LTV: Life Time Value Customers and RFM analysis
I’m trying to discover my life time value customers, I’m not experienced doing this so I’m trying to figure it out….seems one way to do this is by conducting what is called as RFM analysis. Recency, Frequency, Monetary Value. All these seems achievable in FS.
I’m curious if anyone has attempt doing this before. Asking ChatGPT how to do this in Fullstory 😉 I got the following instructions:
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Recency (R): Use FullStory's search functionality to identify users who haven't visited your site recently. You can search for sessions that occurred within a specific timeframe, such as the last 30, 60, or 90 days. Look for patterns in user activity and identify customers who may be inactive or at risk of churn.
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Frequency (F): Analyze user sessions to understand how often customers interact with your website. Look for repeat visits, frequent interactions with key features or pages, and patterns of engagement. FullStory's session replay feature allows you to watch individual user sessions and track their interactions over time.
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Monetary Value (M): While FullStory doesn't directly track monetary transactions, you can still infer purchase behavior based on user activity. Look for sessions where users add items to their cart, initiate checkout, or complete purchases. Analyze the frequency and value of these transactions to identify high-value customers.
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got a bit more guidance from chatGPT, lol.
Identify Key Events: Determine which events in FullStory correspond to actions that indicate recency, frequency, and monetary value. For example, you might consider "Purchase Completed" events for monetary value, "Visit" events for frequency, and "Last Visit" events for recency.
Create Segments: Use FullStory's segmenting capabilities to create segments based on these key events. For instance, you could create a segment of users who have completed a purchase within the last 30 days for recency, a segment of users who have made more than three purchases for frequency, and a segment of users whose total purchase value exceeds a certain threshold for monetary value.
Analyze Behavior: Once you've created these segments, you can use FullStory to analyze the behavior of users within each segment. Look for patterns and trends in how users interact with your site, such as common paths to purchase, areas of friction, or opportunities for upselling or cross-selling.
Export Data: FullStory allows you to export session data and other analytics, which you can then combine with your transactional data from your e-commerce platform. This combined dataset can be used to calculate RFM scores and perform further analysis using tools like Excel, Python, or specialized analytics software.
Iterate and Optimize: Continuously refine your segments and analysis based on new insights and changes in user behavior. Experiment with different segment definitions and strategies to identify what works best for your business goals.
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