Summary of Re-rfme: Real-estate Rfme Model For Customer Segmentation, by Anurag Kumar Pandey et al.
RE-RFME: Real-Estate RFME Model for customer segmentation
by Anurag Kumar Pandey, Anil Goyal, Nikhil Sikka
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an end-to-end pipeline, RE-RFME, to segment customers into four groups (high value, promising, need attention, and need activation) based on their dynamic behaviors. The RFME model tracks behavioral features such as recency, frequency, monetary, and engagement to segment users. A K-means clustering algorithm is trained to cluster users into one of the four categories. The effectiveness of this approach is demonstrated on real-world datasets from Housing.com for both website and mobile application users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps online platforms better understand customers and design effective marketing strategies. It proposes a new way to group customers based on their behaviors, which can lead to more targeted marketing efforts. The approach uses data about how often customers engage with the platform, when they last used it, and how much they’ve spent. This information is then used to sort customers into four groups: those who are valuable, those who have potential, those who need attention, and those who need activation. The paper shows that this approach works well on real-world data from Housing.com. |
Keywords
» Artificial intelligence » Attention » Clustering » K means
