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Summary:<\/strong> Manual dayparting lets you pause and resume<\/em> delivery by hour and weekday at the product or campaign level so you stop paying for low-quality traffic and concentrate budget<\/a> when shoppers actually convert. It\u2019s powerful, a bit advanced, and absolutely worth it once you have enough hourly data from Amazon Marketing<\/a> Stream.<\/p> Manual dayparting<\/strong> is the control to explicitly switch ads off<\/em> during weak hours and switch them on<\/em> during strong hours. In AiHello, you can apply this schedule to all campaigns for a product<\/strong> (recommended) or to specific campaigns<\/strong> if you need surgical control.<\/p> Think of it as the budget\u2019s bodyguard: it keeps your spend away from hours that chronically underperform and pushes that money into windows where conversion rate (CVR) and order density are consistently better.<\/p> Dayparting is only as good as the hourly dataset behind it. AiHello powers dayparting with Amazon Marketing<\/a> Stream<\/strong>; Amazon\u2019s intraday feed that reports performance by hour and other signals. Two critical realities:<\/p> Only after those first couple of weeks should you enable manual dayparting. Until then, let campaigns gather reliable hourly signal.<\/p> You can schedule at two levels:<\/p> Rule of thumb:<\/em> Start by product<\/strong> for simplicity and consistency. Graduate to by campaign<\/strong> once you\u2019ve proven there\u2019s a reason to stagger different campaign types across the day.<\/p> Once Marketing Stream data accumulates, AiHello shows how your product performs by hour of day<\/strong> and day of week<\/strong>. You\u2019ll typically review:<\/p> Example patterns we often see: pre-dawn hours sometimes show strong CVR in certain categories (fewer window-shoppers), late morning may dip, and weekends especially Sunday can surge as buyers finish carts. Your dataset is the truth; trust the hourly and weekday deltas over gut feel.<\/p> Use dayparting to cut persistently<\/em> unprofitable windows. A simple decision framework:<\/p> When those conditions are met, it\u2019s reasonable to switch off<\/strong> those hours. For example, if Friday 2-3 a.m. drains budget with low CVR and no delayed orders to make up for it, switch off the product for that window and resume at 4 a.m.<\/p> When not to cut:<\/strong><\/p> AiHello offers both:<\/p> Blended approach:<\/em> Start with automated to shape spend, then add manual off-blocks where the data shows chronic underperformance.<\/p> Tip:<\/em> If you operate across multiple marketplaces<\/a>, align schedules with buyer behavior per region. Don\u2019t copy-paste a US schedule into the UK without checking the local chart.<\/p> Once set at the product level, your manual dayparting schedule runs indefinitely<\/strong>. AiHello switches campaigns off\/on at those times every day. You can:<\/p> Connect Amazon Ads, then let data accumulate. Resist the urge to cut after 72 hours; hourly patterns can be noisy in the short term.<\/p> CVR is critical, but combine it with CPC and volume. A simple score is Orders per $100 spend<\/strong> by hour (or the inverse, Cost per Order<\/em>). If an hour consistently produces half the orders at the same or higher CPC, it\u2019s a candidate to turn off.<\/p> Some hours are great on weekends but weak on weekdays (and vice versa). AiHello\u2019s weekday chart helps you avoid \u201caverages that lie.\u201d Build off-blocks that reflect both hour<\/em> and weekday<\/em> patterns.<\/p> Prefer multi-hour off-blocks (e.g., 1-4 a.m.) over choppy on\/off toggles every hour. Clean blocks reduce auction instability and are easier to maintain.<\/p> Cut the bottom 10-20% hours first. Review for two weeks. If performance lifts and stability holds, expand the off-blocks. If delivery feels brittle, roll back.<\/p> Major price updates, inventory<\/a> shifts, review swings, or new competitors can reshape your hourly curve. Re-check schedules after such events.<\/p>Index<\/h2>
What is manual dayparting (and how it works in AiHello)<\/h2>
Why you must wait for data first (Marketing Stream 101)<\/h2>
Dayparting by product vs. by campaign<\/h2>
Reading the charts: hours, weekdays, and deltas<\/h2>
When to shut off hours (and when not to)<\/h2>
Manual vs. automated dayparting (what\u2019s the difference?)<\/h2>
How to enable manual dayparting in AiHello<\/h2>
Lifetime schedules, overrides, and maintenance<\/h2>
A practical playbook: from data to schedule<\/h2>
1) Collect enough signal<\/h3>
2) Rank hours by efficiency, not just CVR<\/h3>
3) Validate across weekdays<\/h3>
4) Set clean blocks<\/h3>
5) Start conservative<\/h3>
6) Review after big changes<\/h3>