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

Scoring Model:

Lead Score=(0.30⋅F1)+(0.20⋅F2)+(0.15⋅F3)+(0.10⋅F4)+λ⋅P(x)\text{Lead Score} = (0.30 \cdot F_1) + (0.20 \cdot F_2) + (0.15 \cdot F_3) + (0.10 \cdot F_4) + \lambda \cdot P(x)Lead Score=(0.30⋅F1​)+(0.20⋅F2​)+(0.15⋅F3​)+(0.10⋅F4​)+λ⋅P(x)

• F1F_1F1​: Saved carousel ads featuring high-ticket funnels (Weight: 0.30)

• F2F_2F2​: Clicked “Start Trial” or “Swipe Up” CTA on an influencer story promoting ClickFunnels (Weight: 0.20)

• F3F_3F3​: Follows 3+ verified funnel-building influencers (e.g., @russellbrunson, @clickfunnels, @funnelhacker) (Weight: 0.15)

• F4F_4F4​: Matched ad account spend > $50K/month via data enrichment from Meta API or Clearbit (Weight: 0.10)

• P(x)P(x)P(x): Conversion probability generated from behavioral model trained on post-click sales data, retargeting sequences, and influencer DM reply rate

• λ=1.2\lambda = 1.2λ=1.2: Behavior-heavy weighting to emphasize intent signals over static attributes