Conference & Awards: Kim, D., Kim, Y., Kang, G., Shin, J., Kim, D., & Moon, H. (2025). Profiling the User of Real Estate Tax Tech Market. Journal of The Korean Data Analysis Society, 27(1). Awarded the Poster Encouragement Award.
Figure 1. Poster
As the tax tech market expands, understanding the exact behavioral profiles of digital service users becomes crucial for targeted marketing and service optimization.
📌 The Problem: Lack of Empirical User Data
Despite the rapid digital transformation in the tax sector (e.g., the National Tax Service’s ‘One-Click Service’), empirical research analyzing the actual users of private tax solution services remains severely limited. Without understanding who is using the service and how they transact, real estate tax tech companies struggle to provide customized experiences and optimize their marketing strategies.
⚙️ The Method: RFM and K-Means Clustering
To decode user behavior, this study analyzed nationwide real estate capital gains tax filing data alongside actual user data from a commercial tax solution (NEWEYE Corp.).
- Demographic Goodness-of-Fit: Compared the demographic distribution of nationwide filers with the service’s actual users.
- RFM Transformation: Converted users’ raw transaction history into Recency, Frequency, and Monetary (RFM) variables.
- Clustering: Applied the K-Means clustering method (optimized at k=5 via Silhouette score) to segment the diverse user base into distinct subgroups based on their behavioral patterns.
🚀 The Impact: Data-Driven Market Strategies
The analysis revealed significant demographic shifts and provided actionable business intelligence:
- Demographic Targeting: The goodness-of-fit test revealed a significantly higher adoption rate among male users in their 40s and 50s, while utilization by women and users 60+ was relatively low, indicating a need for tailored accessibility strategies.
- 5 Distinct User Segments & CRM Strategies:
- Cluster 1: Core Users (9.89%): Showed the highest Frequency, likely representing multiple-property owners or investors. Strategy: Advanced feature development.
- Cluster 2: New Users (34.11%): Characterized by high Recency but low Frequency. Strategy: Effective onboarding to encourage repeat usage.
- Cluster 3 & 5: (Ultra) High-Value Users (8.02% combined): Exhibited overwhelmingly higher average transaction values. Strategy: Specialized premium services and professional consultation.
- Cluster 4: Inactive & Lapsed Users (47.98%): The largest segment with the lowest Recency, typical in event-driven services like tax filing. Strategy: Long-term CRM strategies to foster loyalty and encourage returns.
This research provides critical empirical evidence for tax tech companies to deploy data-driven, personalized service strategies optimized for each user segment’s specific needs.