Bidding Strategy Audit: Is Each Campaign Using the Right One?

Bidding Strategy Audit: Is Each Campaign Using the Right One?

There are two separate questions in Google Ads bidding, and they get muddled constantly.

The first is what target to set: what CPA or ROAS to aim for. The second, and more fundamental, is which bidding strategy to use at all. You can get the target exactly right and still be on the wrong strategy for the campaign, in which case the target is a well-tuned setting on the wrong engine.

This audit is about the second question. Not "is the target right?" but "is this campaign even using the strategy that suits its goal, its data, and its stage?" It is a question many accounts never revisit after launch, and the wrong answer quietly caps performance in a way that no amount of target-tweaking will fix.

Here is how to audit bidding strategy properly, campaign by campaign.

The Strategies, Briefly

Google's automated bidding strategies each optimise toward a different objective, and choosing between them is really choosing what you are asking the system to do.

Maximise Conversions chases the most conversions your budget can buy. It cares about volume of conversions, not their value or cost, unless you add a target.

Maximise Conversions with a Target CPA does the same but steers toward a cost-per-acquisition you set. Good when every conversion is worth roughly the same and you care about cost per acquisition.

Maximise Conversion Value chases the most total value, useful when conversions are worth different amounts, as in ecommerce.

Maximise Conversion Value with a Target ROAS does the same while steering toward a return you set. The workhorse for value-driven, margin-aware campaigns.

Manual and Enhanced CPC hand bidding control back to you, largely superseded by automation for most accounts but occasionally still relevant.

(Note: Google relabelled the target-based strategies in 2026, so "Maximise conversions with a Target CPA" now appears simply as "Target CPA" and the value equivalent as "Target ROAS." The behaviour is unchanged; only the names moved.)

The audit is about matching the strategy to the campaign, and there are three questions that decide the match.

Question One: What Is the Campaign's Actual Goal?

The strategy should follow the objective, and the objective is not always what the settings imply.

If you care about the number of conversions and they are worth roughly the same, a conversions-based strategy fits. A lead generation campaign where every lead has similar value, for instance.

If conversions are worth different amounts, you need a value-based strategy, because a conversions-based one will treat a £2,000 order and a £20 order as equally desirable. This is the most common mismatch in ecommerce: a value-driven business running a strategy that optimises for conversion count, quietly favouring cheap, frequent purchases over valuable ones.

If you have a specific efficiency requirement, a target-based strategy lets you express it, provided the campaign has the data to support it (see the next question).

If the goal is pure volume within a budget and efficiency is genuinely secondary, an untargeted maximise strategy can be right, though it removes the efficiency guardrail entirely.

Audit for: whether each campaign's strategy matches what the business actually wants from it, or whether it is optimising for the wrong kind of success, most often chasing conversion count when it should be chasing value.

Question Two: Does the Campaign Have Enough Data?

This is the constraint people most often ignore, and it breaks more bidding than any target ever does.

Automated bidding, especially target-based strategies, needs conversion data to learn. Below a certain volume, the system does not have enough signal to bid well, and a sophisticated strategy on a starved campaign performs erratically or never exits its learning phase.

Audit for:

Whether each campaign generates enough conversions for its strategy to function Whether target-based strategies are sitting on campaigns too thin to support them Whether campaigns are stuck perpetually in learning because the data is insufficient Whether over-segmentation has split conversion data so thin that no campaign has enough

The fix for a data-starved campaign is often not a different target but a different structure: consolidating campaigns so the combined conversion volume gives the automation something to work with, or stepping down to a simpler strategy that needs less data until volume builds.

A powerful strategy on a starved campaign is not powerful. It is guessing.

Question Three: What Stage Is the Campaign At?

Strategy should also fit where a campaign is in its life, not just its eventual goal.

A brand-new campaign with no conversion history cannot support a demanding target-based strategy from day one; there is nothing for it to learn from. It often makes sense to start simpler, gather conversion data, and graduate to a target-based strategy once there is enough history.

A stable, mature campaign with plenty of history can support a precise target-based strategy comfortably.

A campaign that just changed significantly, through a restructure, a tracking change or a major budget shift, has effectively been reset and needs time before its strategy can be judged.

Audit for: whether strategies match campaign maturity, whether new campaigns have been handed demanding strategies too early, and whether recently changed campaigns are being judged before they have settled.

Portfolio vs Individual Strategies

A structural question sits alongside the per-campaign one: whether campaigns should share a bid strategy or run their own.

Portfolio bid strategies apply one target across several campaigns. They can help pool conversion data, useful when individual campaigns are thin, and they enforce a consistent target across a group.

But they cause problems when misapplied.

