Reporting

Find high-traffic landing pages that fail to convert

Ranks landing pages with real traffic but weak purchase rates by lost purchases, adds organic data from Search Console and Core Web Vitals for the worst offenders, and names the three pages to fix first.

When to use it

When pages get plenty of visitors but few buy, and you need to know which ones to fix first.

What you get

A table of landing pages ranked by lost purchases with organic and Core Web Vitals data, plus the three pages to fix first and one concrete fix each.

Does it change anything in my account?

No. The assistant only reads your data and reports back.

Works with

GA4, Search Console, SEO Intelligence

Fill in before sending

[GA4 property][Search Console property][https://www.example.com]

Find the landing pages that get traffic but fail to convert. GA4 property: [GA4 property]. Search Console property: [Search Console property]. Site: [https://www.example.com]. This is read-only. 1. List the key events in the GA4 property so we know what counts as a conversion. 2. Pull sessions and key events by landing page for the last 30 days. To isolate purchases, use landing page as the first dimension and event as the second. If that combination is not accepted, use the key events column instead, and if purchase is not the only key event, label the column "all key events" rather than "purchases" for the rest of the report. 3. Keep only pages with at least 300 sessions. Calculate each page's session-to-purchase rate and the site-wide average (total purchases / total sessions for the whole property over the same 30 days). Flag any page whose rate is below 50% of the site average. 4. Search Console: pull clicks, impressions and average position by page for the same 30 days in one call with a high row limit. Match the rows to the GA4 landing pages by path, ignoring the domain, query strings and trailing slashes. If a flagged page has no Search Console row, write "no organic data" rather than zero. Add organic clicks as a share of GA4 sessions, and note that the two tools count differently, so this is a proxy. 5. Core Web Vitals (mobile) for the top 5 flagged pages by sessions: LCP, INP and CLS. Use real-user field data where it exists. If a URL has no field data, show the lab values and mark them as lab. 6. Deliver a table ranked by lost purchases, where lost purchases = sessions × (site average rate − page rate). Columns: URL, sessions, purchases, rate, lost purchases, organic clicks, average position, LCP, INP, CLS, likely issue. Base the likely issue on the data you have: slow vitals, an intent mismatch suggested by position and traffic mix, or a rate gap with healthy vitals that points to the page content or offer. End with the three pages to fix first and one concrete fix for each.

Find the landing pages that get traffic but fail to convert. GA4 property: [GA4 property]. Search Console property: [Search Console property]. Site: [https://www.example.com]. This is read-only.

1. List the key events in the GA4 property so we know what counts as a conversion.
2. Pull sessions and key events by landing page for the last 30 days. To isolate purchases, use landing page as the first dimension and event as the second. If that combination is not accepted, use the key events column instead, and if purchase is not the only key event, label the column "all key events" rather than "purchases" for the rest of the report.
3. Keep only pages with at least 300 sessions. Calculate each page's session-to-purchase rate and the site-wide average (total purchases / total sessions for the whole property over the same 30 days). Flag any page whose rate is below 50% of the site average.
4. Search Console: pull clicks, impressions and average position by page for the same 30 days in one call with a high row limit. Match the rows to the GA4 landing pages by path, ignoring the domain, query strings and trailing slashes. If a flagged page has no Search Console row, write "no organic data" rather than zero. Add organic clicks as a share of GA4 sessions, and note that the two tools count differently, so this is a proxy.
5. Core Web Vitals (mobile) for the top 5 flagged pages by sessions: LCP, INP and CLS. Use real-user field data where it exists. If a URL has no field data, show the lab values and mark them as lab.
6. Deliver a table ranked by lost purchases, where lost purchases = sessions × (site average rate − page rate). Columns: URL, sessions, purchases, rate, lost purchases, organic clicks, average position, LCP, INP, CLS, likely issue. Base the likely issue on the data you have: slow vitals, an intent mismatch suggested by position and traffic mix, or a rate gap with healthy vitals that points to the page content or offer.

End with the three pages to fix first and one concrete fix for each.

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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.