Reporting

Device purchase rates in GA4 and matching Google Ads bid adjustments

Compare GA4 purchase rates and Google Ads cost per conversion by device, then preview device bid adjustments that match the evidence, with a clear note on how Smart Bidding treats them.

When to use it

When you wonder whether your Google Ads device bid adjustments match how mobile, desktop and tablet visitors really buy, or why one device costs more per conversion.

What you get

A table combining GA4 purchase rate with Ads cost per conversion and ROAS by device, plus previewed bid adjustments (or a Smart Bidding note) applied only after your approval.

Does it change anything in my account?

Only with your approval. The assistant first shows a preview of every change and waits for your go-ahead.

Works with

GA4, Google Ads

Fill in before sending

[campaign][Google Ads account ID][GA4 property][target CPA]

I want to know whether device bid adjustments on Google Ads campaign [campaign] in account [Google Ads account ID] reflect how each device actually buys on the site. 1. GA4 side, property [GA4 property], last 30 days: - List the key events and confirm the exact event name used for purchases (usually "purchase"). - Pull sessions by device category (ga4_report, dimension "device"). - Pull key events by device split by event name (dimension "device", dimension2 "event") and keep only the purchase event. - Purchase rate per device = purchase events / sessions. Note that these are site-wide numbers across all channels, not only Google Ads traffic. 2. Google Ads side, same 30 days, campaign [campaign]: - Pull cost, conversions and conversion value by device with performance_report segmented by device. If that segment is not returned, run a read-only GAQL query on the campaign resource with segments.device and metrics.cost_micros, metrics.conversions, metrics.conversions_value. - Calculate cost per conversion and ROAS (conversion value / cost) yourself. 3. Current setup: - Read the campaign's device criteria with get_campaign_targeting. If the bid modifier values are not in that response, read them with GAQL from campaign_criterion (campaign_criterion.device.type, campaign_criterion.bid_modifier) filtered to this campaign. - Read the campaign's bidding strategy with list_entities (entity campaigns). 4. Build one table with these columns: device, GA4 sessions, GA4 purchase rate, Ads cost, Ads conversions, cost per conversion, ROAS, current bid adjustment. Map GA4 desktop/mobile/tablet to the Ads DESKTOP/MOBILE/TABLET rows, and list CONNECTED_TV separately if it has spend. 5. Decision rules: - Skip any device with fewer than 20 Ads conversions in the period and say so. - GA4 purchase rate below 70% of the all-device average AND Ads cost per conversion above 130% of [target CPA]: propose a -20% bid adjustment. - GA4 purchase rate above 120% of the average AND cost per conversion below [target CPA]: propose +10%. - If CONNECTED_TV has spend with no conversions, flag it separately and tell me whether excluding it (-100%) makes sense. - If the campaign uses Smart Bidding (Maximize Conversions, Maximize Conversion Value, Target CPA, Target ROAS), tell me plainly that Google ignores device bid adjustments under these strategies except -100% exclusions, so a -20% or +10% would have no real effect. In that case, recommend what would actually move results instead and do not push the percentage changes. 6. Show every proposed adjustment as a preview first (manage_device_targeting with confirm=false). Do not apply anything until I approve each one.

I want to know whether device bid adjustments on Google Ads campaign [campaign] in account [Google Ads account ID] reflect how each device actually buys on the site.

1. GA4 side, property [GA4 property], last 30 days:
- List the key events and confirm the exact event name used for purchases (usually "purchase").
- Pull sessions by device category (ga4_report, dimension "device").
- Pull key events by device split by event name (dimension "device", dimension2 "event") and keep only the purchase event.
- Purchase rate per device = purchase events / sessions. Note that these are site-wide numbers across all channels, not only Google Ads traffic.

2. Google Ads side, same 30 days, campaign [campaign]:
- Pull cost, conversions and conversion value by device with performance_report segmented by device. If that segment is not returned, run a read-only GAQL query on the campaign resource with segments.device and metrics.cost_micros, metrics.conversions, metrics.conversions_value.
- Calculate cost per conversion and ROAS (conversion value / cost) yourself.

