Case Study · Auto Transport
Three months building. 21 days scaling. $700 per day to $10K to $12K per day (about $300K to $360K/month), a 14x to 17x increase in daily spend. Daily revenue from $1,200 to $20K to $24K. 86% attribution accuracy at four months old.
A startup auto transport brokerage launched in October 2025 with zero advertising infrastructure, no CRM integration, and no payment processing connection.
The CRM had limited native API endpoints for event-based conversion data. The payment processor output raw transaction payloads in a format that required normalization before any attribution system could process them.
Both systems generated duplicate events under standard load conditions. There was no clean path from transaction data to advertising platform without building one.
Scaling spend before that path existed would train the algorithm on noise rather than signal. Performance at $10K per day (about $300K/month) on a flawed data foundation is worse than $700 per day on accurate data.
The defined threshold before the scale decision was 80% of transactions matched to originating ad clicks.
That threshold determined the timing of the 21-day scale event, not a calendar date, not a budget target, not a campaign performance milestone.
The payment processor generated raw payloads with two specific problems. Both produced the same downstream failure: the algorithm receiving multiple conversion signals for a single transaction, inflating reported revenue and distorting ROAS.
The data format used non-standard field naming that did not map to the conversion event structure required by Google Ads or Meta.
Network retry logic caused duplicate payload submissions, the same transaction arriving two or three times with identical values but different timestamps.
The CRM had limited native event-based API capabilities. Quote events and record updates could not pass to an attribution layer without custom middleware. Every system in the client's stack required a custom integration rather than a native connection. Feeding this data at $700 per day is a recoverable error. Feeding it at $10K per day (about $300K/month) trains the algorithm on noise at a scale that takes months to correct.
LGG Media built a proprietary deduplication layer between the payment processor and the attribution server, the same infrastructure used across the BHPH automotive finance engagement.
The layer identifies duplicates using transaction ID matching and timestamp windowing, removes them, normalizes the field structure, validates against expected value ranges, and passes a single clean record to the attribution server.
LGG Media deployed the proprietary phone tracking system, the same number pool used in the health insurance engagement, and built CRM middleware for quote event tracking across all digital assets.
Explore the approach: Auto transport leads → · Lead generation →
The attribution server matches each clean transaction to the original ad click via device fingerprinting and click ID, then syncs the conversion and revenue value to Google and Meta.
$700 per day was the chosen validation spend. It generates enough inbound events to train the deduplication layer and begin algorithm learning, while keeping cost low enough that inaccurate early data does not corrupt the algorithm at scale. The three months were a defined build period with a defined exit criterion.
In early January 2026, attribution accuracy reached 80% of transactions matched to originating clicks. The readiness threshold had been met. LGG Media declared the infrastructure ready and began the scale event.
The scale event ran over 21 days. Daily spend moved from $700 to $10K to $12K (about $300K to $360K/month), a 14x to 17x increase. The algorithm absorbed the increase without performance degradation because it had been receiving accurate conversion data for three months before scale began.
The alternative, scaling immediately with unvalidated data, would feed duplicate and malformed signals to the algorithm while its predictive model was forming. A model trained on noise at formation does not self-correct when data quality improves later. It requires retraining, which takes months of accurate data to override.
Three months at $700 per day cost approximately $63K in validation spend. The cost of retraining a corrupted algorithm at $10K per day (about $300K/month) is measured in degraded performance across months of scale spend.
Daily revenue grew from $1,200 at launch to $20K to $24K at current spend levels. ROAS improved from 1.7x in the initial weeks to 2.0x sustained as the algorithm accumulated data. Attribution accuracy reached 80% at the January 2026 scale declaration and 86% at four months, a rate that reflects a mature attribution infrastructure, not a four-month-old account.
At 2.0x ROAS, every $1 in ad spend produces $2 in revenue. Against a $450 average order value per shipment and a $350 customer acquisition cost, the $100 gross margin per shipment before operational costs is the unit economics that makes the model viable at scale.
The constraint on further scaling is sales team capacity, not campaign performance. Auto transport brokerage requires human sales interaction to quote and close each shipment.
Current inbound lead volume from the campaigns exceeds the sales team's handling capacity. Growth from this point is tied to operational hiring, not campaign spend levels.
The business is on a $7M to $9M annual revenue run rate. The campaigns outperformed the client's operational capacity within four months of launch. That outcome is the result of the three-month build period producing a model that finds the right customers rather than the most customers.
“Working with Juan and his team has been nothing short of amazing. I would recommend these guys to anybody trying to build their company into something bigger than they ever could have imagined.”
Joshua G., Founder, Auto Transport Brokerage See how the underlying tracking works in our guides to Google Ads call tracking and offline conversion tracking.
Next Step
A three-month infrastructure validation period at controlled spend before a 21-day scale event produced 86% attribution accuracy and a $7M to $9M annual revenue run rate. Scaling before the signal is accurate trains the algorithm on noise at a cost that takes months to correct.
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