1) Set the ground rules: what a “weekly trend” actually means (and how to avoid bad reads)
Use complete, comparable data before you judge a week
Weekly trend analysis only works if you’re comparing like-for-like. In Google Ads, “recent” numbers can be incomplete for two common reasons: reporting latency (some metrics aren’t fully processed right away) and conversion delay (people often convert days after the click). If you compare a fresh week that’s still “filling in” against an older week that’s fully matured, you’ll often conclude performance dropped when it’s really just delayed reporting.
As a baseline, assume most top-line stats update within hours, but some reporting and certain attribution setups can take longer, and some reports are processed only once per day. Build your weekly review rhythm around that reality (for example, reviewing the prior Monday–Sunday week on Tuesday or Wednesday rather than first thing Monday). ([support.google.com](https://support.google.com/google-ads/answer/2544985?hl=en&utm_source=openai))
For conversion-led accounts, the cleaner approach is to pair weekly trend review with a quick “conversion delay” check (more on that below). It’s the difference between reacting to noise and managing a predictable lag that happens every week. ([support.google.com](https://support.google.com/google-ads/faq/10286469?hl=en&utm_source=openai))
Pick the right week definition and stick to it
Decide what “a week” is for your business and keep it consistent across dashboards and reports. Most advertisers use Monday–Sunday. The important thing is consistency: if your week boundary shifts, you’ll blur day-of-week seasonality and make trends look “choppy” even when nothing changed.
Know which KPIs should lead your weekly narrative
Weekly trend conversations go off the rails when teams mix volume metrics (impressions, clicks, spend) with outcome metrics (conversions, conversion value, ROAS/CPA) without a hierarchy. In practice, you want one “north star” outcome metric, supported by two or three diagnostic metrics that explain movement.
A simple structure that works across most accounts is: outcomes first (conversions or conversion value and efficiency), then the volume and rate metrics that explain why (cost, click volume, conversion rate, average CPC). When the account uses automated bidding, keep your evaluation aligned to what the campaign is optimizing toward, because not every metric change is equally meaningful in that context. ([support.google.com](https://support.google.com/google-ads/answer/12294038?hl=en&utm_source=openai))
2) Build a weekly trend view that answers “what changed?” in minutes
Start with the fastest view: segment your table by Week
The quickest way to see weekly performance trends inside Google Ads is to take the table you already trust (Campaigns, Ad groups, Keywords, etc.) and segment it by time. Use the segment control in the table and choose a time-based segment like “Week” so each row becomes a week, and your columns stay your KPIs. ([support.google.com](https://support.google.com/google-ads/answer/2454072?hl=en-GB&ref_topic=3119142))
Two practical notes matter here. First, some segment/column combinations don’t work, especially conversion-related segments with non-conversion columns, and that can produce blanks that look like “missing data” when it’s really an incompatible view. Second, if you try to segment too granularly (like Day), Google Ads limits it to shorter date ranges; if you need longer periods, Week is usually the right tradeoff between detail and usability. ([support.google.com](https://support.google.com/google-ads/answer/2454072?hl=en-GB&ref_topic=3119142))
Use date-range comparison the right way (and don’t include “today”)
For weekly trend reads, most teams want week-over-week comparison. Google Ads supports this in a few ways, but the most actionable workflow is: set your date range to the last 7 days (or a specific Monday–Sunday week), then compare to the previous period of equal length. That keeps the comparison fair and naturally controls for day-of-week patterns.
If you rely on in-platform explanations to understand the “why,” be aware of two key constraints: explanations won’t show if your selected date range includes today (because today’s data can still change), and custom comparisons need equal-length, contiguous ranges to work reliably. ([support.google.com](https://support.google.com/google-ads/answer/9000655?hl=en&utm_source=openai))
Turn your weekly view into a repeatable report (so you’re not rebuilding it every Monday)
Once your table shows weekly rows and the KPI columns you want, save it as a report and schedule it to email on a cadence that fits your business (weekly is the obvious default). Scheduling from the statistics table preserves the filters you applied (for example, only Brand campaigns, only a region, only Search). ([support.google.com](https://support.google.com/google-ads/answer/2404176?hl=en&utm_source=openai))
If you manage multiple stakeholders, a dashboard is often better than a single report because you can combine a scorecard (with percent change and a sparkline) plus one or two charts that show weekly movement by campaign type or objective. Dashboards are also easier to annotate for context (“promo week,” “site outage,” “budget capped”) so the trend remains interpretable months later. ([support.google.com](https://support.google.com/google-ads/answer/6379084/create-and-edit-dashboards?hl=en-GB))
Critical weekly setup checklist (do this once, then reuse forever)
- Lock your KPI set (north star + supporting metrics) and keep it consistent week to week.
