{"id":16113,"date":"2020-06-03T14:55:36","date_gmt":"2020-06-03T12:55:36","guid":{"rendered":"https:\/\/www.dase-analytics.com\/blog\/?p=16113\/"},"modified":"2022-10-27T14:52:06","modified_gmt":"2022-10-27T12:52:06","slug":"rfm-analysis","status":"publish","type":"post","link":"https:\/\/www.dase-analytics.com\/blog\/en\/rfm-analysis\/","title":{"rendered":"RFM Analysis: Understand and Influence Consumer Behavior"},"content":{"rendered":"<div id=\"toc_container\" class=\"toc_wrap_right toc_white no_bullets en-toc\"><p class=\"toc_title\">Obsah \u010dl&aacute;nku<\/p><ul class=\"toc_list\"><li><\/li><li><\/li><li><\/li><li><ul><li><\/li><li><\/li><li><\/li><li><\/li><li><\/li><\/ul><\/li><li><\/li><li><\/li><\/ul><\/div>\n\n<p><span style=\"font-weight: 400;\">RFM (<\/span><b>R<\/b><span style=\"font-weight: 400;\">ecency, <\/span><b>F<\/b><span style=\"font-weight: 400;\">requency, <\/span><b>M<\/b><span style=\"font-weight: 400;\">onetary) analysis is an easy way to divide customers into segments based on their purchasing behavior. This analysis has been used for more than 25 years to improve targeting, reduce costs, and increase the return on advertising investment.<\/span><\/p>\n<h1><span id=\"History_of_RFM_Analysis\"><span style=\"font-weight: 400;\">History of RFM Analysis<\/span><\/span><\/h1>\n<p><span style=\"font-weight: 400;\">The RFM analysis was used for the first time in the USA by mail order companies focused on catalogue sales, such as <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Lands%27_End\"><span style=\"font-weight: 400;\">Land &#8216;s End<\/span><\/a><span style=\"font-weight: 400;\">, <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Charles_Tyrwhitt\"><span style=\"font-weight: 400;\">Charles Tyrwhitt<\/span><\/a><span style=\"font-weight: 400;\"> or <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/JCPenney\"><span style=\"font-weight: 400;\">JCPenney<\/span><\/a><span style=\"font-weight: 400;\">. They used it to minimize shipping costs and <\/span><b>maximize profits from sales<\/b><span style=\"font-weight: 400;\">. Thanks to the analysis, they were able to identify customers to whom they didn\u2019t need to send catalogues or offer various discounts anymore.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The effectiveness of RFM analysis is also confirmed by the following practical examples:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Eastwood increased email marketing profits by 21 % (2008).<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">L\u2019Occitane received 25 times more revenue from email marketing.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Frederick\u2019s of Hollywood increased conversions by 6-9 %.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">You can read more in the <\/span><a href=\"https:\/\/nmcdn.io\/e186d21f8c7946a19faed23c3da2f0da\/712f15a792524df4bfe0912e6bf5bb2a\/files\/RFM-Analysis\/Windsor_Circle_Whitepaper_-_RFM_Analysis_pdf.pdf\"><span style=\"font-weight: 400;\">study from Winston Circle<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h1><span id=\"Customer_Segmentation\"><span style=\"font-weight: 400;\">Customer Segmentation<\/span><\/span><\/h1>\n<p><span style=\"font-weight: 400;\">The result of RFM analysis is usually the division of customers into segments. The number of segments depends on you, but 11 segments are the most often used. The table provides an overview of the segments together with their definition and <\/span><b>suitable recommendations<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Customer Segment<\/b><\/td>\n<td><b>Activity<\/b><\/td>\n<td><b>Actionable Tip<\/b><\/td>\n<\/tr>\n<tr>\n<td><b><i>Champions<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">Bought recently, buy often and spend the most!<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Reward them. They can be early adopters for new products. Will promote your brand.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Loyal Customers<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">Often spend good money buying your products. Responsive to promotions.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Upsell higher value products. Ask for reviews. Engage them.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Potential Loyalist<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">Recent customers, but spent a good amount and bought more than once.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Offer membership\/loyalty programs and recommend other products.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Recent Customers<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">Recent shoppers, but haven\u2019t spent much.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Provide onboarding support, give them early success, and start building relationships.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Promising<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">Bought most recently, but not often.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Create brand awareness, offer free trials.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Customers Needing Attention<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">Above-average recency, frequency and monetary values. They may not have bought very recently though.