Value-Based Bidding with Predicted Lifetime Value in Google Ads Search : Design, Implementation, and Evaluation - Nedvěd, Vojtěch (2025)
This thesis develops and evaluates a value-based bidding (VBB) strategy for Google Ads Search by integrating user-level predicted lifetime value (pLTV) into the bidding system in a freemium environment with low conversion rates. Using first-party data (installs, orders, app usage, and in-app messaging), predictive models are trained on features from the first seven days post-acquisition to forecast outcomes over a 120-day horizon. Predicted purchase probabilities are then converted into pLTV by scaling them with each cohort’s average order value (AOV) over the same period, yielding a dollar-denominated signal directly usable for bidding. Data processing is implemented in BigQuery with Airflow orchestration, while model training and evaluation are conducted in Python on Vertex AI. Model selection prioritises stable and accurate ranking of users by their expected value, leading to the deployment of CatBoost in production. The impact of the approach is assessed through a two-month A/B test (June - July 2025) on non-brand search traffic in a single market. Compared with a Target CPA control, the pLTV-based treatment increases observed day-7 LTV per acquired user by +28.6%, strengthening to approximately +39% by two months, while ROAS is kept approximately constant by design. These results indicate a substantial uplift in acquisition quality at unchanged efficiency. The study is subject to several limitations, including its single-market scope, a measurement window shorter than the 120-day prediction horizon, attribution latency, and potential model drift. Despite these constraints, the system is fully operational and reproducible, supporting phased rollout to additional markets and campaign types. Overall, the findings provide strong evidence that pLTV-driven bidding delivers meaningful business value and enables a practical shift from volume-based to value-based optimisation in performance marketing.