- Segment your primary performance table by Week to make the trend obvious. ([support.google.com](https://support.google.com/google-ads/answer/2454072?hl=en-GB&ref_topic=3119142))
- Use Previous period comparisons for true week-over-week reads, and avoid date ranges that include today if you want explanations to appear. ([support.google.com](https://support.google.com/google-ads/answer/9000655?hl=en-GB&utm_source=openai))
- Save the view and schedule it weekly so your team reviews the same lens every time. ([support.google.com](https://support.google.com/google-ads/answer/2404176?hl=en&utm_source=openai))
3) Diagnose weekly shifts systematically: from “we’re down” to the real driver
Step 1: Separate a volume problem from an efficiency problem
When a week moves, start by classifying the shift. If conversions dropped but spend also dropped, you may have a delivery issue (budgets, bids, eligibility, limited inventory). If spend held and conversions dropped, that’s more likely efficiency (conversion rate, traffic quality, landing page, tracking, or mix shift). If spend rose and efficiency fell, it might be an intentional trade (scaling) or a loss of targeting discipline.
Step 2: Validate the data (freshness + conversion lag) before you take action
If you’re looking at a week that ended yesterday, remember reporting isn’t always final. Some statistics are delayed, and certain attribution setups can delay conversion reporting longer than basic last-click reporting. ([support.google.com](https://support.google.com/google-ads/answer/2544985?hl=en&utm_source=openai))
Then check conversion delay directly. Segment conversion columns by “Days to conversion” to understand how long conversions typically take in your account. If you consistently see meaningful volume arriving 2–7 days after the click, your “last week” conversions will almost always look weak at first glance, especially compared to older weeks. ([support.google.com](https://support.google.com/google-ads/faq/10286469?hl=en&utm_source=openai))
Step 3: Use in-platform explanations to narrow the cause quickly
When Google Ads detects a significant fluctuation, explanations can highlight what changed between two comparable ranges. In practice, I use explanations as a triage tool: they’re not a replacement for analysis, but they often point you to the fastest path (for example, “conversion delay increased,” “budget constrained,” “auction dynamics shifted,” or “target changes”). Remember: they’re designed to focus on metrics tied to campaign goals and may not trigger for every metric or every small fluctuation. ([support.google.com](https://support.google.com/google-ads/answer/9000655?hl=en&utm_source=openai))
Step 4: Cross-check the “did we change something?” layer
Before you blame the market, confirm whether you (or an automation) changed something. Use change history to review edits by date: budgets, bid strategies, targets, keywords, audiences, ads/assets, schedules, etc. This is especially important in accounts with multiple editors, automated rules, API-based tools, or frequent experiments. ([support.google.com](https://support.google.com/google-ads/answer/19888/change-history?hl=en&utm_source=openai))
A practical habit: if you spot a weekly inflection point (performance was stable for three weeks, then sharply shifts), check change history for the day or two leading into that week boundary. You’ll catch most self-inflicted performance swings in under five minutes.
Step 5: If nothing changed, look for “mix shifts” hiding inside the week
Weekly totals can hide big internal shifts. The fastest “under the hood” checks are to re-run the same weekly trend view, but add one extra segmentation lens at a time: device, network, audience, and conversion action. If one segment expands while another shrinks, your blended CPA/ROAS can move even if each segment is stable on its own.
When you find the segment driving the shift, you can decide whether it’s acceptable (for example, mobile share grew but CPA rose slightly) or whether you need to intervene (for example, Search partners expanded unexpectedly, or a specific audience segment began soaking budget with weak conversion rate).
Spike/drop triage checklist (use when a week moves more than expected)
- Confirm the date range doesn’t include today if you’re expecting explanations, and compare equal-length ranges. ([support.google.com](https://support.google.com/google-ads/answer/9000655?hl=en-GB&utm_source=openai))
- Check reporting latency and whether conversions are still “arriving” for the week you’re judging. ([support.google.com](https://support.google.com/google-ads/answer/2544985?hl=en&utm_source=openai))
- Review change history for the inflection window (1–3 days before the shift). ([support.google.com](https://support.google.com/google-ads/answer/19888/change-history?hl=en&utm_source=openai))
- Segment the weekly view by device/audience/network to uncover mix shifts.
