This caught me somewhere between a pricing problem and an ML problem and I wasn't sure which direction they wanted.
Start by framing the bidding problem as a constrained optimization: maximize profit (or ROAS) subject to budget and other constraints. Then walk through a data-driven approach that combines predictive modeling (e.g., click and conversion probabilities) with economic principles (e.g., expected value, marginal analysis) to determine the optimal bid. Finally, discuss how you would implement and iterate using experimentation and feedback loops.
Pro tip: Emphasize that the optimal bid is not static; it depends on the auction context, user intent, and competitive landscape. Show you understand the trade-off between exploration (learning) and exploitation (optimizing) in a live bidding environment.
Clarify the business goal (e.g., maximize profit, revenue, or ROAS) and constraints (budget, CPA targets). This sets the optimization target.
Use historical data to build ML models that predict the probability of a click given an impression and the probability of a conversion given a click, incorporating features like query, user, ad creative, and context.
Compute the expected value of a click as p(click) * p(conversion) * value_per_conversion. The optimal bid is the maximum amount you're willing to pay for a click, often derived from this expected value adjusted by a factor (e.g., target ROAS).
Account for the fact that winning depends on other bids. Use techniques like bid shading or reinforcement learning to adapt bids in real-time based on auction feedback.
Deploy the bidding strategy via an API, run A/B tests or multi-armed bandit experiments to compare against baselines, and continuously retrain models and adjust bids based on performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.