What data do we need as input for RFM analysis?
For RFM analysis, we will need complete transaction data at the level of a specific order/customer . As a rule, we use input data from CRM systems, or transaction data from e-shops. The dataset must contain the date of order placement, order identifier and customer identifier, as well as the nominal value of the transactions made. I recommend performing RFM analysis on complete transaction data containing information about purchased products, product purchase prices, product classification, etc. To provide a holistic overview of the behavior of the customer base and subsequent application of the outputs from RFM analysis, I recommend enriching the transaction data with marketing data (primarily data from e-mailing automation tools + web tracking such as Google Analytics, or other attribution tools).
Many companies are just starting
to implement data democratization initiatives, in the sense of centralizing data sources to a central repository, linking individual marketing linkedin database systems, properly implementing analytical tools, etc.
Today’s dynamic environment of the digital world requires regular recalculation of segments and automation of input data to ensure the up-to-date classification of customers and for further application in communication channels. Although basic RFM analysis can also be executed in Excel, for larger volumes of data, I recommend using Python or another BI/ETL tool for data transformation and subsequent visualization in Power BI or Tableau for internal reporting. Most often, RFM analysis outputs are displayed in a cohort representation, a two-dimensional heat map in a matrix or a histogram.
2) Setting rules for customer segments
RFM analysis rates customers on each of the three main factors. Typically, a score from 1 to 5 is used, with 5 being the highest. However, different implementations of the RFM analysis framework may use different values or scaling.
Let’s take an example:
Customers are assigned a recency score based on their last purchase date or the time interval since their last purchase. This score is based on a simple singapore data arrangement of recency values into a small number of clusters. For example, if you use five categories, customers with the most recent purchase dates would receive a recency score of 5, and customers with the most recent purchase dates would receive a recency score .
Finally, customers are ranked by monetary value, with the highest values earning the highest monetary values. Continuing with the example with five categories, customers who spent the most would earn a monetary rating of 5.