- If you use automated bidding, evaluate performance primarily on the goal-aligned metrics the strategy optimizes toward (and don’t overreact to non-goal metrics). ([support.google.com](https://support.google.com/google-ads/answer/12294038?hl=en&utm_source=openai))
Turning trend insight into action (without thrashing the account)
The best weekly performance analysts don’t just “spot trends”; they decide what deserves action. If the trend is small and within normal variability, document it and keep watching. If the trend is meaningful, act at the right level of the account: fix tracking before bidding, fix landing page issues before rewriting every ad, and address budget limitations before you declare a strategy “failed.”
Finally, if your account sometimes shows limited insights on the Insights page, don’t assume you set something up wrong. Some insights only appear when there’s enough data, compatible campaign types, and a significant change worth surfacing—so treat insights as an assistant, not your only weekly diagnostic tool. ([support.google.com](https://support.google.com/google-ads/answer/10260432?hl=en&utm_source=openai))
Let AI handle
the Google Ads grunt work
| Aspect of weekly analysis | What to do in practice | Why it matters for weekly trends | Relevant Google Ads features / docs |
|---|---|---|---|
| Use complete, comparable data | Avoid judging the most recent week until reporting has largely finished updating. Build a rhythm such as reviewing last Monday–Sunday on Tuesday or Wednesday, and be cautious when using non–last-click attribution where processing can take longer. | Prevents “false alarms” where a fresh week looks worse simply because conversions and some stats haven’t fully posted yet. |
About data freshness About conversion windows |
| Account for conversion delay | Before reacting to a “down” week, segment conversion columns by “Days to conversion” to understand your typical lag and how long you should wait before declaring a trend. | Ensures you’re not undercounting recent weeks when many conversions reliably arrive several days after the click. |
Bidding and conversion delay Use segments in your tables (Days to conversion) |
| Define a consistent “week” | Choose a week definition (typically Monday–Sunday) and use it consistently in all dashboards, reports, and analysis. | Avoids mixing partial weeks or shifting week boundaries, which can blur day‑of‑week seasonality and make trends look erratic. | Use segments in your tables (segment by Week) |
| Set a KPI hierarchy (“north star”) | Pick one primary outcome metric (for example, conversions, conversion value, ROAS, or CPA) and a small set of supporting metrics (spend, clicks, CPC, conversion rate). Align evaluation with what your bid strategies optimize toward. | Keeps weekly conversations focused on business outcomes first and prevents overreacting to secondary metrics that don’t match campaign goals. |
Why you might not have explanations (goal‑aligned metrics) Bidding overview |
| Build a weekly “by Week” table view | Take the statistics table (Campaigns, Ad groups, Keywords, etc.) and apply a time segment of “Week” so each row is a week and your key KPIs stay in the columns. | Gives a fast, native view of how performance moves week over week at the level you already manage (campaigns, ad groups, keywords). |
Use segments in your tables Add or remove columns in your statistics table |
| Use date‑range comparison without “today” | For week‑over‑week reads, select a 7‑day (or Monday–Sunday) period and compare to the previous period of equal length. Exclude “today” if you want explanations to appear. | Ensures fair comparisons that control for day‑of‑week and unlocks explanations for significant changes between the two weeks. |
About explanations Why you might not have explanations |
| Save and schedule your weekly report | Once your weekly table is segmented by Week with the right KPIs and filters (for example, only Brand campaigns), save it as a report and schedule a weekly email for your stakeholders. | Makes weekly reviews consistent and repeatable, so the team is always looking at the same lens rather than rebuilding views every Monday. |
Create, save, and schedule reports from your statistics tables Create and manage reports |
| Use dashboards for multi‑stakeholder views | Combine scorecards (with week‑over‑week deltas) and charts by campaign type or objective in a dashboard. Add annotations such as promos, outages, or budget caps. | Keeps context attached to trends, making performance swings interpretable months later and easier to share across teams. | Create and edit dashboards |
| Lock a reusable weekly setup | Standardize your KPI set, Week segment, previous‑period comparison, and scheduled report cadence. Reuse the same setup every week. | Reduces noise and analysis time, so trend changes are visible immediately rather than buried under ad‑hoc reporting variations. |
Use segments in your tables Create, save, and schedule reports from your statistics tables |
| Separate volume vs. efficiency issues | When a week moves, first classify the shift: did spend change, did conversions change, and how did CPA/ROAS move? Decide whether you’re facing a delivery issue (volume) or an efficiency issue (conversion rate, quality, or mix). | Prevents misdiagnosis, such as treating a budget or eligibility issue like a conversion‑rate problem, or vice versa. | Create and manage reports (to view key performance metrics together) |
