How RFM analysis can be used to filter and prioritize top clients

    RFM analysis

    Recency, Frequency, and Monetary Value analysis is a great way to segment the client segments and identify the top clients along three dimensions

    Enterprises are aware of the 80/20 theory where 80% of results are due to 20% of the factors. When the theory is applied to a client base, marketers point out that the most significant chunk of revenue is due to a minor fraction of the clients. By logic, the organization’s marketing campaigns and customer service needs to be targeted on the revenue-generating clients.

    However, when organizations apply the theory to clients and analyze the revenue, it’s understood that significant parts of the business architecture are not being taken into accounts. There are other metrics by which client value can be measured.

    The significant disadvantage of focusing on one single group is that dependent enterprises will be in deep trouble if the client base drops out. CMOs say that targeting a single group is equivalent to a one-dimensional view of the audience.

    It is vital to develop better methods for customer prioritization, increase focus on them, and keep them satisfied. Marketing leaders consider RFM to be a fool-proof method to organize client segmentation and division based on three metrics.

    RFM analysis can be easily deployed to provide enhanced service to clients, division of email list, and customization of enterprise communication.

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    Understanding RFM analysis

    Recency, frequency, and monetary value analysis is an established manner of segmenting clients.

    Recency is the last instance a client went through with a deal. Most recent clients will remember the business transaction clearly and are good targets for the marketing campaigns.

    Frequency refers to understanding which clients are known to transact regularly with the brand. Frequent clients are familiar and like the marketing strategy and excellent recipients for email campaigns and other techniques.

    Monetary value metric is required to understand the client’s intent and power to buy into an account. Clients with large purchases are critical to the organization. It is essential to identify them and ensure continued positive engagement to boost the organization’s chances of better sales.

    CMOs believe that RFM analysis helps provide a different view to the marketers, allowing segmenting and target clients with relevant marketing techniques. The main reason behind the usefulness of this analysis method is its multidimensionality. By blending three factors of the analysis, marketers can develop customer segments through various methods. The client will be prioritized based on different dimensions.

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    Tools and platforms relevant to RFM

    CMOs acknowledge that enterprises may not have the data needed to complete an RFM analysis. CRM portals and Google Analytics will marketers collect and analyze the end-users’ data to understand their behavior. The organization’s eCommerce platform will have data on transaction behavior as well. With this data, a simple excel spreadsheet will be enough to evaluate the RFM analysis.

    Deploying the analysis to client segmentation

    Email segmentation is the most basic application for RFM analysis. When the email list is divided into segments, it is possible to develop better-personalized content. Chances of increased sales are boosted by customized and targeted content.

    Clients that score well on all metrics are most crucial for business. However, it’s also possible that they are part of a small group, and a different strategy is required to satisfy the other clients. More attention to medium clients, avoiding low RFM score clients, offering discounts, loyalty points, and special rates to attract more clients are good ways to focus on the non-top RFM client base.

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    Megana Natarajan
    Megana Natarajan is a Global News Correspondent with OnDot Media. She has experience in content creation and has previously created content for agriculture, travel, fashion, energy and markets. She has 3.9 years’ experience as a SAP consultant and is an Engineering graduate.