3. Current setup:
- Read the campaign's device criteria with get_campaign_targeting. If the bid modifier values are not in that response, read them with GAQL from campaign_criterion (campaign_criterion.device.type, campaign_criterion.bid_modifier) filtered to this campaign.
- Read the campaign's bidding strategy with list_entities (entity campaigns).

4. Build one table with these columns: device, GA4 sessions, GA4 purchase rate, Ads cost, Ads conversions, cost per conversion, ROAS, current bid adjustment. Map GA4 desktop/mobile/tablet to the Ads DESKTOP/MOBILE/TABLET rows, and list CONNECTED_TV separately if it has spend.

5. Decision rules:
- Skip any device with fewer than 20 Ads conversions in the period and say so.
- GA4 purchase rate below 70% of the all-device average AND Ads cost per conversion above 130% of [target CPA]: propose a -20% bid adjustment.
- GA4 purchase rate above 120% of the average AND cost per conversion below [target CPA]: propose +10%.
- If CONNECTED_TV has spend with no conversions, flag it separately and tell me whether excluding it (-100%) makes sense.
- If the campaign uses Smart Bidding (Maximize Conversions, Maximize Conversion Value, Target CPA, Target ROAS), tell me plainly that Google ignores device bid adjustments under these strategies except -100% exclusions, so a -20% or +10% would have no real effect. In that case, recommend what would actually move results instead and do not push the percentage changes.

6. Show every proposed adjustment as a preview first (manage_device_targeting with confirm=false). Do not apply anything until I approve each one.

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More prompts

ReportingChanges after your approval

Monthly paid channel efficiency ranking with a budget split proposal

A ranking of every paid channel by cost per conversion, with spend share vs conversion share and a proposed budget split for next month.

When to use itWhen a new month is starting and you need to decide which paid channels deserve more or less budget.

Compare last calendar month across Google Ads, Meta, TikTok and Microsoft Ads.

Build one table with these columns for each channel:
- spend
- conversions
- cost per conversion
- conversion value and ROAS, where value is tracked
- share of total spend (%)
- share of total conversions (%)

Rank the channels by cost per conversion. My target cost per conversion is [target CPA].

Decision rules:
- If a channel's share of conversions is at least 10 points below its share of spend, mark it as a candidate to cut.
- If a channel's share of conversions is at least 10 points above its share of spend, mark it as a candidate to scale. For Google Search, only do this if impression share analysis shows the campaigns are losing impression share to budget.
- If a channel has fewer than 10 conversions, mark it "not enough data" and leave its budget unchanged.

Then propose next month's split in a table with current monthly spend, proposed spend and a one-line reason for each channel. Keep the total within [monthly budget].

Do not apply anything. If I approve, show a preview of each budget change and wait for my confirmation.

ReportingChanges after your approval

Month-to-date budget pacing across Google, Meta and TikTok

A pacing table showing which channels will overspend or underspend by month-end, with previewed daily budget corrections.

When to use itWhen you want to know if Google, Meta and TikTok are on track to overspend or underspend your monthly budget.

Check my budget pacing for the current month. My monthly plan is Google Ads [Google monthly budget], Meta [Meta monthly budget] and TikTok [TikTok monthly budget].

1. For Google Ads, use budget pacing to project each campaign's month-end spend against its daily budget.
2. For Meta and TikTok:
   - Take the month-to-date spend and the average daily spend of the last 7 days.
   - Project spend to month-end from those two numbers.
3. Deliver one table with these columns: channel, campaign, planned monthly spend, month-to-date spend, projected month-end spend, variance (%).

Decision rules:
- Flag any channel projected more than 10% over or under plan.
- For each flagged channel, propose the new daily budget that lands it on plan.
- Take the cut from the campaigns with the highest cost per conversion over the last 14 days.
- Add the extra budget to the campaigns with the lowest cost per conversion.

