Bidding strategy is one of the most consequential decisions in any SEM campaign, directly affecting both cost efficiency and overall performance. Advertisers generally choose between manual and automated bidding approaches, each with distinct advantages.

Manual bidding gives advertisers direct control over the maximum amount they’re willing to pay for a click on each keyword. This level of control is useful for advertisers who deeply understand their audience and want precise adjustments based on factors like time of day, device, or location. However, manual bidding requires significant ongoing management and can struggle to react quickly to real-time auction dynamics.

Automated bidding strategies use machine learning to adjust bids in real time based on the likelihood of achieving a specific goal, such as maximizing conversions or hitting a target return on ad spend. These strategies analyze signals like device, browser, location, and time far more granularly than a human manager reasonably could.

The trade-off with automated bidding is a temporary loss of direct control and a required learning period, typically one to two weeks, during which the algorithm gathers enough conversion data to optimize effectively. Campaigns with low conversion volume may struggle to give automated bidding enough data to perform well.

A common approach for newer campaigns is to start with manual or simpler automated strategies like maximize clicks, then transition to conversion-focused automated bidding once sufficient conversion data has accumulated, typically after a few dozen conversions.

Regardless of approach, bidding strategy should be reviewed periodically alongside broader account performance, since business goals, budgets, and market competition all shift over time.