A dayparting heatmap is hourly Amazon ad performance laid out so you can see patterns at a glance. It shows spend, impressions, clicks, orders, and ACoS for each of the 24 hours, usually across a 7x24 grid of the week. The point is not just to find the worst hours and pause them. The point is to understand why your account performs the way it does and to build a schedule that reflects that reality.
This guide covers how to pull the underlying data, what to look for in each zone of the heatmap, and how to make decisions that hold up once they are running automatically.
Where the data comes from
Amazon Ads does not surface hourly performance in the standard campaign dashboard. The data is available, but you have to dig for it. In Seller Central, go to Reports, then Advertising Reports, and create a new report with the time unit set to "Hour" instead of the default "Daily." You will get a CSV with one row per campaign per hour, which you can then aggregate and visualize in a spreadsheet.
This is time-consuming to do by hand across a full account, especially if you have more than a handful of campaigns. Off Hours pulls this hourly data automatically at connection and displays it as a heatmap on the dayparting setup screen, so you can see the patterns before you build the schedule. Either way, the interpretation process is the same.
Before you read anything into the heatmap, you need enough data to trust it. Four weeks is the minimum. Two weeks can look like a pattern but it is usually noise. If your account recently ran a sale, a coupon campaign, or a Prime Day push, that period will skew the heatmap. Pull a "clean" four-week window that represents normal buying behavior for your product.
The four zones to look for
Every heatmap, regardless of product category, tends to have four types of hours when you look across enough data.
Peak hours. These are the hours with above-average impressions, above-average conversion rate, and ACoS at or below your target. These hours are earning their spend. Do not touch them. If anything, these are the hours where a budget rule that temporarily increases your daily cap during the window would make sense.
Dead hours. These are the hours with meaningful spend but near-zero orders across multiple weeks. They usually concentrate in the overnight window for most product categories, though the exact shape depends on your buyer. Dead hours are the primary target for a dayparting pause schedule. The real cost of running ads overnight is not just the spend itself but the budget those hours consume before your peak window opens.
Ghost hours. These are hours with near-zero impressions. This is not a dayparting opportunity because your campaigns are not spending much here anyway. They typically appear in the very early morning (2am to 4am) once competition drops out and search volume falls. Pausing ghost hours does not meaningfully change your performance, and it adds complexity to your schedule for no gain.
Variable hours. These are hours that look inconsistent week to week: one week they convert well, the next they are empty. These hours need more data before you act on them. Running an additional four weeks of data usually resolves the ambiguity. In the meantime, leave them on and let them accumulate signal.
Four common heatmap shapes
Once you have categorized your hours, the overall shape of your heatmap will usually fall into one of a few recognizable patterns. Knowing which pattern you are looking at helps you set expectations for what a dayparting schedule can recover.
Morning peak. Spend and orders concentrate between 7am and noon, then drop sharply in the afternoon and evening. This is common for office supplies, productivity tools, and anything business buyers purchase during work hours. The overnight pause is obvious, but the underperforming afternoon hours are often worth pausing too.
Evening peak. The account is quiet during the day and comes alive after 5pm. Common for home goods, fitness, and lifestyle categories where buyers are browsing on personal devices after work. The overnight hours from midnight to 5am are still typically dead, but the dead zone is narrower than it looks at first.
Weekend spike. Weekday performance is flat and weekend performance is significantly stronger. This pattern shows up most clearly in the 7x24 view. If your weekday hours are just barely profitable, a schedule that reduces bids or pauses completely on slow weekday windows can redirect budget toward Saturday and Sunday without affecting total weekly reach.
Uniform. No clear pattern. Performance is scattered across hours with no consistent peak or trough. This usually means one of three things: the account does not have enough data yet to show patterns, the product has genuinely even demand across all hours (rare), or the account is running across too many unrelated products to show category-specific behavior. Uniform heatmaps benefit from more data before any schedule changes.
