We so far have analyzed the Online Retail dataset with K-Means clustering and grouped customers based on their purchasing behavior. The process required

• cleaning the data,

• adding a feature ('SaleAmount') that can well-describe the purchasing behavior,

• normalization and scaling,

• finding the optimal number of clusters (with the elbow method & silhouette score) and

• fitting and refitting the model.

This analysis is typically for customer segmentation in marketing strategies and to understand customer behaviors. Some of the actions we can take upon this segmentation are:

1. Interpret the Clusters

2. Visualize the Clusters

   You can visualize the clusters in a 3D scatter plot using the three features ('Frequency', 'SaleAmount', and 'ElapsedDays'). This might give you a more intuitive understanding of the customer groups.

3. Customer Profiling

   You can further profile the customers within each cluster based on other available information (like demographics, preferred product categories, or preferred shopping times).

4. Develop Marketing Strategies

5. Predictive Modeling

   The cluster labels also can be a target variable when training a classification model. You can predict the cluster of new customers based on the existing cluster labels.

6. Repeat the Analysis

   Regularly repeat the clustering analysis (for example, every quarter or every year) to catch changes in customer behavior over time.

7. Experiment and Refine

   Experiment with different numbers of clusters, other clustering algorithms, and different features to segment on.

   I recommend to:

   • Add a small constant to the feature (especially to 'ElapsedDays' in this case) and train the model again, and see if you get a different result.

   • Drop the entries with outlier values or unusual behavior or modify them by adding a little offset or something to lessen the impact of outliers on the result. The method I got recommended from ChatGPT is Z-score method but I'm not including it in this series.

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You can save the result and do some interpreting and analysis-like. These are the additional steps but might still be required in writing papers or real-life scenarios.

# Drop columns before saving the result
customer_df = customer_df.drop(['Freq_log', 'SaleAmount_log', 'ElapsedDays_log', 
                                'Freq_scaled', 'SaleAmount_scaled', 'ElapsedDays_scaled'], axis=1)

customer_df.to_csv('./data/customer_segments.csv', index=False)

# Load the dataframe from a CSV file
cluster_df = pd.read_csv('./data/customer_segments.csv')

cluster_df.head()

Out:

# Calculate average spent per transaction
cluster_df['AvgSpent'] = cluster_df['SaleAmount'] / cluster_df['Freq']

cluster_df.head()

Out:

cluster_df.groupby('ClusterLabel').mean()

Out:

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That was the completion of k-means clustering process. References for Z-score method and RFM analysis below and the script from the posts.

• Example for implementation of Z-score measuring:

from scipy import stats

# Define a threshold for outliers
threshold = 3 # sets the minimum Z-score

# Calculate Z-scores
z_scores = stats.zscore(cluster_df[['Freq', 'SaleAmount', 'ElapsedDays', 'AvgSpent']])

# Get boolean mask of outliers
outliers = np.abs(z_scores) > threshold

# Check the outlier rows
cluster_df[outliers.any(axis=1)].info()

• Example of RFM segmentation: