Los Angeles Apparel is a vertically integrated manufacturer challenging the fast-fashion model. With a deep focus on sustainability and fair wages, they handle all knitting, dyeing, and cutting in-house to produce high-quality, American-made basics.
Profitability at Scale
Los Angeles Apparel was already running sophisticated campaigns, but they faced the classic e-commerce trade-off: increasing volume usually meant sacrificing efficiency. Their specific goals were to:
The "Short-Term" Revenue Trap
Like most advanced retailers, Los Angeles Apparel was already using Value-Based Bidding (tROAS) on Google Ads. However, standard ROAS bidding creates a blind spot: it optimizes strictly for the immediate purchase value... forgetting that some custoemr return items, and that some customers will come back and re-purchase.
Google’s algorithm was successfully capturing short-term revenue, but it couldn't distinguish between a one-time discount shopper and a loyal, high-value customer. As Los Angeles Apparel tried to scale, the algorithm naturally found immediate revenue that looked good on day one but failed to deliver long-term profitability, effectively capping the growth potential.
Predictive Value Optimization
We launched a Google experiment on Los Angele Apparel's Performance Max campaigns to enhance the bidding strategy. We moved away from standard Revenue bidding and implemented a strategy focused on Predicted Long-Term Value.
By enriching the conversion data, we trained Google’s algorithm to look beyond the immediate purchase value. We signaled which users were likely to generate higher value over 90 days, prioritizing high-intent behaviors and cart compositions indicative of both returns and retention. This allowed the algorithm to bid more aggressively on future loyalists while pulling back on one-and-done buyers.
Scale Meets Efficiency
The experiment proved that you don't have to choose between growth and ROAS. By optimizing for longer-term value:
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