| Validate data freshness before acting | For a week that just ended, confirm that reporting latency and conversion lag are understood, especially if you use non–last‑click or data‑driven attribution. | Stops you from making bid or budget changes based on incomplete or still‑updating data. |
About data freshness Bidding and conversion delay |
| Use explanations as fast triage | When week‑over‑week shifts are large, use explanations on compatible campaigns and metrics to see if Google flags issues like budget constraints, targeting changes, or auction shifts. | Quickly narrows down the likely causes of big changes so you can spend more time fixing and less time hunting for the “why.” |
About explanations Why you might not have explanations |
| Check change history around inflection points | When you see a sharp weekly shift after a stable period, use change history to review edits in the 1–3 days before the new week: budgets, bid strategies, targets, keywords, audiences, ads, and schedules. | Quickly surfaces “self‑inflicted” changes (manual or automated) that explain many sudden performance swings. | About change history |
| Look for mix shifts inside the week | Re‑run the weekly trend view and add one segmentation lens at a time (device, network, audience, conversion action) to see whether the mix shifted even if totals look similar. | Reveals hidden drivers such as more spend on mobile, Search partners, or a particular audience that can change blended CPA/ROAS without obvious top‑line changes. | Use segments in your tables |
| Run a structured spike/drop triage | For unexpectedly big weekly moves: confirm equal‑length ranges without today, check data freshness and conversion lag, review change history, and then segment by device/audience/network. | Gives you a repeatable playbook for diagnosing weekly spikes and drops without jumping straight to drastic account changes. |
About data freshness About change history Use segments in your tables |
| Turn insights into measured actions | Decide which weekly trends are within normal variability (monitor and document) and which justify action. Fix tracking and measurement before bids, fix landing pages before rewriting ads, and resolve budget caps before judging strategy. | Prevents over‑reactive optimization and ensures you address root causes in the right order instead of repeatedly “thrashing” the account. |
Evaluate the performance of your landing pages Why you might not have insights About the Insights page |
| Use the Insights page as a supplement | Review the Insights page for performance shifts, auction insights, and change history insights, but don’t rely on it as your only diagnostic tool—some insights only surface when there’s enough data and a meaningful change. | Adds machine‑generated context to your weekly trends while keeping your core analysis grounded in your own reporting views. |
About the Insights page Why you might not have insights |
Let AI handle
the Google Ads grunt work
Weekly trend analysis works best when you make the weeks comparable and the data trustworthy: use a consistent Monday–Sunday definition, wait until reporting has refreshed (especially if you have conversion lag or use data-driven attribution), and focus on a clear KPI hierarchy (one “north star” like CPA/ROAS plus a few supporting metrics like spend, clicks, and CVR). In Google Ads, a practical workflow is to segment your main tables by “Week,” compare equal 7-day ranges while excluding “today” to unlock explanations, and then triage big swings by checking conversion delay (“Days to conversion”), reviewing change history around inflection points, and segmenting (device, network, audience, conversion action) to spot mix shifts before taking action. If you want a more repeatable way to keep this cadence without rebuilding reports each week, Blobr connects to your Google Ads and runs AI agents that continuously monitor what changed since last week, surface likely drivers, and turn findings into clear, prioritized recommendations you can apply on your terms.
1) Set the ground rules: what a “weekly trend” actually means (and how to avoid bad reads)
Use complete, comparable data before you judge a week
Weekly trend analysis only works if you’re comparing like-for-like. In Google Ads, “recent” numbers can be incomplete for two common reasons: reporting latency (some metrics aren’t fully processed right away) and conversion delay (people often convert days after the click). If you compare a fresh week that’s still “filling in” against an older week that’s fully matured, you’ll often conclude performance dropped when it’s really just delayed reporting.
As a baseline, assume most top-line stats update within hours, but some reporting and certain attribution setups can take longer, and some reports are processed only once per day. Build your weekly review rhythm around that reality (for example, reviewing the prior Monday–Sunday week on Tuesday or Wednesday rather than first thing Monday). ([support.google.com](https://support.google.com/google-ads/answer/2544985?hl=en&utm_source=openai))
For conversion-led accounts, the cleaner approach is to pair weekly trend review with a quick “conversion delay” check (more on that below). It’s the difference between reacting to noise and managing a predictable lag that happens every week. ([support.google.com](https://support.google.com/google-ads/faq/10286469?hl=en&utm_source=openai))
Pick the right week definition and stick to it
Decide what “a week” is for your business and keep it consistent across dashboards and reports. Most advertisers use Monday–Sunday. The important thing is consistency: if your week boundary shifts, you’ll blur day-of-week seasonality and make trends look “choppy” even when nothing changed.
