We developed a customized CRFM (Conversion, Recency, Frequency, Monetary) model for a large caregiver company to provide deeper insights into their customer base and enable the tracking of changes on a weekly basis. This project was a part of a broader effort to enhance the client’s ability to understand and engage their customers more effectively.
The client sought to better understand their customer base, particularly to identify and track active customers on a weekly basis. The challenge was to create a model that could accurately classify active customers, even those without direct revenue footprints during the review period. Traditional RFM models were inadequate due to the client’s specific business dynamics, which included subscription services and non-purchase-based customer activities. The client required a tailored approach that would capture the complexity of their customer interactions and provide actionable insights.
After detailed discussions with the client’s internal marketing and data teams, we proposed and built a custom CRFM model. This model expanded on the classic RFM framework by introducing a Conversion (C) index to account for customers who converted to paid subscriptions. The model was designed to integrate data from multiple sources, including direct purchases, subscription activity, and Amplitude events, which track customer engagement without direct revenue.
Customers who made at least one transaction, excluding refunds, within the review period.
Customers who triggered specific Amplitude events (e.g., open, create, click) within the review period, indicating engagement without direct purchases.
Customers with an active paid subscription during the review period.
We consolidated data from the client’s multiple sources to define a customer as active if they exhibited any of the three main activities: revenue, Amplitude events, or subscription status. This allowed us to capture a broad spectrum of customer behaviors, ensuring a comprehensive analysis.
The introduction of the Conversion (C) index allowed us to build a CRFM model that segmented customers based on Recency, Frequency, and Monetary value, tailored to the client’s business model.
The segmentation process involved calculating indices:
Using a K-Means clustering algorithm, we segmented customers into eight meaningful buckets, such as “Active,” “Rare,” “Endangered,” and “Most Valuable.” Additional buckets, like “Snoozers” (frequent users of discounted subscriptions) and “Fresh” (recently signed-up customers), were created based on specific business data. This bucketing process allowed the client to better target their marketing and retention efforts.
We built a Tableau dashboard to visualize the CRFM data. The dashboard included a transition matrix that tracked weekly changes in customer segments, providing the client with detailed insights into how customers moved between different buckets over time. This tool became essential for the client’s marketing and data teams, enabling them to make informed decisions based on real-time data.
The model segmented the customer base into 8 actionable groups, allowing the client to tailor their marketing strategies to specific customer behaviors. This led to more targeted and effective campaigns.
The transition matrix and weekly reports provided a clear view of customer behavior, enabling the client to quickly respond to changes and make data-driven decisions. This contributed to more agile and effective marketing strategies.
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Hello,
Thank you for your interest in Stellan’s Data Services. After you fill out this form, one of our team members will get in touch with you at the requested time to discuss your tech initiative.
We can’t wait to deliver the best results to you!
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