10. Lead-Gen & Repeat-Visit Behavioral Engine
Overview
Built an algorithmic behavioral intelligence engine designed to distinguish high-propensity prospective buyers from casual digital browsers by analyzing micro-interactions throughout the user journey. Extracted and normalized multi-dimensional clickstream telemetry—including scroll velocities, page transition latencies, category dwell times, and repeated search queries—from Google Analytics and custom tag deployments. Developed predictive classification models in Python using scikit-learn and pandas to estimate repeat-visit likelihood and 7-day conversion propensity scores across incoming visitor sessions. Formulated automated webhooks and tag manager integrations dispatching dynamic scores into CRM queues and advertising platforms, enabling real-time personalized site banners and automated email sequences. Validated model accuracy and revenue uplift through structured multi-variant A/B experiments, lifting client retargeting campaign conversion rates by over 28% while reducing overall paid advertising acquisition expenditures.
Challenge
Online service providers and high-ticket B2B businesses struggled with high ad spend on generic retargeting campaigns because they could not distinguish high-intent visitors from low-intent casual web traffic.
Approach
- Engineered custom behavioral telemetry instrumentation in Google Tag Manager tracking micro-interactions (scroll depth, pricing page dwell, documentation queries).
- Trained supervised machine learning models in Python using scikit-learn (Random Forest, Logistic Regression) on historical conversion sessions.
- Built real-time scoring hooks dispatching propensity tiers directly into Google Analytics custom dimensions and client marketing automation tools.
- Executed controlled A/B testing campaigns tailoring dynamic promotional offers and personalized chat prompts to high-propensity segments.
Outcome
Boosted marketing conversion rates by 28% on targeted user segments while lowering overall retargeting ad expenditure by filtering out cold, low-propensity traffic.