These case studies represent actual client work (names changed for confidentiality). Results vary based on industry, budget, creative, and market conditions.
High ad spend with declining ROAS and inconsistent lead quality across Google and Meta. Creative fatigue was rapid and audience targeting was too broad.
Implemented predictive intent modeling, dynamic creative optimization, and real-time bid adjustments. AI identified high-intent micro-segments and automatically shifted budget to best-performing creative variants.
Low volume of qualified marketing qualified leads (MQLs). Long sales cycles and difficulty scaling paid acquisition profitably on LinkedIn and Google.
Deployed predictive lead scoring models, AI-enhanced LinkedIn audience targeting, and automated nurture sequences that qualified and routed leads faster to sales.
Extremely high cost per lead in competitive local search market. Inconsistent lead quality and difficulty booking jobs from digital channels.
Combined local AI search intent targeting, smart bidding on high-converting job types, and landing page personalization based on search query and device.
