How dayparting works
Ad scheduling lets you define which hours and days a campaign is eligible to serve, and historically let you apply bid adjustments so you could bid up during high-converting hours and down during weak ones. On a manual-bidding account this was essential, because a flat bid across the week ignored real patterns in when people convert. You would study the hour-of-day and day-of-week conversion data and shape delivery to match, concentrating budget where it worked hardest.
Dayparting under Smart Bidding
Smart Bidding already factors time of day and day of week into its predictions, so it adjusts bids for time patterns on its own using far more signal than a manual schedule could. That makes old-style bid-adjustment dayparting largely redundant on automated strategies, and stacking manual time bids on top can even interfere with the model. The nuance is that ad scheduling still controls when a campaign is eligible to serve at all, which is a different lever from telling the algorithm how to bid within those hours.
Where dayparting still matters
Dayparting remains genuinely useful in specific cases. Lead-gen businesses whose sales team can only respond during office hours may not want to pay for clicks that generate leads no one can call for two days, so restricting delivery to actionable hours protects lead quality. Businesses with hard budget constraints may want to concentrate a limited daily budget in proven windows. And service businesses with real operating hours, like a clinic or a firm taking calls, often schedule around when they can actually convert the interest.
How iClick uses dayparting
iClick lets Smart Bidding handle time-of-day bid adjustments and uses ad scheduling deliberately for serving eligibility, not to second-guess the algorithm. The rule is to restrict delivery only where there is an operational reason, such as a lead-gen team that can act on leads only during office hours or a fixed budget that should concentrate in proven windows, and to avoid layering manual time bids on automated strategies where they fight the model.
