predictive model can forecast customer segments
for new or existing customers based on their purchasing behavior,
enabling proactive marketing and retention strategies.
4. Inventory Optimization: Insights into top-selling products and
purchasing patterns over time help in optimizing inventory management,
ensuring that popular products are always in stock and reducing overstock
of less popular items.
5. Marketing Efficiency: Tailoring marketing campaigns based on customer
segments improves the efficiency of marketing spend, ensuring that
resources are allocated to the most promising customer groups.

Questions ?

Thank You!

E-Commerce Customer Segmentation and Behavior Prediction: A Data-Driven Strategy

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