{"id":23127,"date":"2025-01-11T18:19:52","date_gmt":"2025-01-11T18:19:52","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/understand-your-customers-and-segmentation-express-analytics\/"},"modified":"2025-01-11T18:19:52","modified_gmt":"2025-01-11T18:19:52","slug":"understand-your-customers-and-segmentation-express-analytics","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=23127","title":{"rendered":"Understand Your Customers and Segmentation \u2013 Express Analytics"},"content":{"rendered":"


\n<\/p>\n

\n

Overview<\/strong><\/p>\n

The <\/span>RFM (Recency, Frequency, Monetary) analysis<\/b> is a powerful tool for understanding customer behavior<\/a> and segmenting customers based on their purchasing patterns<\/span>.<\/span><\/p>\n

[ez-toc]<\/p>\n

It is based on three key metrics:<\/span><\/p>\n

    \n
  1. Recency: How recently a customer made a purchase<\/li>\n
  2. Frequency: How often a customer makes purchases<\/li>\n
  3. Monetary: How much money a customer spends on purchases<\/li>\n<\/ol>\n

    This analysis helps businesses identify their most valuable customers, tailor marketing strategies, and optimize customer relationship management<\/span>.<\/span><\/p>\n

    Approach<\/b><\/p>\n

    Our approach to RFM analysis involved the following steps:<\/span><\/p>\n

    Exploratory Data Analysis (EDA)<\/span><\/em><\/p>\n

    RFM Metrics Calculation<\/span><\/em><\/p>\n

    RFM Scoring<\/span><\/em><\/p>\n

    \"RFM<\/p>\n

    Fig 1. A heatmap showing the distribution of customers across different RFM score combinations<\/span><\/p>\n

    Customer Segmentation using various clustering algorithms<\/em><\/strong><\/p>\n

    Model Evaluation and Comparison<\/em><\/strong><\/p>\n

    Customer Profiling<\/em><\/strong><\/p>\n

    \"Customer<\/p>\n

    Fig 2. Radar Chart of Customer Profiles to compare the characteristics of each customer segment<\/span><\/p>\n

    Sources<\/strong><\/p>\n

    The analysis was performed on the Online Retail dataset, which is a transactional data set which contains all the transactions occurring between 01\/12\/2010 and 09\/12\/2011 for a UK-based and registered non-store online retail.<\/p>\n

    The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers. The dataset includes information such as:<\/p>\n

      \n
    1. InvoiceNo<\/li>\n
    2. StockCode<\/li>\n
    3. Description<\/li>\n
    4. Quantity<\/li>\n
    5. InvoiceDate<\/li>\n
    6. UnitPrice<\/li>\n
    7. CustomerID<\/li>\n
    8. Country<\/li>\n<\/ol>\n
      \n

      Win New Customers with Customer Journey Mapping<\/b><\/h4>\n<\/p>\n<\/div>\n

      Algorithms Used
      <\/b>[subscribe_to_unlock_form]
      <\/b><\/p>\n

      K-Means Clustering<\/b><\/h4>\n

      Purpose<\/strong>: K-Means is used to partition customers into distinct groups based on their RFM scores, aiming to minimize within-cluster variance.<\/p>\n

      Method<\/strong>: The optimal number of clusters was determined using the Elbow Method, resulting in 4 clusters.<\/p>\n

      The algorithm iteratively assigns customers to the nearest cluster centre and adjusts these centre\u2019s to minimize the variance within each cluster.<\/p>\n

      \"K-Means<\/p>\n

      Fig 3. Elbow Method \u2013 the approach used to identify the optimal number of Customer Clusters.<\/p>\n

      Result<\/strong>: The K-Means clustering produced well-defined groups, with a Silhouette Score of 0.6114, indicating a good separation between clusters.<\/p>\n

      Hierarchical Clustering<\/b><\/h4>\n

      Purpose<\/strong>: Hierarchical Clustering is used to build a hierarchy of clusters, allowing for a flexible choice in the number of clusters by cutting the dendrogram at different levels.<\/p>\n

      Method Applied<\/strong>: Ward\u2019s linkage method was employed to minimize the variance within clusters.<\/p>\n

      A dendrogram was created to visually assess the appropriate number of clusters, leading to a 4-cluster solution.<\/p>\n

