From recency to monetary value: The RFM model and its extension in Mosaique 1.1
Introduction to the RFM model
With increasing customer acquisition costs (CAC) in a competitive marketing environment, it is becoming increasingly critical to maintain and maximize existing customer relationships (CLV). The RFM model, based on the three key metrics Recency, Frequency, and Monetary Value, provides insights into the engagement and value levels of a company's customers. These metrics are essential to understand how a company can build and increase customer loyalty to ultimately ensure profitability in increasing, cost-intensive acquisition strategies.
- Recency
Recency measures the time since a customer last made a purchase. Customers who have bought recently tend to be more likely to buy again. Prioritizing this customer group in marketing campaigns and selections can help to increase customer loyalty and increase customer value.
- Frequency
Frequency shows how often a customer has made a purchase within a specific period of time. Customers who shop frequently show greater loyalty and are less price-sensitive, making them ideal segments for cross-selling and upselling.
- Monetary value
This metric analyzes the total amount that a customer has spent in a period. High-spending customers are often the most profitable and it's beneficial to invest in these relationships to solidify their loyalty.
Application of the RFM model in marketing
The segmentation of customers according to these three RFM parameters enables the development of targeted marketing messages, with Mosaique being highly flexible and individually designing segments based on the respective business model. The customer base can thus be effectively divided into groups, which allow tailor-made approaches.
Preparing for Mosaique 1.1: An Extension to the RFM Model
The development of Mosaique 1.0 builds on the traditional RFM model and integrates additional customer profile information to further refine marketing strategies and tactics. Many companies are faced with the challenge that new customers acquired once are often difficult to convert into returning customers, in particular due to a lack of marketing permissions such as email or SMS. Mosaique 1.1 will make this issue transparent by integrating marketing permissions that are the real Contact- Explicit the potential of a segment and creates the initial basis for calculating the probability of a reactivation of new customers.
Conclusion
The continuous development of mosaics through additional data enhancements gives marketing and CRM teams more precise and dynamic insights into the customer base. The upcoming introduction of Mosaique 1.1 represents the next step to further optimize the effectiveness of marketing activities and maximize the ROI of marketing campaigns.