What the ACoS column tells you that spend alone does not
The most common mistake in reading a heatmap is sorting by spend and pausing the expensive hours. Spend is only half the signal. An hour can have high spend and excellent ACoS, which means it is generating profitable orders. Pausing it would hurt the account. The hour you actually want to pause is the one with high spend and zero orders, or ACoS that blows past your target consistently over multiple weeks.
The right read is always spend-plus-ACoS together. Look for the intersection of non-trivial spend and poor returns. That is where the recoverable budget lives.
Northlane Goods, a home storage brand, had an apparent dead zone in their heatmap from 11pm to 6am. Spend in that window averaged $18 per day. ACoS was running at 94%. Over a 30-day window, the overnight pause recovered roughly $540 in spend and redirected it toward their 8am to 2pm peak, which had been running below budget because daily caps were being consumed overnight before buyers in their core time zone woke up. The complete framework for thinking through ad scheduling by hour covers the full decision process.
How to turn the heatmap into a dayparting schedule
Once you have identified your dead hours with confidence, the schedule itself is straightforward. Pause the dead hours. Leave everything else on. The complete dayparting guide covers the full setup process including how to handle edge cases like DST adjustments and accounts that span multiple time zones.
A few things to decide before you commit:
What time zone to use. Amazon campaigns run in the time zone of the associated seller account. Most accounts should use the time zone that matches their primary buyer geography, not their own location. A seller in Denver selling to a predominantly East Coast audience should schedule based on Eastern time.
Buffer hours. The transition from dead to active is not always clean. If your data shows the dead zone ending at 6am, consider starting the schedule at 5:30am rather than exactly 6am to catch early buyers. The reverse applies at the end of the day: if your data shows drop-off around 11pm, starting the pause at 10:30pm gives you a small buffer without leaving meaningful spend on the table.
Day-of-week differences. Your dead hours on Sunday may look different from dead hours on Tuesday. A 7x24 heatmap lets you set different schedules by day. Day-of-week performance is often as important as hour-of-day for accounts with strong weekend or weekday concentration. When patterns are meaningfully different by day, a single universal schedule is a compromise. Build separate weekday and weekend configurations where the data supports it.
Review cadence. A dayparting schedule is not set-and-forget permanently. Seasonal products will show different hourly patterns in November than in February. Review the heatmap quarterly against your live schedule and update if the underlying patterns have shifted. The PPC account audit checklist includes a section on reviewing dayparting schedules alongside other structural checks.
Frequently asked questions
What is a dayparting heatmap for Amazon Ads? A dayparting heatmap is a visualization of your Amazon ad performance broken down by hour of day. It shows metrics like spend, impressions, clicks, orders, and ACoS for each of the 24 hours so you can see which hours are generating returns and which are burning budget with nothing to show for it.
How do I get hourly data from Amazon Ads? Amazon Ads does not display hourly performance directly in the standard campaign dashboard. You can access hourly data through the Advertising Reports section in Seller Central by setting the report time unit to "Hour" when generating a campaign report. Off Hours pulls this hourly data automatically and displays it as a heatmap when you connect your account.
Which hours should I pause my Amazon ads? There is no universal answer, but the most common pause candidates are late-night hours (roughly midnight to 5am) for most product categories, since buyer intent is low and the clicks that do come in rarely convert. However, your own hourly data is more reliable than any benchmark. Run at least 30 days of data before drawing conclusions, and look for hours with consistently high spend and near-zero orders across multiple weeks.
How many weeks of data do I need before setting a dayparting schedule? Four weeks minimum. Two weeks is not enough to separate a bad week from a structural pattern. Seasonal accounts may need 6 to 8 weeks that represent typical demand (not a promotional period or holiday). The goal is to see the same dead hours and peak hours repeat across multiple independent weeks before you commit to pausing anything.
Off Hours pulls your hourly data at connection and displays it as a heatmap so you can see the patterns before you build a schedule. Once you set the schedule, it runs on autopilot. Start a free 14-day trial to see your account's heatmap.