      Result Obtained<\/strong>: The resulting clusters were similar to those from K-Means, with a Silhouette Score of 0.5893.<\/p>\n

      This method provided a clear visual representation of the customer hierarchy.<\/p>\n

      \"Hierarchical<\/p>\n

      Fig 4. Dendrogram for Hierarchical Clustering showcasing the hierarchical relationships between customers<\/span><\/p>\n

      DBSCAN Clustering<\/strong><\/h4>\n

      Purpose<\/strong>: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identifies clusters of varying shapes and sizes while also recognizing outliers as noise, which is particularly useful for identifying anomalous customer behaviors<\/a>.<\/p>\n

      Method Applied<\/strong>: DBSCAN was applied with an epsilon of 0.5 and a minimum sample size of 5.<\/p>\n

      The algorithm clusters customers based on density, with points in dense regions forming clusters, while sparse regions are considered noise.<\/p>\n

      Result Obtained<\/strong>: DBSCAN achieved a Silhouette Score of 0.6561, effectively identifying core clusters and outliers.<\/p>\n

      It proved to be useful in distinguishing customers with unusual purchasing patterns.<\/p>\n

      \"DBSCAN<\/p>\n

      Fig 5. DBSCAN Clusters depicting the identified clusters and noise points<\/span><\/p>\n

      Gaussian Mixture Model<\/b><\/h4>\n

      Purpose<\/strong>: GMM is used to model the data as a mixture of multiple Gaussian distributions, offering a probabilistic approach to cluster assignment, which allows for soft clustering.<\/p>\n

      Method Applied<\/strong>: The algorithm was applied with 4 components, representing the number of clusters.<\/p>\n

      GMM estimates the probability that each customer belongs to a particular cluster, providing a flexible clustering solution.<\/p>\n

      Result Obtained<\/strong>: The GMM produced a Silhouette Score of 0.1213, which was lower than the other methods, indicating some overlap between clusters.<\/p>\n

      However, it offered valuable insights into the probabilistic nature of customer behavior.<\/p>\n

      \"Gaussian<\/p>\n

      Fig 6. GMM Clusters depicting the overlap between clusters based on Probability<\/span><\/p>\n

      Decision Tree Classifier<\/b><\/h4>\n

      Purpose<\/strong>: Decision Trees are used in RFM analysis to create interpretable rules for customer segmentation.<\/p>\n

      By analyzing the RFM data, Decision Trees identify key thresholds for Recency, Frequency, and Monetary values that can be used to classify customers into different segments.
      Method<\/strong>: A Decision Tree classifier was trained on the RFM data, with the customer clusters (from K-Means) as the target variable.<\/p>\n

      The tree was pruned to avoid overfitting, ensuring that the resulting rules were both accurate and generalizable.<\/p>\n

      Result:<\/strong><\/p>\n

        \n
      1. The Decision Tree produced a set of clear, interpretable rules that can be used to classify new customers based on their RFM scores.<\/li>\n
      2. The structure of the tree revealed the most important features and thresholds for distinguishing between different customer segments.<\/li>\n
      3. The confusion matrix showed that the Decision Tree performed well in classifying customers into their respective clusters, with a high accuracy rate.<\/li>\n<\/ol>\n

        \"Decision<\/p>\n

        Fig 7. The confusion Matrix gives a comparison between actual and predicted values. The balanced performance across all segments means that the model can be confidently used for customer segmentation based on RFM Scores.<\/p>\n

        \n

        Win New Customers with Customer Journey Mapping<\/b><\/h4>\n<\/p>\n<\/div>\n

        Model-Wise Conclusion<\/strong><\/h4>\n

        K-Means Clustering<\/b><\/p>\n

        Performance<\/strong>: K-Means provided well-separated clusters with a high silhouette score, making it a reliable method for segmenting customers<\/a>.
        Cluster Insights<\/strong>:<\/p>\n

          \n
        1. Cluster 1 (High Value): Customers with high frequency and monetary value but low recency, ideal for loyalty programs.<\/li>\n
        2. Cluster 2 (Low Value): Customers with low frequency and monetary value, suitable for re-engagement strategies.<\/li>\n<\/ol>\n

