Value-based optimization (VBO) with real-time LTV: Facebook

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 process with Facebook with one of our customers ($80M beauty box), where our ROI is even higher (18%):

Value-based bidding (VBB) on Facebook

Goal: Allow Facebook Ads to optimize bidding toward acquiring customers with higher LTV.

Test methodology: VBB test

• Push real-time LTV back to Facebook for all conversions identified via the Facebook Click ID collected and resolved across domains by Ocuboost^TM 
• Use Facebook Experimentation tool to set up A/B test
• Treatment and control arm listen to the data for two weeks, and are exact replica of existing campaign
• Kick off treatment in 2 phases:

‍Planned timeline:  This test will run for at least 5 weeks unless statistically significant results are garnered sooner.

• Run phase 1 until 30 click-through purchases are achieved in the last 7 days
• Run phase 2 until 100 conversions are generated in both treatment and control arm to assess results with significance

‍Achieved Result: 18%+ increase in LTV:CAC

‍Significance: Increase first-year gross profit from acquisition media spend by 18%

Impact for every $10M ad spend: Increase annual returns by $3.2M, from $17.7M to $20.9M – a 18% incremental increase in 12 months gross profit