Ocurate’s value-based optimization (VBO) solution leverages Machine Learning for customer value and integrations into advertising platforms to increase the Return on ad spend (ROAS) by 15%+.
Over the last years, Google, Facebook, and TikTok have built out capabilities to push data back into the ads APIs.
VBO leverages that technology by infusing the APIs with predictions of future per-customer revenues. Predictions are made immediately upon sign-up and exceed 90%+ accuracy.
The platforms can now allocate bids for ad impressions based on customer value as opposed to first purchase. The result: increasing ROAS without increases to the existing ad budget.
Below, we describe the VBO process with Google non-branded search for one of our customers, Curology ($200M+ revenue D2C skincare brand), where our ROI is even higher (16%+):
Goal: Allow Google Ads to optimize bidding toward acquiring customers with higher LTV.
Test methodology: Matched Market Test
Planned timeline: This test will run for 8 weeks unless statistically significant results are garnered sooner.
Achieved Result: 16% increase in LTV:CAC
Significance: increase first-year gross profit from customer acquisition by 16%
For every $10M ad spend: Increase annual returns from ad spend by $920K, from $5.72M to $6.64M