          Hierarchical Clustering<\/b><\/p>\n

          Performance:<\/b> Hierarchical clustering also provides well-defined clusters, similar to K-Means, and is useful for cases where a dendrogram is needed for better cluster understanding.<\/span><\/p>\n

          DBSCAN Clustering<\/b><\/p>\n

          Performance:<\/b> DBSCAN effectively identified noise and outliers<\/a>, resulting in a high silhouette score. <\/span><\/p>\n

          However, it may not be ideal for datasets with continuous customer engagement<\/a>.<\/span><\/p>\n

          Gaussian Mixture Model<\/b><\/p>\n

          Performance:<\/b> GMM provided more flexible clustering but had the lowest silhouette score, indicating less distinct clusters.
          [\/subscribe_to_unlock_form]
          <\/span><\/p>\n

          Result Summary<\/b><\/h4>\n
            \n
          1. RFM Scoring successfully categorized customers based on their Recency, Frequency, and Monetary values.<\/li>\n
          2. K-Means and Hierarchical Clustering provided the most interpretable and well-separated clusters.<\/li>\n
          3. DBSCAN showed the highest silhouette score, indicating its effectiveness in identifying distinct customer groups.<\/li>\n
          4. The Decision Tree Classifier demonstrated high accuracy in predicting K-Means clusters, offering interpretable rules for customer segmentation.<\/li>\n
          5. Customer profiles were created based on the K-Means clusters, revealing distinct characteristics for each segment.<\/li>\n<\/ol>\n

            Recommendations<\/b><\/p>\n

            Focus on High-Value Customers<\/b>
            <\/b><\/h4>\n

            Action:<\/b> Prioritize marketing efforts and personalized services<\/a> for customers in clusters with high Frequency and Monetary values. <\/span>
            <\/span><\/p>\n

            Rationale:<\/b> Customers who frequently purchase and have a high monetary value represent the most profitable segment of the customer base<\/a>. <\/span><\/p>\n

            By focusing marketing efforts on these high-value customers, you can increase their loyalty, maximize their lifetime value<\/a>, and encourage repeat purchases<\/span>.<\/span><\/p>\n

            Re-Engagement Campaigns<\/b>
            <\/b><\/h4>\n

            Action:<\/b> Design targeted campaigns for customers with high Recency scores to bring them back to active status.<\/span>
            <\/span><\/p>\n

            Rationale:<\/b> High Recency scores indicate that a customer has not made a purchase recently. <\/span><\/p>\n

            By targeting these customers with re-engagement campaigns, such as special offers or personalized messages, you can encourage them to return and make new purchases, thereby reducing churn<\/a> and increasing retention<\/span>.<\/span><\/p>\n

            Cross-Selling and Upselling<\/b>
            <\/b><\/h4>\n

            Action:<\/b> Utilize the customer profiles<\/a> from different clusters to identify opportunities for cross-selling and upselling products. <\/span>
            <\/span><\/p>\n

            Rationale:<\/b> By understanding the purchasing behavior and preferences of each customer segment, you can tailor your cross-selling and upselling strategies. <\/span><\/p>\n

            This not only increases the average order value but also enhances customer satisfaction<\/a> by offering relevant products that meet their needs.<\/span><\/p>\n

            Loyalty Programs<\/b>
            <\/b><\/h4>\n

            Action:<\/b> Develop or refine loyalty programs based on the characteristics of the most valuable customer segments.<\/span><\/p>\n

            Rationale:<\/b> Loyalty programs can significantly increase customer retention and lifetime value<\/a>, particularly for high-frequency and high-monetary customers. <\/span><\/p>\n

            By offering rewards and incentives that appeal to these segments, you can foster long-term loyalty and encourage ongoing engagement.<\/span><\/p>\n

            Personalized Marketing<\/b><\/h4>\n

            Action:<\/b> Use the Decision Tree rules to create easily interpretable customer segments for tailored marketing strategies.<\/span><\/p>\n

            Rationale:<\/b> Decision Trees provide clear rules for segmenting customers based on their RFM scores.<\/span><\/p>\n

            These rules can be used to design personalized marketing strategies that are more likely to resonate with each segment, leading to higher conversion rates and better customer experiences.<\/span><\/p>\n

