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RFM model is a user layering method commonly used in enterprise fine operation. Through the calculation of last consumption (Recency), consumption frequency (Frequency) and consumption amount (Monetary), enterprises can divide users into eight levels ranging from general retention to high value, which is convenient for personalized marketing.

Sample RFM model (diagram source network)

Major banks and e-commerce platforms are common practitioners of the RFM model. By collecting and calculating the data of customers' transactions, bills and other dimensions, the bank will divide the customers into several groups ranging from ordinary to high net worth, while the e-commerce platform will divide the user groups according to the membership level. As consumers' needs become more refined, they will subdivide the user group at the original level and label it with different values, such as circling a higher-value user group among higher-level members in order to carry out more personalized operations and create greater growth.

Take the e-commerce industry as an example, Amazon's Prime members, Taobao's 88VIP, VIPSHOP's super VIP, Sam's outstanding members, and so on, are all re-selection of high-value members, whose consumption frequency, purchasing power and loyalty are all higher. In the face of this group, the goal of enterprise marketing is to further improve the customer unit price and purchase frequency.

For example, in terms of rights and interests, Sam's outstanding members have 2% more points rebates and 12 freight coupons for the whole year than ordinary members, and have also increased the rights and interests frequently used by middle-class people such as free car washing and dental care, bringing value to members in addition to shopping. The marketing effect is also very obvious. According to the data, nearly 85% of Sam's outstanding members come from ordinary membership promotion, the membership rights and interests have achieved nearly 100% exchange rate, and the membership renewal rate is more than 80%.

There is no doubt about the role of RFM model in fine operation. With the use of this analysis method becoming more and more common, in order to shorten the path for business personnel to analyze with RFM model and lower the analysis threshold, data service providers, including GrowingIO, have developed the "RFM" tag function one after another, so that business personnel can achieve user clustering and tagging through simple settings, thus one-click circle selection for personalized marketing.

Low code, super flexible, easier to operate

Take the "RFM tag" in the GrowingIO customer data platform as an example, its setting is very similar to the calculation method of the RFM model, users need to define R, F, M three indicators first, so that the background can calculate the average.

Example of RFM label setup

Then define the RFM value hierarchy and set the level name to distinguish different levels of user groups, which is very simple.

As a low-code tag, RFM tags also have a high degree of flexibility beyond the low threshold. Business staff can set the equation of R, F, M and change the level name according to the actual demand, and can adjust it at any time. Next, you can click to circle the group hierarchical marketing. From tagging to circling, the whole process can be completed in less than five minutes, and there is no need for analysts and developers to write SQL to retrieve data, build models, and do not need weeks of scheduling to produce.

Through RFM tags, enterprises can not only understand the user structure, find the most valuable users and users who need to be recovered, but also understand their purchasing behavior and preferences, so as to adjust the product positioning and promotion strategy, improve the marketing effect and stimulate user consumption, so as to improve product sales and market share.

All the tags of GrowingIO customer data platform (CDP) are low code tags, covering rules, algorithms and other dimensions, which can fully meet the various needs of the enterprise circle. In the future, we will also develop more low code tags to help enterprises do a good job in fine operation, maintain high-value groups, and achieve greater growth.

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