Real attribution work, real business outcomes. Every metric verified against closed CRM data. No platform screenshots. No modeled estimates.
The problem: Cedar & Stone Roofing was running $68K/month across Meta, Google Search, and Google Display. Their platform dashboards showed strong conversion volume. Their CRM showed something different — only a fraction of those "conversions" ever became signed contracts.
The insight: After connecting their ad platforms to HubSpot and verified invoice data, we found that 42% of Meta spend was attributed to leads who never closed. Google Display was generating clicks at scale with zero correlation to closed revenue. Meanwhile, Google Brand exact-match was producing a 6.1× true ROAS and was significantly underfunded.
The outcome: We reallocated $28K/month from Meta prospecting and Display to Google Brand and LSA campaigns. Within one quarter, CPA dropped 51% and true ROAS rose from 1.2× to 3.8×. The $340K in recovered budget was reinvested into channels with verified revenue track records.
The problem: Halcyon Logistics had a 9-month enterprise sales cycle. Their attribution stopped at demo request. LinkedIn spend was being justified on faith — no one could prove it was producing revenue. Finance was pushing to cut the LinkedIn budget entirely.
The insight: We built a full pipeline source model connecting LinkedIn ad impressions and form fills to Salesforce opportunity data and CRM-closed revenue. Using time-decay attribution calibrated to a 9-month cycle, we traced $2.1M of closed pipeline to LinkedIn touchpoints that would have shown zero credit in a last-click model. LinkedIn was the primary assist channel for 73% of enterprise deals.
The outcome: The LinkedIn budget was preserved and increased by $18K/month. Sales and marketing aligned on a shared attribution framework. Win rate improved 62% as the sales team received better-qualified pipeline with richer touchpoint history — allowing for more relevant outreach at each stage.
The problem: Meridian Outdoor Co. was running a blended paid media strategy across Google Shopping, Meta, and Pinterest. Their optimization strategy was built on add-to-cart events — because that's what their pixel data made easy to track. Their agency was reporting strong engagement across every channel.
The insight: We rebuilt their entire attribution layer around Stripe-verified purchases, eliminating reliance on pixel-reported events that were inflated by cart abandonment. When we mapped ad spend to actual completed transactions, the channel picture inverted entirely. Google Shopping was driving a verified 7.2× ROAS. Meta was producing a 1.1× true ROAS — effectively breaking even after platform fees and creative costs. Pinterest was worse.
The outcome: $22K/month shifted from Meta and Pinterest to Google Shopping and Performance Max. Blended CAC dropped 38% within two months. Annual revenue run rate increased by $900K with no increase in total ad spend. The brand had found the highest-performing channel in their stack — they just couldn't see it through pixel-based reporting.
Every business we've worked with had the same reaction when they saw their real attribution data: "We had no idea." Book a free audit call and find out what your numbers are actually saying.
Every metric in every case study is verified against closed CRM data. We don't report platform numbers.