            Churn Prevention<\/b>
            <\/b><\/h4>\n

            Action:<\/b> Monitor customers moving towards higher Recency scores and implement retention strategies.<\/span><\/p>\n

            Rationale:<\/b> Customers with increasing Recency scores are at a higher risk of churning. <\/span><\/p>\n

            By identifying these customers early and implementing retention strategies\u2014such as targeted offers, personalized outreach, or loyalty incentives\u2014you can reduce the likelihood of losing them and maintain their engagement with your brand.<\/span><\/p>\n

            Regular Analysis<\/b>
            <\/b><\/h4>\n

            Action:<\/b> Conduct RFM analysis periodically to track changes in customer behavior and adjust strategies accordingly.<\/span>
            <\/span><\/p>\n

            Rationale:<\/b> Customer behaviors and market conditions change over time.<\/span><\/p>\n

            Regular RFM analysis allows you to stay updated on these changes, ensuring that your marketing strategies<\/a> remain effective and aligned with current customer needs and preferences.<\/span><\/p>\n

            Integrate with Other Data<\/b>
            <\/b><\/h4>\n

            Action:<\/b> Combine RFM analysis results with other customer data (e.g., demographics, product preferences) for more comprehensive insights.<\/span><\/p>\n

            Rationale:<\/b> RFM analysis provides valuable insights, but combining it with additional data can offer a more holistic view of your customers<\/a>. <\/span><\/p>\n

            This integration allows for more accurate segmentation and personalization, ultimately leading to better-targeted marketing efforts and improved customer satisfaction.<\/span><\/p>\n

            Test and Iterate<\/b>
            <\/b><\/h4>\n

            Action:<\/b> Continuously test different marketing approaches for each customer segment<\/a> and refine strategies based on results.<\/span><\/p>\n

            Rationale:<\/b> Not all marketing strategies will work equally well for every segment. <\/span><\/p>\n

            By testing different approaches and analyzing the results, you can identify the most effective strategies for each segment and refine your tactics to maximize their impact.<\/span><\/p>\n

            \n

            Use Behavioral Modeling to Acquire New Customers<\/strong><\/h4>\n<\/p>\n<\/div>\n

            Customer Journey Mapping<\/b>
            <\/b><\/h4>\n

            Action:<\/b> Use RFM insights to improve the overall customer journey<\/a> and experience across different touchpoints.<\/span><\/p>\n

            Rationale:<\/b> Understanding where each customer segment is in their journey allows you to optimize their experience at every touchpoint. <\/span><\/p>\n

            By applying RFM insights to customer journey mapping<\/a>, you can enhance engagement, satisfaction, and loyalty by ensuring that customers receive the right message at the right time.<\/span><\/p>\n

            The RFM Model<\/strong><\/a> is a tool businesses use to understand their customers by looking at three key factors:
            Recency (R)<\/strong>: How recently a customer made a purchase.
            Frequency (F)<\/strong>: How often they make purchases.
            Monetary (M)<\/strong>: How much money they spend.<\/p>\n

            Let\u2019s break this down with a simple example:<\/span><\/p>\n

            Example<\/strong>:
            Imagine you own an online clothing store. You have three customers:
            Customer A<\/strong>: Bought something last week, buys clothes every month, and usually spends $100 per order.
            Customer B<\/strong>: Bought something six months ago, buys once a year, and spends $200 per order.
            Customer C<\/strong>: Bought something three months ago, buys every few months, and spends $50 per order.<\/p>\n

            How RFM Model Works?<\/b><\/h3>\n
              \n
            1. Recency<\/b>: Customer A is the most recent buyer, followed by Customer C. Customer B bought a while ago, so they\u2019re considered less \u201crecent.\u201d<\/span><\/li>\n
            2. Frequency<\/b>: Customer A buys the most often, making them the most frequent shopper. Customer C is in the middle, and Customer B buys the least frequently.<\/span><\/li>\n
            3. Monetary<\/b>: Customer B spends the most per purchase, but since they don\u2019t buy often, Customer A is considered more valuable overall.<\/span><\/li>\n<\/ol>\n

              How is RFM Model Useful?<\/b><\/h3>\n

              The RFM model helps you figure out which customers are the most valuable and which ones might need more attention. In our example:<\/span><\/p>\n