Know which KPIs should lead your weekly narrative
Weekly trend conversations go off the rails when teams mix volume metrics (impressions, clicks, spend) with outcome metrics (conversions, conversion value, ROAS/CPA) without a hierarchy. In practice, you want one “north star” outcome metric, supported by two or three diagnostic metrics that explain movement.
A simple structure that works across most accounts is: outcomes first (conversions or conversion value and efficiency), then the volume and rate metrics that explain why (cost, click volume, conversion rate, average CPC). When the account uses automated bidding, keep your evaluation aligned to what the campaign is optimizing toward, because not every metric change is equally meaningful in that context. ([support.google.com](https://support.google.com/google-ads/answer/12294038?hl=en&utm_source=openai))
2) Build a weekly trend view that answers “what changed?” in minutes
Start with the fastest view: segment your table by Week
The quickest way to see weekly performance trends inside Google Ads is to take the table you already trust (Campaigns, Ad groups, Keywords, etc.) and segment it by time. Use the segment control in the table and choose a time-based segment like “Week” so each row becomes a week, and your columns stay your KPIs. ([support.google.com](https://support.google.com/google-ads/answer/2454072?hl=en-GB&ref_topic=3119142))
Two practical notes matter here. First, some segment/column combinations don’t work, especially conversion-related segments with non-conversion columns, and that can produce blanks that look like “missing data” when it’s really an incompatible view. Second, if you try to segment too granularly (like Day), Google Ads limits it to shorter date ranges; if you need longer periods, Week is usually the right tradeoff between detail and usability. ([support.google.com](https://support.google.com/google-ads/answer/2454072?hl=en-GB&ref_topic=3119142))
Use date-range comparison the right way (and don’t include “today”)
For weekly trend reads, most teams want week-over-week comparison. Google Ads supports this in a few ways, but the most actionable workflow is: set your date range to the last 7 days (or a specific Monday–Sunday week), then compare to the previous period of equal length. That keeps the comparison fair and naturally controls for day-of-week patterns.
If you rely on in-platform explanations to understand the “why,” be aware of two key constraints: explanations won’t show if your selected date range includes today (because today’s data can still change), and custom comparisons need equal-length, contiguous ranges to work reliably. ([support.google.com](https://support.google.com/google-ads/answer/9000655?hl=en&utm_source=openai))
Turn your weekly view into a repeatable report (so you’re not rebuilding it every Monday)
Once your table shows weekly rows and the KPI columns you want, save it as a report and schedule it to email on a cadence that fits your business (weekly is the obvious default). Scheduling from the statistics table preserves the filters you applied (for example, only Brand campaigns, only a region, only Search). ([support.google.com](https://support.google.com/google-ads/answer/2404176?hl=en&utm_source=openai))
If you manage multiple stakeholders, a dashboard is often better than a single report because you can combine a scorecard (with percent change and a sparkline) plus one or two charts that show weekly movement by campaign type or objective. Dashboards are also easier to annotate for context (“promo week,” “site outage,” “budget capped”) so the trend remains interpretable months later. ([support.google.com](https://support.google.com/google-ads/answer/6379084/create-and-edit-dashboards?hl=en-GB))
Critical weekly setup checklist (do this once, then reuse forever)
- Lock your KPI set (north star + supporting metrics) and keep it consistent week to week.
- Segment your primary performance table by Week to make the trend obvious. ([support.google.com](https://support.google.com/google-ads/answer/2454072?hl=en-GB&ref_topic=3119142))
- Use Previous period comparisons for true week-over-week reads, and avoid date ranges that include today if you want explanations to appear. ([support.google.com](https://support.google.com/google-ads/answer/9000655?hl=en-GB&utm_source=openai))
- Save the view and schedule it weekly so your team reviews the same lens every time. ([support.google.com](https://support.google.com/google-ads/answer/2404176?hl=en&utm_source=openai))
3) Diagnose weekly shifts systematically: from “we’re down” to the real driver
Step 1: Separate a volume problem from an efficiency problem
When a week moves, start by classifying the shift. If conversions dropped but spend also dropped, you may have a delivery issue (budgets, bids, eligibility, limited inventory). If spend held and conversions dropped, that’s more likely efficiency (conversion rate, traffic quality, landing page, tracking, or mix shift). If spend rose and efficiency fell, it might be an intentional trade (scaling) or a loss of targeting discipline.