Audit for:

Whether portfolios group campaigns that genuinely share a goal and similar economics Whether campaigns with very different margins are forced under one target Whether brand and non-brand have been lumped into one portfolio Whether a portfolio exists for a real reason or just to hit a data threshold Whether one campaign's performance inside a portfolio is masking another's

Portfolios are a tool for pooling data and enforcing consistency, not a default. The audit question is whether each one groups campaigns that truly belong together.

The Interaction With Targets

Strategy and target are separate decisions, but they interact, and an audit should note where the interaction is causing trouble.

A demanding target on a strategy the campaign cannot support will fail regardless of how well the target is set. A campaign frequently limited by budget on a target-based strategy behaves differently from an unconstrained one. And the 2026 change to how budget-limited campaigns on target strategies are steered means the strategy-and-target combination deserves fresh review on any campaign that is budget-constrained.

Audit for: campaigns where the strategy and target are fighting each other, where the strategy is too demanding for the data, or where a budget constraint is undermining the whole setup.

Common Bidding Strategy Mistakes

Chasing conversion count when value matters An ecommerce campaign on a conversions strategy treats a huge order and a tiny one as equal. Value-driven campaigns need value-based strategies.

Demanding strategies on starved campaigns Target-based bidding needs data. On a thin campaign it guesses, and stays stuck in learning.

Launching new campaigns on demanding strategies With no history to learn from, a target-based strategy has nothing to work with. Start simpler and graduate.

Forcing mismatched campaigns into one portfolio Different margins, or brand and non-brand, under a single target removes the control that mattered.

Never revisiting the strategy after launch Goals, data volume and maturity all change. The strategy chosen at launch is often wrong a year later.

Confusing a strategy problem with a target problem Endlessly tuning the target when the strategy itself is wrong for the campaign.

How AI Can Help

Matching strategy to campaign means weighing goal, data volume and maturity across every campaign at once, which is exactly the kind of systematic comparison that is slow by hand.

AI can process:

Whether each campaign's strategy matches its actual objective Which campaigns lack the conversion volume their strategy needs Which are stuck in perpetual learning Whether portfolios group campaigns that genuinely belong together Ranked strategy fixes by impact and confidence

GoogleAdsAudits.com reviews account structure, tracking, bidding, creative, feed and channel mix, then produces a prioritised action plan positioning each recommendation by impact, effort and confidence.

The decision about what each campaign is really for still comes from the business.

Final Thoughts

Setting the right target is only half of bidding. Choosing the right strategy for the campaign is the other half, and it is the one accounts most often get wrong and never revisit.

Audit it against three questions. Does the strategy match the campaign's real goal, chasing value where value matters and count where it does not? Does the campaign have enough conversion data to support the strategy, or is a powerful strategy just guessing on thin signal? And does the strategy fit the campaign's stage, rather than demanding history a new campaign does not have?

Get those three right and the target has a sound engine to sit on. Get them wrong and the best-tuned target in the world is optimising the wrong thing. Strategy first, then target. Not the other way around.

Build a personalised Google Ads action plan that identifies wasted spend, scalable opportunities and the fixes your team should prioritise next.

Frequently Asked Questions

How do I choose the right Google Ads bidding strategy?

Match it to three things: the campaign's real goal (value versus conversion count), whether it has enough conversion data for the strategy to learn, and its stage (new campaigns need simpler strategies first). The strategy should follow the objective, not the other way around.

What's the difference between Maximise Conversions and Maximise Conversion Value?

Maximise Conversions chases the most conversions regardless of their worth; Maximise Conversion Value chases the most total value, treating a large order as more desirable than a small one. Value-driven businesses, especially ecommerce, usually need the value-based version.

When should I use Target CPA versus Target ROAS?

Target CPA suits campaigns where every conversion is worth roughly the same and you care about cost per acquisition. Target ROAS suits campaigns where conversions vary in value and you care about return. Both need enough conversion data to work.

Why is my campaign stuck in the learning phase?

Usually because it does not have enough conversion volume for the strategy you have chosen, often the result of over-segmentation splitting data too thin. Consolidating campaigns or stepping down to a simpler strategy until volume builds typically helps.

Should new campaigns use Smart Bidding straight away?

Target-based strategies need conversion history to learn from, which a brand-new campaign lacks. It often works better to start with a simpler strategy, gather data, then graduate to a target-based one once there is enough history.

What is a portfolio bid strategy and when should I use it?

It applies one target across several campaigns, which can pool thin conversion data and enforce a consistent target. Use it only when the campaigns genuinely share a goal and similar economics; forcing different margins, or brand and non-brand, under one target causes problems.

Is manual bidding ever the right choice now?

Rarely for most accounts, as automated bidding generally outperforms it once there is data. It can occasionally suit very low-volume or highly specific situations where automation lacks signal, but for most campaigns the question is which automated strategy, not whether to automate.

AUTHOR BIO

Jack Felstead is an award-winning Paid Media, AI and Digital Growth Consultant with 17 years of experience helping businesses improve customer acquisition, advertising efficiency, revenue and profit.