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Make limited-time offers. Recommendations based on past purchases. Reactivate them.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>About To Sleep<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">Below average recency, frequency, and monetary values. Will lose them if not reactivated.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Share valuable resources. Recommend popular products\/renewals at discount. Reconnect with them.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>At Risk<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">They spent big money and purchased often. But the last purchase was a long time ago. Need to bring them back!<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Send personalized emails to reconnect, offer renewals, provide helpful resources.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Can\u2019t Lose Them<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">Often made the biggest purchases but they haven&#8217;t returned for a long time.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Win them back via renewals or newer products. Don\u2019t lose them to competition, talk to them.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Hibernating<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">The last purchase was long ago. Low spenders with a low number of orders.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Offer other relevant products and special discounts. Recreate brand value.<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Lost<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">Lowest recency, frequency, and monetary scores.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Revive interest with a reach-out campaign, ignore otherwise.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h1><span id=\"Why_RFM_Analysis\"><span style=\"font-weight: 400;\">Why RFM Analysis<\/span><\/span><\/h1>\n<p><span style=\"font-weight: 400;\">With RFM analysis, you can find answers to questions such as:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Who are your <a href=\"https:\/\/growmatik.ai\/blog\/how-to-find-and-keep-your-best-customers-using-rfm-segmentation\/\" target=\"_blank\" rel=\"noopener noreferrer\">best and the most loyal customers and how to keep them<\/a>?<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Which customers are close to leaving you?<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">How to increase the frequency of recurring purchases?<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">What to communicate to individual customer segments?<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">How to increase the likelihood of recurring purchases for new customers?<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The main principle used in RFM analysis is the <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Pareto_principle\"><span style=\"font-weight: 400;\">Pareto principle<\/span><\/a><span style=\"font-weight: 400;\">. So focus on important customer segments that are likely to give you a <\/span><b>higher return on investment<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">I hope you are now convinced of the usefulness of the RFM analysis for your own business.<\/span><\/p>\n<h1><span id=\"Basic_Calculations\"><span style=\"font-weight: 400;\">Basic Calculations<\/span><\/span><\/h1>\n<p><span style=\"font-weight: 400;\">For calculations, you will need the following information about each customer:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Customer ID (email, user ID, client ID, name, etc.) so you can identify each customer.<\/span><\/li>\n<li style=\"font-weight: 400;\"><b>Recency (R): <\/b><span style=\"font-weight: 400;\">How many days, weeks, months have passed since their last purchase? To calculate the current value, simply subtract the date of the last purchase from today.<\/span><\/li>\n<li style=\"font-weight: 400;\"><b>Frequency (F): <\/b><span style=\"font-weight: 400;\">How many times has a customer bought from you? For example, if someone has bought 10 times from you in a certain period of time, their frequency will be 10.<\/span><\/li>\n<li style=\"font-weight: 400;\"><b>Monetary (M): <\/b><span style=\"font-weight: 400;\">How much money did the customer spend? If someone bought products or services a total of \u20ac 1,000 from you for the selected period, then this value will be \u20ac 1,000.<\/span><\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/RFM-Recency-Frequency-and-Monetary-Value.png\" data-rel=\"lightbox-image-0\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone size-full wp-image-16119\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/RFM-Recency-Frequency-and-Monetary-Value.png\" alt=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/RFM-Recency-Frequency-and-Monetary-Value.png\" width=\"1200\" height=\"628\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/RFM-Recency-Frequency-and-Monetary-Value.png 1200w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/RFM-Recency-Frequency-and-Monetary-Value-300x157.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/RFM-Recency-Frequency-and-Monetary-Value-1024x536.