In this article, we'll take a look at RFM (recency, frequency, monetary value) analysis, which is based on the behavior of customer groups (or segments). This method of analysis allows you to study the behavior of users and how they make payments. As a result, you’ll receive valuable insights for direct marketing. Show
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What is RFM analysis and why does a marketer need it?RFM analysis allows you to segment customers by the frequency and value of purchases and identify those customers who spend the most money.
According to these metrics, it’s possible to divide your customers into groups to understand which customers buy lots of things frequently, which buy few things but frequently, and which haven’t bought anything for a long time. As a rule, only a small percentage of customers respond to general promotional offers. RFM is an excellent segmentation method for predicting customer responses, improving interactions, and increasing profits. RFM uses customer behavior data to determine how to work with each customer group. RFM analysis using OWOX BI and ExcelThe essence of RFM analysis is to divide customers into groups based on how recently they made their last purchase, how often they buy things, and the average value of their orders. For each of these metrics, we assign customers to one of three groups, which are assigned a number from 1 to 3. Recency:
Frequency:
Monetary value
Customers are assigned RFM values by concatenating their numbers for Recency, Frequency, and Monetary value. For example, customer 111 made one order with a low monetary value a long time ago. Customer 333, on the other hand, often makes large-value orders and made a purchase recently. Customers with 3’s in every category are your best customers. There are two convenient ways to perform RFM analysis: using OWOX BI and using Excel (or Google Sheets). RFM analysis example using OWOX BIUnlike Excel, OWOX BI allows you to automatically calculate RFM. The data source for this analysis is a table or view in Google BigQuery with data about each order with the following fields:
Calculating RFM segmentsTo calculate RFM segments we recommend using data on confirmed orders from your ERP. You can easily export this data to BigQuery using OWOX BI Pipeline. OWOX BI also gives you the opportunity to customize the importing of RFM analysis results to Google Analytics. This will allow you to:
Setting up the importing of RFM analysis data into Google Analytics from Google BigQuery consists of two steps:
Let's take a closer look at the last two points. Configure the Google Analytics web propertyCreate Custom Dimensions at the user level to store the results of RFM analysis (Custom Definitions → Custom Dimensions → +New Custom Dimension): Create a data set to import data into Google Analytics (Data Import → New Data Set). Select the data set type — User data: Specify the import behavior as Query time. This will allow you to combine imported data with historical data; otherwise, the data will be merged only with hits collected in Google Analytics after downloading the results of the RFM analysis. Note that Query time import is available only to users of Google Analytics 360. Next, name the data set and determine the list of views in which the imported data will be available: Finally, define a data schema and save the data set: That’s it. You’ve now configured the Google Analytics web property settings for importing data. Now you can proceed to import. Creating a stream in OWOX BI Pipeline
Wait until data appears in Google Analytics: Everything is ready. OWOX BI will automatically perform RFM analysis without requiring much involvement from you. Excel RFM analysis algorithm
That’s it for analysis preparation. Now we need to transfer this data to a new page to calculate RFM values.
That’s it. We’ve made all the necessary calculations for RFM analysis in Google Sheets (or Excel). Bear in mind that although these formulas help to automate some miscalculations, you still have to spend a lot of time calculating RFM. But if you value your time and the time of your employees, or if you have a large database, we recommend performing RFM analysis using OWOX BI. If you aren’t an OWOX BI user yet, you can try all the functionality for free. How to use RFM analysis in marketingWhen all the calculations are ready and you’ve segmented your customers, it’s time to move on to the marketing part. By grouping customers by RFM values, you can immediately get a complete picture of what’s happening with your customer base. Let’s look at examples of some client groups. Group 3R-3F-3M – the most active, buy often These are your ideal customers. It’s possible to expand your engagement with them by launching a loyalty program, inviting them to special events, or asking them about how they would want the company to develop. It’s important to show these customers that they’re respected and welcome users. Although these customers seem to be the least promising, you shouldn’t write them off completely: they showed interest in your products at least once. Most often, marketers prepare special provocative messages to divide these customers into “definitely disinterested in the product” and “promising.” Promising customers can be transferred to the next category. 1 in one of the categories Some of your customers may have a value of 122 (lame Recency). This segment should be given a little time to decide about returning to you. Try to offer them products that are usually bought along with those that they purchased earlier in order to arouse a renewed interest in your company. 3 in one of the categories These users are a promising segment for your research. They’re consistent enough for you to experiment and find a suitable way to raise their other indicators. Useful materials to help you master the topic:Wrapping upThe RFM methodology is far from absolute, but it’s an extremely useful tool for analyzing your customer base. With just a little bit of work, you’ll see how to take an individual approach to your customers. At the same time, keep in mind that data is influenced by seasons, promotions, and holidays. If a customer with an extensive purchase history for the current month doesn’t buy anything next month, this doesn’t mean you should immediately transfer them to another segment. Perhaps this is just the effect of seasonality, and after a while, they’ll resume purchasing. Which are the three factors considered in an RFM analysis?RFM is a strategy for analyzing and estimating the value of a customer, based on three data points: Recency (How recently did the customer make a purchase?), Frequency (How often do they purchase), and Monetary Value (How much do they spend?).
What are the steps of RFM analysis?5-Step Approach to RFM Analysis. Step 1: Relevant Data Assembly.. Step 2: Setting Up RFM Scales.. Step 3: Score Designation.. Step 4: Segment Classification.. Step 5: Personalization of Strategies for Relevant Segments.. What is RFM calculation?RFM (as in Recency, Frequency, Monetary) is a marketing technique used to analyze customer value based on three variables: Recency: number of days since the last purchase or order. Frequency: average orders during a certain period (for instance, number of monthly purchases).
What are the parameters used in RFM analysis?The Parameters of RFM Analysis
The RFM score is the aggregate of three parameters: recency, frequency, and monetary value.
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