Case Study · Moving & Relocation
Third-party leads replaced in 90 days. Off-season revenue at 13 months exceeded the prior peak season. 2.5x ROAS sustained at $100K+ peak daily spend (about $3M+/month).
A nationwide moving broker came to LGG Media spending $20K per day (about $610K/month) on third-party leads. The leads were non-exclusive, sold to competing brokers at the same time. Close rate on third-party inbound calls was approximately 5%.
The cost per inbound call was $130 and per form submission $90. ROAS held at 1.5x and would not move regardless of spend level. Every attempt to increase volume made the economics worse.
Non-exclusive leads meant the brokerage was competing against other buyers of the same data for the same prospects. It was running acquisition budget against its own lead sources at a 1.5x return that left no margin for scaling.
Third-party leads trained the campaigns on a signal with no relationship to revenue. The algorithm found more traffic that produced form fills and inbound calls.
It had no information about whether those calls became booked moves, or what those moves were worth. Without revenue data flowing back, there was no way to scale past the 1.5x ceiling.
Every booked move on the third-party model carried the full cost of 20 unbooked calls before it. The algorithm was finding callers, not customers.
LGG Media launched on $500 per day with Google Search campaigns bidding on form submissions as an initial conversion signal. First sales came within three weeks. Within 90 days, spend had scaled to $20K per day (about $610K/month) on owned channels and every third-party lead source had been replaced.
Tracked quote and estimate data. When a prospect received a moving quote, that event was passed back to the lead record and synced to the ad platforms.
Tracked cash collected. When a booking was confirmed and payment processed, the actual revenue value was passed back to Google and Meta as an offline conversion event.
Attribution accuracy reached 93% exact click attribution across all transactional events. Quote requests, bookings, and collected payments all matched to the originating ad click. For the tracking mechanics behind this, see how attribution infrastructure works →
With actual booked revenue flowing back to the algorithm, the campaigns could identify which keywords, audiences, and query patterns produced customers who booked and paid, rather than customers who called and did not convert.
LGG Media set a fixed target ROAS of 2.5x and let Google's algorithm spend as much as it could within that profitability constraint. No manual spend caps were applied.
The algorithm absorbed seasonal demand fluctuations automatically while the revenue target held the profitability floor. A fixed tROAS without manual caps is the correct approach in a seasonal business, because demand fluctuates significantly between peak season (May through August) and off-season months.
Manual caps would have suppressed spend during high-demand periods when the algorithm could have found more profitable customers at the same 2.5x return. Removing the caps let it scale into demand as it appeared.
The math: at $100K per day (about $3M/month) in ad spend, a sustained 2.5x ROAS produces $250K in daily revenue. The 2.5x target was not a starting floor, it was the sustained rate held across all spend levels from $500 per day through $100K+ per day (about $3M+/month).
Quote-to-booking conversion on owned channel leads ran at 30 to 40 percent throughout the engagement, compared to the roughly 5 percent close rate on third-party shared leads. That is a 6x to 8x improvement in conversion rate on the same sales team handling the same inbound volume.
The difference was not the sales process. It was that owned channel prospects had not simultaneously received the same call from six competing brokers.
Daily ad spend stabilized at $40K to $60K (about $1.2M to $1.8M/month) as the sustained off-season baseline, with peaks above $100K (about $3M/month) during moving season. ROAS held consistently at 2.5x across all channels throughout scaling.
Google published two separate case studies on clients in the moving and relocation vertical managed by LGG Media in 2025, both documenting results from this same owned acquisition infrastructure. View the Google-published case studies →
Published by Google Ads: Google released two official case studies on LGG Media moving clients. Mover Matcher drove 28.5% more leads for long-distance moves with AI Max, and Coastal Moving Services increased long-distance conversions by 88%.
Read the Mover Matcher case study (PDF) → · Read the Coastal Moving Services case study (PDF) →
By February 2024, 13 months after launch, off-season daily revenue exceeded what the best summer peak month had produced under the third-party model the prior year. February is historically the lowest-demand month in the moving industry. The off-season baseline had become more valuable than the previous peak season.
The reason is algorithm learning. Each month of accurate revenue data, booked moves, collected payments, matched to originating clicks, made the algorithm's predictions more precise.
By month 13, the model had a full seasonal cycle of high-quality signal. It knew which queries, audiences, and geographies produced customers who booked long-distance moves and paid in full. That learning does not reset between seasons. It compounds.
The $20K per day (about $610K/month) ceiling on third-party leads was not a market size limitation. Third-party volume at that ceiling produced a 1.5x ROAS. Owned channel infrastructure at $100K+ per day (about $3M+/month) produced a 2.5x ROAS. The difference was the quality of the signal, not the size of the market.
Explore the approach: Moving leads → · Lead generation → · Pay-per-click management → · All client results → See how the underlying tracking and bidding work in our guides to Google Ads offline conversion tracking and target ROAS.
Next Step
If ROAS has plateaued at every spend level and volume increases make the economics worse, the conversion signal reaching the algorithm is the problem.
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