                \n
              1. Customer A<\/b> is a loyal, high-value customer\u2014they buy often, spend regularly, and have bought recently. You might reward them with special offers to keep them coming back.<\/span><\/li>\n
              2. Customer B<\/b> spends a lot but rarely buys\u2014maybe a reminder or promotion could get them to purchase more often.<\/span><\/li>\n
              3. Customer C<\/b> is somewhat engaged but not as valuable\u2014targeting them with offers to increase frequency or spending could boost their value.<\/span><\/li>\n<\/ol>\n

                Why Use RFM Model?<\/b><\/h2>\n

                Using the RFM model helps you focus your marketing efforts on the customers most likely to respond. Instead of sending random promotions to everyone, you can:<\/span><\/p>\n

                  \n
                1. Offer loyalty rewards to frequent buyers.<\/span><\/li>\n
                2. Send re-engagement emails to those who haven\u2019t bought in a while.<\/span><\/li>\n
                3. Encourage bigger purchases by offering discounts or free shipping to high spenders.<\/span><\/li>\n<\/ol>\n

                  This personalized approach saves time and money while helping you retain your best customers and improve sales.<\/span><\/p>\n

                  Data Sources Required for RFM:<\/h4>\n\n\n\n\n\n\n\n\n\n
                  Data Type<\/b><\/td>\nDescription<\/b><\/td>\nSource<\/b><\/td>\nExample<\/b><\/td>\n<\/tr>\n
                  Transaction Data<\/b><\/td>\nRecords of customer purchases, including dates and amounts.<\/span><\/td>\nInternal sales system, POS system, e-commerce platform<\/span><\/td>\nDate of purchase, order amount, order ID<\/span><\/td>\n<\/tr>\n
                  Customer ID<\/b><\/td>\nUnique identifier for each customer to track their transactions.<\/span><\/td>\nCRM, e-commerce platform<\/span><\/td>\nCustomer ID, email address, phone number<\/span><\/td>\n<\/tr>\n
                  Purchase Date<\/b><\/td>\nDate and time of each transaction to calculate <\/span>Recency<\/b>.<\/span><\/td>\nSales database, order history<\/span><\/td>\nLast purchase date: 2024-09-01<\/span><\/td>\n<\/tr>\n
                  Number of Purchases<\/b><\/td>\nTotal count of purchases per customer for <\/span>Frequency<\/b>.<\/span><\/td>\nCRM, sales database<\/span><\/td>\nCustomer A: 5 purchases in the last 12 months<\/span><\/td>\n<\/tr>\n
                  Total Spend Amount<\/b><\/td>\nThe sum of all purchases made by a customer to calculate <\/span>Monetary<\/b> value.<\/span><\/td>\nSales database, accounting software<\/span><\/td>\nTotal spent by Customer A: $500<\/span><\/td>\n<\/tr>\n
                  Customer Segments<\/b><\/td>\n(Optional) Customer profiles to categorize by demographics or behavior.<\/span><\/td>\nCRM, marketing database<\/span><\/td>\nHigh-spending customers, frequent buyers, occasional shoppers<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                  Output that can be generated through RFM:<\/strong><\/h4>\n\n\n\n\n\n\n\n\n\n\n\n
                  Output Type<\/b><\/td>\nDescription<\/b><\/td>\nExample<\/b><\/td>\n<\/tr>\n
                  RFM Scores (Individual Scores)<\/b><\/td>\nEach customer is assigned a score for Recency (R), Frequency (F), and Monetary (M) on a scale (e.g., 1-5).<\/span><\/td>\nA customer receives an RFM score of <\/span>5-4-3<\/b>, meaning recent buyer (5), buys often (4), moderate spender (3).<\/span><\/td>\n<\/tr>\n
                  Customer Segments<\/b><\/td>\nCustomers are grouped into segments based on their RFM scores.<\/span><\/td>\n\u2013 Best Customers (5-5-5): Frequent buyers, high spenders, recent purchases.<\/span><\/td>\n<\/tr>\n
                  \u2013 At-Risk Customers (1-3-2): Have not bought recently, infrequent purchases.<\/span><\/td>\n<\/tr>\n
                  Customer Segmentation Report<\/b><\/td>\nCategorized list of customers based on RFM score combinations.<\/span><\/td>\n\u2013 Segment 1: Best customers (R=5, F=5, M=5).<\/span><\/td>\n<\/tr>\n
                  \u2013 Segment 2: At-risk customers (R=1, F=3, M=2).<\/span><\/td>\n<\/tr>\n
                  Actionable Insights<\/b><\/td>\nClear strategies for different customer groups based on RFM scores.<\/span><\/td>\n\u2013 <\/span>Best Customers<\/b>: Offer loyalty rewards or exclusive offers.<\/span><\/td>\n<\/tr>\n
                  \u2013 <\/span>At-Risk Customers<\/b>: Send reminders or discount offers to re-engage.<\/span><\/td>\n<\/tr>\n
                  Visualization\/Dashboard<\/b><\/td>\nVisual representation of customer distribution based on RFM scores.<\/span><\/td>\nA heatmap showing customer groups, e.g., best, loyal, at-risk, churned, and their proportions.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                  Reports that can be generated through RFM Models<\/strong><\/h4>\n