Step 2: Validate the data (freshness + conversion lag) before you take action
If you’re looking at a week that ended yesterday, remember reporting isn’t always final. Some statistics are delayed, and certain attribution setups can delay conversion reporting longer than basic last-click reporting. ([support.google.com](https://support.google.com/google-ads/answer/2544985?hl=en&utm_source=openai))
Then check conversion delay directly. Segment conversion columns by “Days to conversion” to understand how long conversions typically take in your account. If you consistently see meaningful volume arriving 2–7 days after the click, your “last week” conversions will almost always look weak at first glance, especially compared to older weeks. ([support.google.com](https://support.google.com/google-ads/faq/10286469?hl=en&utm_source=openai))
Step 3: Use in-platform explanations to narrow the cause quickly
When Google Ads detects a significant fluctuation, explanations can highlight what changed between two comparable ranges. In practice, I use explanations as a triage tool: they’re not a replacement for analysis, but they often point you to the fastest path (for example, “conversion delay increased,” “budget constrained,” “auction dynamics shifted,” or “target changes”). Remember: they’re designed to focus on metrics tied to campaign goals and may not trigger for every metric or every small fluctuation. ([support.google.com](https://support.google.com/google-ads/answer/9000655?hl=en&utm_source=openai))
Step 4: Cross-check the “did we change something?” layer
Before you blame the market, confirm whether you (or an automation) changed something. Use change history to review edits by date: budgets, bid strategies, targets, keywords, audiences, ads/assets, schedules, etc. This is especially important in accounts with multiple editors, automated rules, API-based tools, or frequent experiments. ([support.google.com](https://support.google.com/google-ads/answer/19888/change-history?hl=en&utm_source=openai))
A practical habit: if you spot a weekly inflection point (performance was stable for three weeks, then sharply shifts), check change history for the day or two leading into that week boundary. You’ll catch most self-inflicted performance swings in under five minutes.
Step 5: If nothing changed, look for “mix shifts” hiding inside the week
Weekly totals can hide big internal shifts. The fastest “under the hood” checks are to re-run the same weekly trend view, but add one extra segmentation lens at a time: device, network, audience, and conversion action. If one segment expands while another shrinks, your blended CPA/ROAS can move even if each segment is stable on its own.
When you find the segment driving the shift, you can decide whether it’s acceptable (for example, mobile share grew but CPA rose slightly) or whether you need to intervene (for example, Search partners expanded unexpectedly, or a specific audience segment began soaking budget with weak conversion rate).
Spike/drop triage checklist (use when a week moves more than expected)
- Confirm the date range doesn’t include today if you’re expecting explanations, and compare equal-length ranges. ([support.google.com](https://support.google.com/google-ads/answer/9000655?hl=en-GB&utm_source=openai))
- Check reporting latency and whether conversions are still “arriving” for the week you’re judging. ([support.google.com](https://support.google.com/google-ads/answer/2544985?hl=en&utm_source=openai))
- Review change history for the inflection window (1–3 days before the shift). ([support.google.com](https://support.google.com/google-ads/answer/19888/change-history?hl=en&utm_source=openai))
- Segment the weekly view by device/audience/network to uncover mix shifts.
- If you use automated bidding, evaluate performance primarily on the goal-aligned metrics the strategy optimizes toward (and don’t overreact to non-goal metrics). ([support.google.com](https://support.google.com/google-ads/answer/12294038?hl=en&utm_source=openai))
Turning trend insight into action (without thrashing the account)
The best weekly performance analysts don’t just “spot trends”; they decide what deserves action. If the trend is small and within normal variability, document it and keep watching. If the trend is meaningful, act at the right level of the account: fix tracking before bidding, fix landing page issues before rewriting every ad, and address budget limitations before you declare a strategy “failed.”
Finally, if your account sometimes shows limited insights on the Insights page, don’t assume you set something up wrong. Some insights only appear when there’s enough data, compatible campaign types, and a significant change worth surfacing—so treat insights as an assistant, not your only weekly diagnostic tool. ([support.google.com](https://support.google.com/google-ads/answer/10260432?hl=en&utm_source=openai))
