January 6, 2025
v 1.2.6
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
Use Case:
An e-commerce brand sells conversion tools and funnel templates via ClickFunnels. They upload a curated list of 50,000 Instagram leads, pre-filtered by ad spend ($50K+/month), into their CRM. Riddler’s engine scores these users using interaction data from Instagram—including saves, swipes, CTA clicks, and influencer affinity—combined with verified ad spend enrichment.
The goal: Identify the top 30,000 leads most likely to purchase a $1,200–$2,500 funnel asset bundle or subscribe to a $297/month SaaS plan.
Results:
Leads scored ≥ 0.81: 30,000
Conversion Rate for top tier (scored ≥ 0.81): 34.2%
Baseline (full 50k list): 6.8%
Efficiency Lift: 5x higher ROAS, 70% lower CAC per conversion
Campaign Result: Generated $18.3M in funnel sales in 45 days with only $430K in ad spend