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This summarization enables analysts to extract meaningful statistics, identify trends, and gain a comprehensive understanding of the dataset, facilitating subsequent analysis and decision-making across diverse domains.\n\n![Untitled.png](attachment:419b139b-06e7-449a-a200-97e23a6e6dfc.png)\n  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"}}},{"cell_type":"markdown","source":"**RFM analysis:** is a powerful **marketing** technique used by businesses to **segment** and **understand** their customer base. The abbreviation stands for **Recency**, **Frequency**, and **Monetary** value, which are the three key factors considered in this analysis. \n* Recency refers to how recently a customer has made a purchase, with the idea being that recent customers are more likely to make additional purchases. \n* Frequency measures how often a customer makes purchases, indicating their level of engagement with the brand. \n* Monetary value assesses the amount of money a customer spends, highlighting high-value customers.  \n\nBy analyzing these three metrics together, businesses can **identify their most valuable customers**, tailor **marketing strategies to specific segments**, and maximize the return on investment in their marketing efforts. RFM analysis enables companies to **allocate resources effectively**, **personalize communications**, and ultimately **enhance customer satisfaction** and **loyalty**.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\ndf = pd.read_csv('/kaggle/input/rfm-dataset/aggregate.csv')\ndf.info()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# First, ensure that the 'Date' column is of datetime type\ndf['Date'] = pd.to_datetime(df['Date'], format='%Y%m%d')\ndf.info()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Construct RFM Metrics\nIn constructing Recency, Frequency, and Monetary (RFM) metrics from **transaction datasets**, businesses typically employ **groupby** or **aggregation** methods to distill valuable insights about their customers.   \n* Recency is determined by calculating the time elapsed since each customer's last transaction, often measured in days or months.   \n* Frequency is derived by counting the total number of transactions made by each customer within a specified period, providing insight into their purchasing habits and **engagement level**.   \n* Monetary value is computed by summing the total monetary amount spent by each customer, reflecting their **contribution to revenue**.   \n\nThrough groupby or aggregation techniques, businesses can efficiently **summarize transactional data**, enabling them to identify **patterns**, **segment customers** based on their RFM scores, and tailor marketing strategies accordingly. ","metadata":{}},{"cell_type":"code","source":"# You can calculate Recency, Frequency, Monetary, and Gender based on aggregation on the ID column\n# Recency can be calculated as the difference between the latest date and each date for each ID\nrecency = df.groupby('ID')['Date'].max().apply(lambda x: (df['Date'].max() - x).days)\n\n# Frequency can be calculated as the count of transactions for each ID\nfrequency = df.groupby('ID').size()\n\n# Monetary can be calculated as the sum of monetary values for each ID\nmonetary = df.groupby('ID')['Monetary'].sum()\n\n# Gender can be taken as the mode value for each ID\ngender = df.groupby('ID')['Gender']..apply(lambda x: list(x.mode()))\n\n# Now, you can create a new dataframe with these calculated values\nagg_df = pd.DataFrame({\n    'Recency': recency,\n    'Frequency': frequency,\n    'Monetary': monetary,\n    'Gender': gender\n}).reset_index()\n\n# You can print or use agg_df as needed\nprint(agg_df)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Other Metrics\nIn addition to Recency, Frequency, and Monetary (RFM) metrics, other metrics such as **duration**, **frequency_duration_ratio**, and **duration_frequency_ratio** can be valuable in RFM analysis for **deeper customer insights**.   \n* Duration represents the **span of time** between a customer's first and last transaction, offering a comprehensive view of their **engagement** over a given period.   \n* The frequency_duration_ratio, calculated as the ratio of transaction frequency to duration, provides insight into **how often** a customer makes purchases relative to the overall time span they've been active.   \n* The duration_frequency_ratio, calculated as the ratio of duration to frequency, reveals the average time lapse between transactions, indicating the **spacing or intensity** of a customer's purchasing behavior.  \n\nIncorporating these additional metrics alongside RFM allows businesses to refine customer segmentation further, **better understand customer behavior patterns**, and tailor marketing strategies more effectively to enhance overall performance and customer satisfaction.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\n\n# Calculate the duration of the minimum and maximum transactions for each ID\nmin_date = df.groupby('ID')['Date'].min()\nmax_date = df.groupby('ID')['Date'].max()\nduration = (max_date - min_date).dt.days + 1\n\n# Calculate indicators frequency/duration and duration/frequency\nfrequency_duration_ratio = frequency / duration\nduration_frequency_ratio = duration / frequency\n\n# Create a new dataframe with these calculated values\ncomplete_agg_df = pd.DataFrame({\n    'Recency': recency,\n    'Frequency': frequency,\n    'Monetary': monetary,\n    'Gender': gender,\n    'Duration': duration,\n    'Frequency_Duration_Ratio': frequency_duration_ratio,\n    'Duration_Frequency_Ratio': duration_frequency_ratio\n}).reset_index()\n\n# You can print or use agg_df as needed\nprint(complete_agg_df)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Create RFM Scores\nCreating RFM scores involves **categorizing** Recency, Frequency, and Monetary metrics into numerical values ranging from 1 to 5 to effectively segment customers based on their purchasing behavior. Typically, **higher scores indicate better performance** or higher value.   \n* For Recency, customers with the most recent transactions receive a score of 5, while those with the longest intervals since their last purchase receive a score of 1, thus **reversing** the scoring logic to emphasize recent activity.   \n* Frequency and Monetary values are also divided into quintiles, with the top quintile receiving a score of 5 and the bottom quintile receiving a score of 1.   \n\nBy assigning these scores businesses can easily identify and prioritize their **most valuable customers** for **targeted marketing efforts**, retention strategies, and personalized experiences, ultimately driving growth and profitability.","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import KBinsDiscretizer\n\n# Define the discretizer\ndiscretizer = KBinsDiscretizer(n_bins=5, encode='ordinal', strategy='quantile')\n\n# Fit and transform the features\nagg_df['Recency_Score'] = discretizer.fit_transform(agg_df[['Recency']]) + 1\nagg_df['Frequency_Score'] = discretizer.fit_transform(agg_df[['Frequency']]) + 1\nagg_df['Monetary_Score'] = discretizer.fit_transform(agg_df[['Monetary']]) + 1\n\n# Reverse scores in Recency\nagg_df['Recency_Score'] = 5 - agg_df['Recency_Score']","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Calculate CLV Score  \nCalculating **Customer Lifetime Value (CLV)** involves estimating the total value a customer is expected to generate for a business over their entire relationship. One method to compute CLV involves using a weighted mean of RFM scores. First, RFM scores are assigned to each customer based on their recency, frequency, and monetary value metrics. Then, each RFM score is multiplied by a **corresponding weight** representing its **importance** in driving long-term value. For instance, recency might carry a higher weight as recent customers are more likely to make future purchases. By summing these weighted RFM scores, businesses can derive a CLV score that reflects the **overall value of each customer**. This approach allows companies to prioritize their resources and tailor strategies towards high CLV customers, thereby **maximizing profitability** and fostering sustainable growth.","metadata":{}},{"cell_type":"code","source":"# Define the weights for each score\nrecency_weight = 0.2\nfrequency_weight = 0.3\nmonetary_weight = 0.5\n\n# Calculate the weighted mean\nagg_df['RFM_Score'] = (agg_df['Recency_Score'] * recency_weight + \n                       agg_df['Frequency_Score'] * frequency_weight + \n                       agg_df['Monetary_Score'] * monetary_weight)\n\n# Print or use agg_df as needed\nprint(agg_df)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Clustering for RFM Analysis  \nAnalyzing RFM scores through clustering offers a **multidimensional approach** to understanding **customer behavior patterns**, enabling businesses to **segment** their customer base effectively. By employing clustering algorithms such as K-means or hierarchical clustering, customers can be grouped into distinct clusters based on **similarities** in their RFM scores. This process reveals **hidden patterns** and insights within the data, allowing businesses to identify segments with different purchasing behaviors, preferences, and needs. Once clusters are established, businesses can tailor **marketing campaigns** and **strategies** to suit the unique characteristics of each group.   \nFor example, **high-value clusters** may be targeted with **loyalty programs** or **personalized promotions** to encourage repeat purchases, while **low-value clusters** may receive targeted offers to **re-engage** them.   \nOverall, leveraging RFM clustering analysis enhances marketing effectiveness, customer satisfaction, and ultimately drives revenue growth.","metadata":{}},{"cell_type":"markdown","source":"--------------------------------------------------------------------------------------------------------------------  \n### Useful Functionss for Dates  \nWhen analyzing data, **dates in their raw format** may not be as useful because a date alone **lacks repeatability** and **doesn't conform** to recognizable patterns for analysis. To address this limitation, it's essential to **extract various components** from dates or **compute differences between them** to create **meaningful features** for models. By breaking down dates into components such as year, month, day, day of the week, and week of the year, we can derive **more informative variables** for analysis. Additionally, calculating the difference between two dates allows us to capture temporal relationships and durations, which can be valuable for modeling purposes. These extracted components and calculated differences serve as vital features in data analysis and modeling tasks, facilitating deeper insights and more accurate predictions.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\ndf_Date = pd.read_csv('/kaggle/input/rfm-dataset/aggregate.csv')\n\n# Convert the 'Date' column to datetime if it's not already\ndf_Date['Date'] = pd.to_datetime(df_Date['Date'], format='%Y%m%d')\n\n# Extract year, month, day, day of week, week of year from the 'Date' column\ndf_Date['Year'] = df_Date['Date'].dt.year\ndf_Date['Month'] = df_Date['Date'].dt.month\ndf_Date['Day'] = df_Date['Date'].dt.day\ndf_Date['Day_of_Week'] = df_Date['Date'].dt.dayofweek\ndf_Date['Week_of_Year'] = df_Date['Date'].dt.isocalendar().week\n\n# Print or use df as needed\nprint(df_Date)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\n\n# Sample dates\ndate1 = pd.to_datetime('2024-03-12')\ndate2 = pd.to_datetime('2024-01-01')\n\n# Calculate the difference between the dates\ndifference = date1 - date2\n\n# Extract differences \ndifference_days = difference.days\n\n# Print the difference\nprint(\"Difference in days:\", difference_days)","metadata":{"execution":{"iopub.status.busy":"2024-04-17T18:49:02.112911Z","iopub.execute_input":"2024-04-17T18:49:02.114328Z","iopub.status.idle":"2024-04-17T18:49:02.123859Z","shell.execute_reply.started":"2024-04-17T18:49:02.114281Z","shell.execute_reply":"2024-04-17T18:49:02.122958Z"},"trusted":true},"execution_count":null,"outputs":[]}]}