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/RFM-Recency-Frequency-and-Monetary-Value-600x314.png 600w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<h2><span id=\"Example_of_sample_data\"><span style=\"font-weight: 400;\">Example of sample data<\/span><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">We will use data from Google Analytics for our calculations, where you can send a client ID to your own dimension as a customer identifier.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In our case, the transaction represents the reading of the article to the end and the revenue represents the number of words in the article that the user read on our blog.<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Client ID<\/b><\/td>\n<td><b>Date<\/b><\/td>\n<td><b>Transactions<\/b><\/td>\n<td><b>Revenue<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">100438314.1556<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2019-05-02<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><span style=\"font-weight: 400;\">1750<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">100515227.1557<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2019-05-02<\/span><\/td>\n<td><span style=\"font-weight: 400;\">3<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2297<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">1006060326.1564<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2019-08-28<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2916<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">1010126481.1552<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2019-04-05<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2308<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">101406401.1571<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2020-03-20<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><span style=\"font-weight: 400;\">4590<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">1027069711.1550<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2019-04-17<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><span style=\"font-weight: 400;\">1812<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">1046122396.1556<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2019-04-21<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><span style=\"font-weight: 400;\">1460<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">1047540009.1561<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2019-06-24<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><span style=\"font-weight: 400;\">1790<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">1049573649.1575<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2020-01-08<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><span style=\"font-weight: 400;\">1806<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">1064248633.1569<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2019-09-18<\/span><\/td>\n<td><span style=\"font-weight: 400;\">5<\/span><\/td>\n<td><span style=\"font-weight: 400;\">5205<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">1075101880.1572<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2019-10-25<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2377<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<h2><span id=\"Calculation_of_Recency_Frequency_and_Monetary_values\"><span style=\"font-weight: 400;\">Calculation of Recency, Frequency, and Monetary values<\/span><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">From the data above, you can calculate the following information in Google Sheets:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Date of first transaction (<\/span><a href=\"https:\/\/support.google.com\/docs\/answer\/3094017\"><span style=\"font-weight: 400;\">MIN function<\/span><\/a><span style=\"font-weight: 400;\">)<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Date of last transaction (<\/span><a href=\"https:\/\/support.google.com\/docs\/answer\/3094013\"><span style=\"font-weight: 400;\">MAX function<\/span><\/a><span style=\"font-weight: 400;\">)<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Number of months since last purchase (<\/span><a href=\"https:\/\/support.google.com\/docs\/answer\/6055612\"><span style=\"font-weight: 400;\">DATEDIF<\/span><\/a><span style=\"font-weight: 400;\">)<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Average Order Value (<\/span><a href=\"https:\/\/support.google.com\/docs\/answer\/3093583\"><span style=\"font-weight: 400;\">SUMIF<\/span><\/a><span style=\"font-weight: 400;\">)<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Average order frequency (number of transactions)<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">To make your data <\/span><b>more realistic<\/b><span style=\"font-weight: 400;\">, you can use the frequency of orders per day, month, or year. You can choose a time period that makes sense for your business. For example, if you sell coffee, it will be appropriate to use days, if cars, it would be more reasonable to choose, for example, the year.<\/span><\/p>\n<p><b>Note<\/b><span style=\"font-weight: 400;\">: <\/span><i><span style=\"font-weight: 400;\">When calculating, it doesn&#8217;t matter if you focus on the average order value per month, year, or day, because you&#8217;ll then calculate an RFM score on a scale of 1-5 using a <\/span><\/i><a href=\"https:\/\/en.wikipedia.org\/wiki\/Percentile\"><i><span style=\"font-weight: 400;\">percentile<\/span><\/i><\/a><i><span style=\"font-weight: 400;\"> for each customer.