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MICROSOFT ADS ENCYCLOPAEDIA

Bid adjustments under Smart Bidding

When automated bidding is active, manual bid adjustments change their behaviour. Here is how device, location, and LinkedIn modifiers interact with Smart Bidding.

Quick answerUnder Microsoft Smart Bidding (Target CPA, Target ROAS, Maximise Conversions), manual bid adjustments for devices, demographics, and locations are treated as audience signals rather than hard multipliers. The only exception is a -100% adjustment, which completely excludes traffic.
Section 01

The fundamental shift: multipliers versus signals#

Under Manual CPC, a +50% device bid adjustment increases a £2.00 bid to £3.00. Under Smart Bidding (Target CPA and Target ROAS), manual adjustments no longer act as strict mathematical multipliers on auction bids.

Instead, as outlined in Microsoft Smart Bidding Behavior Docs, 2026, the algorithm incorporates device, location, and time signals into its real-time machine learning models.

Section 02

Device bid adjustments under Target CPA#

In Target CPA campaigns, setting a device bid adjustment modifies your target CPA rather than the underlying bid. For example, a +20% mobile adjustment informs the algorithm that you are willing to accept a 20% higher cost per acquisition on mobile leads.

Setting a -100% adjustment remains the sole deterministic override, completely preventing ad impressions on that device category as confirmed in Microsoft Campaign Management Service, 2026.

Hard exclusion

A -100% bid adjustment is the only manual modifier that Smart Bidding cannot override.

Section 03

LinkedIn profile adjustments under Smart Bidding#

LinkedIn Profile Targeting bid adjustments (Company, Industry, Job Function) serve as high-priority audience signals for automated bidding models, steering budget towards enterprise decision-makers.

Section 04

Location and ad schedule interactions#

Smart Bidding natively evaluates geographic conversion probability and time-of-day trends at the auction level, rendering manual hourly bid adjustments largely redundant unless operational sales hours require strict gating.

Consult our Microsoft Ads Hub for detailed audience signal frameworks.

Section 05

Bid adjustment behaviour comparison table#

The table below summarizes how each manual bid modifier functions across Manual and Smart Bidding strategies.

Behavior of bid modifiers under Manual vs Smart Bidding
Adjustment TypeManual CPC / ECPCTarget CPA / Target ROAS
Device (+/- %)Direct mathematical bid multiplierModifies the CPA/ROAS target
Device (-100%)100% traffic exclusion100% traffic exclusion
Location (+/- %)Direct mathematical bid multiplierEvaluated as an algorithmic signal
Ad Schedule (+/- %)Direct mathematical bid multiplierEvaluated as an algorithmic signal
LinkedIn Target (+/- %)Direct mathematical bid multiplierHigh-priority audience steering signal
Questions

Frequently asked questions#

Do device bid adjustments work with Target CPA on Microsoft Ads?

Yes, but instead of altering the raw keyword bid, device adjustments under Target CPA modify your CPA target for that specific device.

Can I completely block mobile traffic when using Smart Bidding?

Yes, applying a -100% device bid adjustment on mobile completely prevents ads from serving on mobile devices regardless of bid strategy.

How does LinkedIn profile targeting work with Target ROAS?

LinkedIn modifiers act as audience signals, teaching the algorithm to bid more aggressively on queries originating from high-value industries or companies.

Should I remove manual ad schedule adjustments after switching to Smart Bidding?

Yes, unless you have strict operational hours when calls cannot be answered, removing manual schedule adjustments gives the algorithm full freedom to optimize.

Does Smart Bidding override negative location exclusions?

No, negative location exclusions are hard boundaries that Smart Bidding always respects.

Keep reading

Read more on this topic#

Confused about how your bid adjustments interact with AI?

folkfox audits automated bidding signals to ensure manual modifiers complement algorithmic learning.