Bidding strategy is the setting that determines whether the platform optimizes for the right thing. Most healthcare accounts have it set to whatever the launch wizard suggested, which is rarely what fits.
Among the configuration decisions in a Google Ads account, bidding strategy is one of the most consequential and one of the most commonly set on autopilot. The default options have changed several times over the past five years. The recommendations from Google’s account representatives shift each quarter. The naming conventions are confusing, and the underlying mechanics of each strategy are rarely explained in ways a practice owner can confidently evaluate.
The result is that most medical practice accounts are running a bidding strategy chosen at campaign launch by someone who picked the option Google was promoting at the time, and never revisited the decision. For some accounts, the choice was correct. For many, it was not, and the campaign performance has been quietly suboptimized for years as a result.
What Bidding Strategy Actually Does
Bidding strategy is the rule the platform follows when deciding how much to bid for each individual auction. Every time a Google search produces an ad-eligible query, the platform runs an auction. The bidding strategy is the logic that determines what the account is willing to pay for that auction.
The choice of strategy determines what the algorithm is optimizing for, what data it uses to make decisions, and what tradeoffs it accepts. Different strategies are designed for different goals, and using the wrong one produces results that look fine on the surface but miss the campaign’s actual purpose.
Maximize Conversions
Maximize Conversions is exactly what it sounds like. The algorithm bids to produce the highest number of conversions within the campaign’s budget. It does not consider what each conversion costs, only that conversions occur.
This strategy fits campaigns where volume is the priority and conversion economics are roughly consistent across leads. A primary care practice running a campaign to fill open new-patient slots, where each new patient has similar long-term value, might appropriately use Maximize Conversions.
The risk of Maximize Conversions is that it can pursue cheap conversions of low value if budget is not constrained. In healthcare, where lead quality varies dramatically — a wrong-specialty inquiry, a not-yet-qualified prospect, a job applicant — Maximize Conversions can produce high volume of low-value leads if the conversion tracking does not differentiate. The strategy assumes all conversions are equally valuable, which is rarely true in healthcare.
Maximize Conversion Value
Maximize Conversion Value optimizes for the total value of conversions rather than their count. The algorithm bids more aggressively for conversions assigned higher values and less aggressively for lower-value conversions.
This strategy is appropriate when conversion values vary meaningfully and the practice can track those values accurately. A multi-service practice where a knee replacement consultation is worth substantially more than a general orthopedic inquiry should optimize for value, not count.
The strategy depends entirely on accurate conversion value data flowing into the platform. If the practice’s CRM does not track conversion outcomes and report them back to the account, Maximize Conversion Value falls back to a uniform value assumption, which collapses it to essentially the same behavior as Maximize Conversions. For most healthcare practices, getting the value data right is the prerequisite for this strategy to outperform the simpler count-based approach.
Target CPA
Target CPA, or tCPA, optimizes for conversions while attempting to keep the average cost per acquired conversion at or below a specified target. The advertiser sets the target cost, and the algorithm adjusts bids to stay near it.
This strategy fits campaigns where the conversion economics are well-understood and the practice has a clear ceiling for acceptable cost per lead. A med spa with an internal benchmark of paying no more than a specific cost per qualified inquiry can express that constraint through tCPA.
tCPA requires sufficient conversion data for the algorithm to optimize meaningfully. The general guidance is that campaigns need a meaningful number of conversions in the relevant lookback window before tCPA produces reliable results. Campaigns running tCPA with very thin conversion volume often see erratic performance, because the algorithm does not have enough data to stabilize.
Setting the target too low strangles the campaign. The algorithm responds to a tight target by reducing bids, which reduces impressions, which reduces conversions, which signals the algorithm to reduce bids further. Campaigns can spiral down quickly if the tCPA is set below what the market actually supports.
Setting the target appropriately requires honest assessment of current conversion costs. The tCPA should typically be set around the current cost per conversion or modestly below, then tightened gradually as the campaign learns and performance stabilizes.
Target ROAS
Target ROAS — return on ad spend — is the value-based equivalent of tCPA. The advertiser sets a target ratio of conversion value to ad spend, and the algorithm bids to maintain that ratio.
This strategy fits campaigns with meaningful conversion value variability and accurate value tracking. It requires the same data infrastructure as Maximize Conversion Value, plus a defensible internal target for what ratio the practice wants to maintain.
Target ROAS is one of the more sophisticated strategies and depends on substantial data and clear value definitions. For practices early in their paid-search maturity, simpler strategies often produce more reliable results until the conversion-value infrastructure is fully built.
Manual CPC and Enhanced CPC
Manual CPC bidding — where the advertiser sets specific bids at the keyword level — has become less common as automated strategies have matured, but it still has a role in specific contexts.
Campaigns with very thin conversion data, where automated strategies cannot optimize meaningfully, sometimes perform better under manual control. New campaigns building initial conversion history can also benefit from manual bidding in the launch period before transitioning to an automated strategy once enough data has accumulated.
Enhanced CPC — manual bidding with algorithmic adjustments — sits between manual and fully automated approaches. It allows the algorithm to adjust bids modestly based on conversion likelihood while preserving the advertiser’s control. For some healthcare campaigns, particularly those with sensitive keyword inventories where the advertiser wants visibility into bid behavior, Enhanced CPC remains useful.
How to Choose for Healthcare Specifically
A practical framework for choosing among these strategies for medical practices follows from a few questions.
How much conversion data does the campaign have? Thin data points toward manual or enhanced CPC during the learning phase. Adequate data opens up tCPA. Substantial data with meaningful value variability opens up tROAS.
How much do conversion values vary? Roughly uniform values fit Maximize Conversions or tCPA. Substantially variable values fit Maximize Conversion Value or tROAS.
How constrained is the budget relative to demand? Budget-constrained campaigns with clear cost ceilings benefit from tCPA. Volume-constrained campaigns where the practice wants as much volume as possible within budget fit Maximize Conversions.
How accurate is conversion tracking? Strategies that depend on conversion value require accurate value data. If the practice’s CRM and conversion infrastructure cannot report values reliably, value-based strategies will not work even when they would otherwise be appropriate.
What to Do When Strategy Changes
Bidding strategy changes are not free. Each change triggers a learning period, typically one to two weeks, during which the algorithm rebuilds its understanding of the campaign. Performance during this period is often erratic, and accounts that change strategies frequently can find themselves perpetually in learning periods, never producing stable performance.
The right rhythm is to commit to a chosen strategy for long enough to produce meaningful data — typically four to six weeks at minimum — before evaluating whether to change it. Changing strategies because of two weeks of unsatisfactory results usually means the algorithm never had time to learn.
When a change is needed, scheduling it during a period of stable budget and seasonal demand reduces the noise in the learning period. Changes made during seasonal spikes or budget shifts produce learning periods contaminated by external factors and harder to evaluate.
The Single Most Common Mistake
Across medical practice accounts, the single most common bidding-strategy mistake is leaving the strategy unchanged from the campaign launch decision regardless of how the campaign has matured. A campaign that launched on Manual CPC because it had no data still on Manual CPC three years later with thousands of conversions has likely outgrown the strategy. A campaign launched on Maximize Conversions still on that strategy after the practice developed clear cost-per-lead targets has likely outgrown it too.
Bidding strategy is a setting that should be revisited as the account matures. Reviewing it annually, in the context of current conversion data and current practice priorities, is one of the lowest-effort, highest-value optimization disciplines available.
