Enhancing E-commerce Strategies Using Decision Tree Modeling with KNIME

Introduction

In the rapidly evolving world of e-commerce, understanding customer behavior is crucial for enhancing sales and improving customer satisfaction. One effective way to achieve this is by using decision tree modeling to analyze factors influencing purchase decisions. This article demonstrates how KNIME, a powerful data analytics platform, can be utilized to build a decision tree model using a dataset from Kaggle, which includes factors such as holidays, discounts, and free delivery. The dataset can be accessed here.

Data Preparation

We begin with a dataset containing 30 rows and four columns: Holiday, Discount, Free Delivery, and Purchase. Each column contains categorical data represented by 'Yes' or 'No'.

1. Table Creator: The initial dataset is created using the Table Creator node with the specified columns and rows.
2. Case Converter: To ensure uniformity, all text data is converted to uppercase using the Case Converter node.

Data Partitioning

Next, we partition the data into training and testing sets:

1. Partitioning: The data is split randomly into a training set (70%) and a test set (30%) to ensure that the model is trained and validated effectively.

Model Training and Prediction

The core of the process involves training the decision tree model and making predictions:

1. Decision Tree Learner: The Decision Tree Learner node is configured with 'Purchase' as the class column and the Gini Index as the quality measure. Pruning is enabled with a minimum of two records per node to prevent overfitting.
2. Decision Tree Predictor: This node applies the trained model to the test set, creating predictions in a new column named 'Prediction (Purchase)'.

Visualization and Evaluation

To understand the model's performance and visualize the decision-making process:

1. Decision Tree View: This node provides an interactive view of the decision tree, helping to identify how different factors influence purchase decisions. The tree's title and subtitle are customized to provide clear insights.
2. Scorer: The Scorer node evaluates the model's performance by generating a confusion matrix and accuracy statistics. The confusion matrix for our model is as follows:

The model achieved a high accuracy, with the majority of predictions being correct.

Confusion Matrix Explanation

The confusion matrix provides detailed insights into the model's performance:

• True Positives (Yes, Yes): 6 instances where the model correctly predicted 'Yes'.
• True Negatives (No, No): 2 instances where the model correctly predicted 'No'.
• False Positives (Yes, No): 0 instances where the model incorrectly predicted 'Yes' when the actual value was 'No'.
• False Negatives (No, Yes): 1 instance where the model incorrectly predicted 'No' when the actual value was 'Yes'.

Results and Insights

The decision tree model reveals interesting insights into customer behavior:

• Free Delivery: The root node of the decision tree indicates that free delivery is a significant factor influencing purchases.
• Discounts: Further splits show that discounts also play a crucial role, particularly when free delivery is not offered.

By understanding these patterns, e-commerce businesses can tailor their marketing strategies to optimize sales, such as offering free delivery and discounts during key periods.

Analysis of Predictions Using Decision Tree in KNIME

The table above shows test data and predictions made by the decision tree model. Here's a concise analysis:

Summary:

• High Accuracy: The model correctly predicted 8 out of 9 instances.
• Influence of Free Delivery: Rows with 'YES' for Free Delivery mostly resulted in 'YES' predictions, highlighting its strong influence on purchases.
• Role of Discounts: Discounts combined with Free Delivery generally led to correct 'YES' predictions, indicating their significant impact.

Key Observations:

• Correct Predictions: Rows 0, 2, 7, 9, 14, 16, and 20 were accurately predicted.
• Incorrect Prediction: Row 25 was incorrectly predicted as 'YES' instead of 'NO', likely due to the influence of Free Delivery.

Conclusion

The decision tree model in KNIME effectively captures the key factors influencing customer purchases. Free delivery and discounts are major drivers, significantly impacting the prediction of purchase decisions. The high accuracy of the model, with only one incorrect prediction in the provided subset, showcases its reliability and usefulness for e-commerce businesses looking to optimize their strategies. By understanding these patterns, businesses can tailor their marketing efforts, focusing on offering free delivery and discounts to boost sales.

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