This tool aims to show the way to combine the analysis of customer segmentation using the RFM tool and the gender approach. It involves a high level of complexity, and requires medium knowledge of Power BI and knowledge of statistical analysis to execute. It is aimed at the managerial profiles and the marketing team of a company.
Tool 12. Data analytics for my company, from lens 3, is an introduction to data analysis or data analytics.
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This guide explains what the RFM (Recency / Frequency / Monetary Value) tool is and why it is key when analyzing customer bases in a segmented way to design loyalty plans. Likewise, it proposes combining the RFM analysis with sociodemographic variables of the clients, such as sex and age, to enrich it.
Companies that sell products or services to large groups of clients have the challenge of generating loyalty programs that allow them to be closer to their most valuable clientele. For this, the marketing areas need to be able to know who their clientele is and segment them based on different characteristics: one of them is the relationship between the client and the organization. The RFM tool allows us to analyze three characteristics of this relationship simultaneously and thus group customers based on the value they represent for the company. Another complementary way is to analyze the customer base based on their sociodemographic characteristics such as sex and age. By combining both segmentation criteria, more information is generated in order to design more personalized loyalty plans.
People are not a homogeneous group. Women and men have different realities from which the company-client relationship can be deepened and personalized. In this sense, the tool proposes adding an analysis of the sociodemographic characteristics to an RFM analysis, so that businesses can improve the way they reach their customers and add value to them through their products or services.
In terms of the gender approach, there is the opportunity to consider the sociodemographic characteristics of the people at the same time as segmenting the clientele based on the value for the company. This makes it possible to design loyalty plans that improve people’s satisfaction with the products or services offered to them. Likewise, by being able to plan in a more personalized way the generation of value by the company to different segments of people, it is possible to positively affect the lives of the people who use the products or services offered.
The RFM tool seeks to optimize actions for different customer segments and thus be able to maximize commercial results with it.
It is called RFM because it analyzes simultaneously:
- R-recency (when the last purchase was made);
- F-frequency of purchases (how often are purchases made, it can be in days, months or years); Y
- M- monetary value of purchases (price of purchases made).
For each of these variables, a score from 1 to 5 is calculated, where 5 is high and 1 is low. Once the different scores are obtained for each of the three variables, the clientele can be grouped according to these and begin to segment them.
RFM analysis is used when it is necessary to segment, order and / or group customers to think about specific loyalty actions in each of the segments.
The following table shows some examples of the types of clientele groups from the RFM calculation combined with the sex of the people. On these, loyalty plans can be designed that combine value for the company and a gender perspective.
In order to explain how to calculate the RFM, we use an example of a bicycle shop that has a history of customers who have purchased products from it. The database has data on the purchases made and the characteristics of the clientele in terms of sex and age.
In order to know which customers have the most value for the company, the RFM is calculated and then displayed on a dashboard that groups customers by different clusters. A cluster is a specific grouping of observations (in our case, clients and clients) based on some variables.
By means of a statistical technique, the observations that "most resemble each other" are grouped considering the selected variables. The clusters are analyzed based on the sex and age of the people. To do this, a dashboard is built where the clusters are displayed along with different filters.
In the following link you can view the dashboard with the analysis of the three variables:
- R> Average days elapsed
- F> Average amount of transactions
- M> Average price
You can also select on the gender of the people and see how the position of the clusters is modified based on this variable.
After calculating the R, F and M scores, clustering can be applied, as shown in the video tutorial, and segmenting the clientele groups by sex.
In the following link you can see a tutorial that explains step by step how to calculate the RFM and how to visualize the results based on clusters of clients and clients. Everything done in Power BI:
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