I will give a few examples of how the defined segments can look like:
Top Customers – This group consists of those customers who are in R-segment-5, F-segment-5, and M-segment-5, meaning they have recently transacted, shop frequently, and spend more than other customers. The shorthand for this segment is 555.
New high spenders – this group consists of those customers in 515 and 514. These are customers who have only traded once, but very recently, and have spent a lot.
Lowest Spending Active Loyal Customers – This group consists of those customers in segments 551 and 552 (they have shopped recently and do so frequently, but spend the least).
“Top Customer Laggards” – this segment consists vnpay database of customers in groups 155, 154, 145 and 144 (they traded frequently and spent a lot, but it has been a long time since their last activity)
Based on the resulting RFM score, RFM segments are defined, which are then applied across the company’s internal reporting.
RFM Data Analysis can help companies from personalizing marketing presentations for individual customers to identifying and mitigating business risks. Let’s take a look at some of the benefits of using RFM analysis in marketing.
1) Using RFM segmentation in email marketing
Expand your existing mailing database with RFM segmentation and take your email marketing to the next level. Here we come to the point where we evaluate my mention of the advantages of a holistic dataset for more advanced use, not only taiwan data in RFM. In the case when we have complete web tracking information from the email automation tool at the level of a specific contact containing information about customer behavior (emails received, open rate, clicks, which products they visited on the website, etc.) + information and complete we proposed a quick-win solution transaction history recalculated within the RFM segments, targeting our other campaigns is a breeze . In advanced implementations, this information can be enriched with additional metrics from Google Analytics, or other CRM data to define the exact logic of automation rules for completely personalized and automated emailing.
2) Using RFM segments for lookalike audiences in social media targeting
Using automatic segmentation with RFM, you can automatically define audiences for retargeting within social networks. Thanks to this, you will be able to constantly expand your audiences with new customers and always target relevant customers based on their CLV and specific interest in products.
3) Extended RFM for optimization/strategy using order detail information
Basic RFM analysis addresses segmentation only at the transaction level, in terms of how often a customer purchases, when they last purchased, and how much they spent. For a full 360° view, it is necessary to delve a level deeper into the very details of customer transactions. This is one of the biggest challenges of RFM analysis (and segmentation in general), how to create a full 360° view of the customer.