\u00a0<\/span><\/i><\/p>\n<h2><span id=\"How_to_calculate_the_RFM_scores_on_a_scale_from_1_to_5\"><span style=\"font-weight: 400;\">How to calculate the RFM scores on a scale from 1 to 5?<\/span><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">You can calculate these values \u200b\u200bin several ways. One way is to use an estimate when you look at the results and say that all customers who have bought from you within 30 days, will have a score for recency (R) 5.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">However, I recommend that you use <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Quantile\"><span style=\"font-weight: 400;\">quantiles<\/span><\/a><span style=\"font-weight: 400;\"> to calculate the RFM score. The quantiles are like a percentile, but instead of dividing the data into 100 parts, it divides them into 5 equal parts. This method is a bit more complicated to calculate, but you can apply it to any industry. Its advantage is that it <\/span><b>distributes customers evenly<\/b><span style=\"font-weight: 400;\"> to all quantiles.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the next step, you need to sort your customers by using the <\/span><a href=\"https:\/\/support.google.com\/docs\/answer\/3094098\"><span style=\"font-weight: 400;\">RANK<\/span><\/a><span style=\"font-weight: 400;\"> function. Use this feature to assign a <\/span><b>score to each customer<\/b><span style=\"font-weight: 400;\">, which you use later to calculate percentiles.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">After sorting, you need to calculate the percentiles in the help table, according to which you assign the RFM score. To do this, use the <\/span><a href=\"https:\/\/support.google.com\/docs\/answer\/3094093\"><span style=\"font-weight: 400;\">PERCENTILE<\/span><\/a><span style=\"font-weight: 400;\"> function. With it, you get values \u200b\u200bbased on which you can divide customers into 5 equal groups (quantiles).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Based on the percentile, you assign an RFM score to each customer using the <\/span><a href=\"https:\/\/support.google.com\/docs\/answer\/3093364\"><span style=\"font-weight: 400;\">IF function<\/span><\/a><span style=\"font-weight: 400;\"> based on their rank. So if the customer&#8217;s rank for recency is higher than 255.4, then this customer is among the top 20 % of customers by recency.\u00a0<\/span><\/p>\n<p>As a result, you get <strong>each customer&#8217;s score for recency, frequency, and monetary.<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Customers who are frequent buyers, have recently bought from you and usually are spending a lot of money, they would get a score of 555: Recency (R) &#8211; 5, Frequency (F) &#8211; 5, Monetary (M) &#8211; 5. <\/span><b>They are your best customers.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">On the other hand, customers who spend the least, do almost no buying at all and their last purchase was really long time ago, they will get a score of 111: Recency (R) &#8211; 1, Frequency (F) &#8211; 1, Monetary (M) &#8211; 1.<\/span><\/p>\n<p><b>That makes sense. Right?<\/b><\/p>\n<h2><span id=\"Grouping_customers_into_segments_clusters\"><span style=\"font-weight: 400;\">Grouping customers into segments (clusters)<\/span><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">R, F, and M have scores from 1 to 5, so there are a total of 5 x 5 x 5 = 125 combinations of RFM values. If you want to see how many customers you have for each RFM value, you would need to look at 125 separate segments.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Having high granularity in data may not make sense for many companies, so you can summarize the analysis into 11 segments to <\/span><b>better understand your customers<\/b><span style=\"font-weight: 400;\">. I mentioned these segments at the beginning of this article.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here&#8217;s a table that explains how you can create 11 segments based on an RFM score.<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Segment<\/b><\/td>\n<td><b>Scores<\/b><\/td>\n<\/tr>\n<tr>\n<td><b><i>Champions<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">555, 554, 544, 545, 454, 455, 445<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Loyal Customers<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">543, 444, 435, 355, 354, 345, 344, 335<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Potential Loyalist<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">553, 551,552, 541, 542, 533, 532, 531, 452, 451, 442, 441, 431, 453, 433, 432, 423, 353, 352, 351, 342, 341, 333, 323<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Recent Customers<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">512, 511, 422, 421, 412, 411, 311<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Promising<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">525, 524, 523, 522, 521, 515, 514, 513, 425, 424, 413,414, 415, 315, 314, 313<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Customers Needing Attention<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">535, 534, 443, 434, 343, 334, 325, 324<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>About To