Customer Segmentaton using RFM analysis

Data Cleaning

• Checked for missing data.Filled the null CustomerID values with 10 frequent values and equally distributed them.
• Removed duplicate data records.

Data Transformation

• Performed Cohort analysis. Analyzed active customers for each cohort.
• Analyzed Retention Rate of the customers.

Data modelling(RFM analysis)

• Built an RFM (Recency Frequency Monetary) model(Period:1 year)
• Calculated RFM metrics. Added them(as strings) get an RFM segment. Also, added the individual scores in order to get an RFM score.
• Analyzed the RFM segments and commented on the findings.

Data modelling(KMeans)

• Prepared the data for the algorithm. If the data was asymmetrically distributed, managed the skewness with log transformation. Scaled the data using StandardScaler.
• Found the optimal number of clusters(elbow method). Performed clustering using Kmeans.
• Analysed the clusters and commented on the findings.

Data visualization(Dashboard link)

• Country-wise analysis to demonstrate average spend. Used a bar chart to show the monthly figures.
• Made a Bar graph of top 15 products which were mostly ordered by the users to show the number of products sold
• Made a Bar graph to show the count of orders vs. hours throughout the day
• Plotted the distribution of RFM values using histogram
• Visualized to compare the RFM score of the clusters using heatmap