                  \"Reports<\/p>\n

                  What Questions does RFM Resolve for Client:<\/strong><\/h4>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
                  Question<\/b><\/td>\nRFM Focus<\/b><\/td>\nResolved Insight<\/b><\/td>\n<\/tr>\n
                  Who are my best customers?<\/b><\/td>\nHigh Recency, Frequency, and Monetary scores<\/span><\/td>\nIdentifies the most valuable customers who frequently purchase, spend a lot, and have made recent transactions.<\/span><\/td>\n<\/tr>\n
                  Which customers are at risk of churning?<\/b><\/td>\nLow Recency, moderate Frequency, and Monetary scores<\/span><\/td>\nPinpoints customers who haven\u2019t purchased recently, helping to target them with retention strategies.<\/span><\/td>\n<\/tr>\n
                  Who are my most loyal customers?<\/b><\/td>\nHigh Frequency, moderate Recency, and Monetary scores<\/span><\/td>\nHighlights frequent purchasers, indicating the need for loyalty rewards or exclusive offers.<\/span><\/td>\n<\/tr>\n
                  Which customers spend the most money?<\/b><\/td>\nHigh Monetary scores<\/span><\/td>\nIdentifies customers with the highest spending, focusing on maximizing these relationships.<\/span><\/td>\n<\/tr>\n
                  Which customers need re-engagement?<\/b><\/td>\nLow Recency, varying Frequency and Monetary scores<\/span><\/td>\nReveals customers who haven\u2019t purchased recently but were valuable, suggesting the need for re-engagement.<\/span><\/td>\n<\/tr>\n
                  Who are the potential high-value customers?<\/b><\/td>\nHigh Recency and Frequency, low Monetary scores<\/span><\/td>\nIdentifies customers who buy often and recently, but spend less, offering cross-selling or upselling opportunities.<\/span><\/td>\n<\/tr>\n
                  Which customers are new and need nurturing?<\/b><\/td>\nHigh Recency, low Frequency, and Monetary scores<\/span><\/td>\nRecognizes new customers with recent, low-value purchases, helping develop strategies to nurture them into loyal ones.<\/span><\/td>\n<\/tr>\n
                  What is the distribution of revenue across my customer base?<\/b><\/td>\nSegmentation based on Monetary scores<\/span><\/td>\nHelps understand which segments contribute the most to revenue, guiding resource allocation and marketing focus.<\/span><\/td>\n<\/tr>\n
                  Which customers should we avoid investing in?<\/b><\/td>\nLow Recency, Frequency, and Monetary scores<\/span><\/td>\nIdentifies low-value customers to avoid spending resources on, preventing wasteful marketing investments.<\/span><\/td>\n<\/tr>\n
                  How can we increase customer retention and spending?<\/b><\/td>\nOverall RFM score analysis<\/span><\/td>\nGuides the creation of targeted marketing campaigns to increase purchase frequency, spending, and retention.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                  \u00a0<\/p>\n<\/p><\/div>\n\n","protected":false},"excerpt":{"rendered":"

                  Overview The RFM (Recency, Frequency, Monetary) analysis is a powerful tool for understanding customer behavior and segmenting customers based on their purchasing patterns. 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