Sleep<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">331, 321, 312, 221, 213<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>At Risk<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">255, 254, 245, 244, 253, 252, 243, 242, 235, 234, 225, 224, 153, 152, 145, 143, 142, 135, 134, 133, 125, 124<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Can\u2019t Lose Them<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">155, 154, 144, 214,215,115, 114, 113<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Hibernating<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">332, 322, 231, 241, 251, 233, 232, 223, 222, 132, 123, 122, 212, 211<\/span><\/td>\n<\/tr>\n<tr>\n<td><b><i>Lost<\/i><\/b><\/td>\n<td><span style=\"font-weight: 400;\">111, 112, 121, 131,141,151<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span id=\"Visualization_of_RFM_analysis_results\"><span style=\"font-weight: 400;\">Visualization of RFM analysis results<\/span><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The graphical representation will help you to orientate <\/span><b>faster and easier in the results of RFM analysis<\/b><span style=\"font-weight: 400;\">. When visualizing, it is good to use simple bar graphs, which will show you the distribution of customers in individual segments. You can also add average RFM values for each segment, individual <\/span><b>recommendations for specific segments<\/b><span style=\"font-weight: 400;\">, and detailed RFM reports for each customer.<\/span><\/p>\n<p><a href=\"https:\/\/datastudio.google.com\/u\/0\/reporting\/1m_KZ81sshpQ-s3poGCtFbhM3sBvBcxbP\/page\/tDvNB\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone size-full wp-image-16128\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/RFM-Analysis-report-cta.png\" alt=\"RFM Analysis report cta\" width=\"951\" height=\"619\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/RFM-Analysis-report-cta.png 951w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/RFM-Analysis-report-cta-300x195.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/RFM-Analysis-report-cta-600x391.png 600w\" sizes=\"(max-width: 951px) 100vw, 951px\" \/><\/a><\/p>\n<p><strong><a href=\"https:\/\/datastudio.google.com\/reporting\/1m_KZ81sshpQ-s3poGCtFbhM3sBvBcxbP\/page\/tDvNB\">Example of visualization of RFM analysis results in Google Data Studio<\/a><\/strong><\/p>\n<h1><span id=\"Useful_Recommendations\"><span style=\"font-weight: 400;\">Useful Recommendations<\/span><\/span><\/h1>\n<p><span style=\"font-weight: 400;\">If you are interested in <\/span><b>which channel brings the most VIP customers to you<\/b><span style=\"font-weight: 400;\">, you can upload the results of the RFM analysis to Google Analytics via the data import feature. Then, you can target these customers with customized advertising and with a <\/span><b>high probability of reducing costs and increasing returns<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you use emails for RFM analysis you can import these emails into the ad platform and target users who are <\/span><b>similar to your VIP customers<\/b><span style=\"font-weight: 400;\"> or exclude customers who belong to the lost segment from advertising.\u00a0<\/span><\/p>\n<h1><span id=\"Conclusion\"><span style=\"font-weight: 400;\">Conclusion<\/span><\/span><\/h1>\n<p><span style=\"font-weight: 400;\">It is nice to read the article, but the important thing is to <\/span><b>use this RFM analysis and its results in practice.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Sign up for the newsletter and we will send you detailed recommendations on <\/span><b>how to import email addresses<\/b><span style=\"font-weight: 400;\"> into advertising platforms such as Google Ads, Facebook Ads, Linkedin Ads, or Twitter Ads.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you like the article, I&#8217;d love it if you could <\/span><b>share it with your colleagues and friends<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>RFM (Recency, Frequency, Monetary) analysis is an easy way to divide customers into segments based on their purchasing&#8230;<\/p>\n","protected":false},"author":69,"featured_media":16115,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[768],"tags":[781,779,782,756,783,780],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/en\/wp-json\/wp\/v2\/posts\/16113"}],"collection":[{"href":"https:\/\/www.dase-analytics.com\/blog\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dase-analytics.com\/blog\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/en\/wp-json\/wp\/v2\/users\/69"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/en\/wp-json\/wp\/v2\/comments?post=16113"}],"version-history":[{"count":22,"href":"https:\/\/www.dase-analytics.com\/blog\/en\/wp-json\/wp\/v2\/posts\/16113\/revisions"}],"predecessor-version":[{"id":18919,"href":"https:\/\/www.dase-analytics.com\/blog\/en\/wp-json\/wp\/v2\/posts\/16113\/revisions\/18919"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/en\/wp-json\/wp\/v2\/media\/16115"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/en\/wp-json\/wp\/v2\/media?parent=16113"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/en\/wp-json\/wp\/v2\/categories?post=16113"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/en\/wp-json\/wp\/v2\/tags?post=16113"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}