January 15, 2025
v.1.2.5.9
Lead Score=(0.35⋅F1)+(0.25⋅F2)+(0.15⋅F3)+(0.10⋅F4)+λ⋅P(x)
F1F_1F1: Viewed 3 or more luxury property listings (Weight: 0.35)
F2F_2F2: Spent more than 2 minutes on any property detail page (Weight: 0.25)
F3F_3F3: Requested a private showing within the last 14 days (Weight: 0.15)
F4F_4F4: Verified income over $200K from Clearbit or similar data provider (Weight: 0.10)
P(x)P(x)P(x): Predicted probability of home purchase within 30 days, generated by a logistic regression model trained on Zillow clickstream data and historical CRM close rates
λ=0.8\lambda = 0.8λ=0.8: Regularization parameter to balance signal weight and behavioral probability
Specific of the Client:
A luxury real estate CRM system receives 10,000 Instagram-based inbound leads over 90 days. Using the model above, each lead is scored in real-time based on social media engagement (Instagram content viewed, clicked, saved), website interaction, and enriched financial signals.
The model identifies 7,000 leads with scores ≥ 0.85. These leads receive priority outreach from high-performing agents, automated appointment scheduling messages, and dynamic retargeting campaigns across SMS and email.Editing Fields
Results:
Conversion Rate (Scored ≥ 0.85): 31%
Baseline Conversion Rate (Unfiltered): 3%
Lift: 933% improvement in conversion efficiency
Operational Impact: Cut agent call time by 61%, reduced CAC by 42%, and increased ROI on lead gen campaigns by 7.6x.