Do not change anything yet. Show a preview of every proposed budget change, with old and new daily amounts, and apply only the ones I confirm.

ReportingChanges after your approval

Monthly Google Ads client report with a shareable white-label link

Builds a one-page, jargon-free monthly report for a client with exact calendar-month figures versus the previous month, explains every big KPI move from campaign data and creates the white-label link only after you approve the draft.

When to use itWhen you have to send a client their monthly Google Ads results in plain language and explain what moved and why.

Prepare the monthly Google Ads report for [client account] for [month, e.g. 2026-09], compared with the previous calendar month.

Data:
- Use list_accounts to find the customer ID of [client account].
- Pull exact calendar-month figures with get_report (platform google_ads) using start_date and end_date for [month], then again for the previous month. Get both the account totals and the campaign-level rows.
- If you need more campaign detail, use performance_report at campaign level.
- KPIs: cost, clicks, conversions, conversion value, CPA, ROAS and CTR, each with the % change. If conversion value is not tracked in this account, leave ROAS out and say why in one line.

Write it for a client who is not a marketer:
1. A 3-sentence executive summary covering the result, the main driver and what comes next.
2. A KPI table with this month, last month and % change.
3. The top 3 campaigns by conversions. Also name the campaign with the highest CPA among those that took more than 10% of the month's total spend.
4. Three concrete next steps we will take next month, each tied to something in the data.

Flag every KPI that moved more than 15%. Explain each one with evidence from the campaign-level rows, such as a spend shift between campaigns, a CPA change in a specific campaign or a conversion drop. If the data does not explain the move, say so plainly and do not guess.

Keep it to one page with no jargon. Show me the draft first and stop.

After I approve the draft, create the shareable white-label link with client_report_link using brand_name [agency or client brand name]. Note that this tool only takes a rolling days window ending now, not a named month. So before creating it, tell me which exact dates the link will cover with the days value you plan to use, and whether that matches [month]. If it does not match (for example we are well past month end), tell me that clearly and let me choose: create the link anyway with its rolling window, or skip the link and I will send the approved draft myself. Then give me the URL.

ReportingRead-only

Agency portfolio triage: which client accounts need attention this week

Compares every connected Google Ads client account over the last 30 days versus the previous 30, adds month-to-date budget pacing, ranks accounts by risk and gives account managers one data-backed first check for the top 5. Read-only.

When to use itWhen you manage many Google Ads client accounts and need to know which ones to open first this week.

Triage all connected Google Ads client accounts. Compare the last 30 days with the previous 30 days.

How to get the numbers:
- Run portfolio_report with days=30, then again with days=60. For each account, derive the previous 30 days by subtracting the 30-day figures from the 60-day figures (cost, clicks, conversions, conversion value), then calculate CPA and ROAS for both periods yourself.
- Use list_accounts to get each account's customer ID, then run budget_pacing_alerts with threshold_pct=10 for each account. Pacing is a month-to-date projection to month-end, not the 30-day window, so label that column "pacing (this month)".
- If an account does not track conversion value, mark ROAS as n/a instead of treating it as below target.

Mark an account as at risk if it meets any of these conditions:
- CPA up more than 20%
- Conversions down more than 20%
- ROAS below [target ROAS, or list per-account targets]
- Any campaign flagged by budget pacing as over or under by more than 10% by month-end

Deliver one table with these columns: account, spend, conversions, CPA, ROAS, % change in CPA and conversions, pacing (this month), risk reasons. Sort it by the number of risk reasons, then by spend.

Below the table, list the 5 accounts our account managers should open first this week. For those 5 only, run conversion_tracking_health (days=30) and search_terms_report (days=30, sort by cost) to ground the advice. Give one concrete first check for each, for example "search terms in campaign X are spending without conversions" or "conversion action Y recorded nothing in 30 days", based on what the data shows. If neither report explains the account's problem, say what you would look at next instead of guessing.

Finish with the accounts that improved the most. These are good news for client calls.

This is read-only. Do not change anything in any account.