Value-based optimization (VBO) with real-time LTV: Google non-branded search with Curology

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%+):

VBB + real-time LTV on Google NB Search

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

Test methodology: Matched Market Test

  • Push real-time LTV back to Google for all conversions identified via OcuboostTM
  • Identify 2 regions in an existing campaign with similar demographics, spend and performance.
  • Exclude the two designated regions from the current campaign and adjust the budget accordingly.
  • Create 2 new campaigns mimicking the strategy of the current campaign: same ad copy and keywords
  • Control - targeting Region #1
  • Treatment - targeting Region #2
  • Run both the Treatment & Control campaigns mimicking the original strategy, allowing both campaigns to learn until behavior normalizes.
  • Once behavior normalizes - within the Treatment campaign, switch the campaign Goal to the new Lifetime Value conversion action, and set the bidding strategy to Max Conversion Value.

Planned timeline:  This test will run for 8 weeks unless statistically significant results are garnered sooner.

  • Run for 4 weeks before measuring results
  • Run for 4 additional weeks to leverage optimized LTV predictions

Achieved Result: 16% increase in LTV:CAC

Significance: increase first-year growth 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

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