SEO and content

Diagnose an organic traffic drop: rankings, demand or CTR

Compares Search Console before and after a drop date, classifies each losing page as a ranking, demand or CTR loss, checks the top suspects and delivers a one-page memo with the first fixes.

When to use it

When your organic clicks suddenly dropped and you need to know whether you lost rankings, search demand or click-through rate.

What you get

A one-page memo naming the dominant cause, a table of the affected pages with before/after metrics and their loss type, and the 3 fixes to do first.

Does it change anything in my account?

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

Works with

Search Console, SEO Intelligence

Fill in before sending

[property URL][date of drop]

Organic clicks on [property URL] dropped around [date of drop]. Compare the 14 days after that date with the 14 days before it in Search Console. Search Console data lags about 2 to 3 days, so if fewer than 14 complete days have passed since the drop, use the days available on both sides and tell me. 1. Confirm the property, then pull the daily click series for the period so we can see the exact day the drop started. If it clearly started on a different day, use that day and say so. 2. Check which breakdowns the Search Console report supports, then pull both 14-day windows by explicit start and end dates, broken down by page, by query, by device and by country (one call per breakdown and window). Show the top movers for each. 3. For the 20 pages that lost the most clicks, classify each loss with this rule: - Ranking loss: average position worsened by 2 or more and impressions fell. - Demand loss: position is stable (within 1) and impressions fell by more than 20%. - CTR loss: position and impressions are stable but CTR fell by more than 25%. - Mixed: anything that fits none or more than one of these. For the 5 biggest ranking losses: - inspect the URL for index, canonical and crawl problems, - read the stored rank history (it is free) for those pages' main queries if they are tracked, and say which ones are not. For demand losses, check seasonality on up to 5 of the affected main queries with keyword trends before blaming the site. This costs SEO add-on credits, so first check whether the same query was already run and can be reused for free, and skip it if the add-on is not available. For the top 3 CTR losses, optionally check whether Google currently shows an AI Overview for the main query and whether my site is cited (add-on credits). This shows today's results page only, not what it looked like before the drop, so treat it as supporting evidence. Deliver a memo of at most one page: - the dominant cause with the numbers behind it, - a table of the affected pages with clicks, impressions, CTR and position for both windows and their classification, - the 3 fixes to do first. Mark every statement as either measured (it comes straight from the data) or hypothesis. Attributing a CTR loss to a SERP feature or AI Overview is always a hypothesis.

1. Confirm the property, then pull the daily click series for the period so we can see the exact day the drop started. If it clearly started on a different day, use that day and say so.
2. Check which breakdowns the Search Console report supports, then pull both 14-day windows by explicit start and end dates, broken down by page, by query, by device and by country (one call per breakdown and window). Show the top movers for each.
3. For the 20 pages that lost the most clicks, classify each loss with this rule:
- Ranking loss: average position worsened by 2 or more and impressions fell.
- Demand loss: position is stable (within 1) and impressions fell by more than 20%.
- CTR loss: position and impressions are stable but CTR fell by more than 25%.
- Mixed: anything that fits none or more than one of these.

For the 5 biggest ranking losses:
- inspect the URL for index, canonical and crawl problems,
- read the stored rank history (it is free) for those pages' main queries if they are tracked, and say which ones are not.

For demand losses, check seasonality on up to 5 of the affected main queries with keyword trends before blaming the site. This costs SEO add-on credits, so first check whether the same query was already run and can be reused for free, and skip it if the add-on is not available.

For the top 3 CTR losses, optionally check whether Google currently shows an AI Overview for the main query and whether my site is cited (add-on credits). This shows today's results page only, not what it looked like before the drop, so treat it as supporting evidence.

Deliver a memo of at most one page:
- the dominant cause with the numbers behind it,
- a table of the affected pages with clicks, impressions, CTR and position for both windows and their classification,
- the 3 fixes to do first.

Mark every statement as either measured (it comes straight from the data) or hypothesis. Attributing a CTR loss to a SERP feature or AI Overview is always a hypothesis.

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Build intent-based keyword clusters from a few seed keywords

A ranked list of keyword clusters with volume, difficulty and the right page type or ad group for each.

When to use itWhen you are starting keyword research for a business and only have a few seed keywords.

I sell [product or service] in [city or country]. Start from these seed keywords: [3 to 5 seed keywords].

First check seo_history so you reuse earlier results instead of paying for the same query twice. Then use Keyword Planner and SEO keyword research to collect ideas with average monthly searches, competition and keyword difficulty.

Clean the list:
- remove keywords under 30 monthly searches
- remove job, free, DIY, course and competitor-brand terms unless they clearly fit my business
- merge close variants and plurals

Cluster the rest by search intent, with one cluster per distinct need a searcher has. For each cluster give:
- cluster name and head keyword
- 5 to 10 supporting keywords
- combined monthly volume and average difficulty
- intent: informational, commercial or transactional
- best use: service page, blog post or a Google Ads ad group

Rank the clusters by volume and fit for a small business with a limited budget. Mark the top 3 to start with and explain why in one line each.

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Turn search terms that convert in ads into organic content clusters

Uses 90 days of converting Google Ads and Microsoft Ads search terms to find topics where you pay for conversions but have weak or no organic visibility. Returns a prioritized content plan and a preview-only list of keyword additions.

When to use itWhen you pay for conversions on search terms but are unsure which topics deserve organic content so you can rely less on ads.

I want to use my ad data to decide what to write about. Use the last 90 days on every source.

1. Pull my Google Ads search terms for the last 90 days (days=90, or an explicit start and end date), sorted by conversions. Keep only terms with at least 1 conversion. If the response is truncated, page through until you have every converting term.
2. If Microsoft Ads is connected, find the default account and pull its search terms for the same 90 days. Keep only terms with at least 1 conversion and add them to the pool, marking which platform each term came from.
3. Group all converting terms into topic clusters by search intent. For each cluster show total cost, conversions and cost per conversion, split by platform where both are present.
4. Find my Search Console property (use [site property] if I named one) and pull organic data for the last 90 days with dimension query, plus a page,query pull so you can see which page ranks for each query. Use a high enough limit to cover the long tail.
5. Match each cluster to the Search Console queries that express the same intent. Use your judgement for close variants and wording differences, and say which queries you counted. Report organic clicks, impressions and average position per cluster, and the page that ranks best for it if there is one.

Classify each cluster:
- Content gap: converts in ads, organic average position worse than 10 or no organic impressions. I should build a page.
- Improve: converts in ads, organic position 4 to 10. Name the existing page that needs strengthening.
- Covered: organic position 1 to 3. Note it as a possible place to test lower ad spend later.

Deliver a table sorted by ad conversions, then the top 5 content-gap clusters with a suggested page title and URL slug for each.

Separately, list my Google Ads keywords (list_entities, entity keywords) and show converting Google search terms that are not yet keywords in the account. For Microsoft terms, just flag them; do not check or change Microsoft keywords. Do not add anything. If you think some Google terms should be added as exact match, tell me which ad group each one belongs in and run add_keywords with confirm=false so I see the exact preview. Wait for my approval before applying.

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Cluster the searches that find my Business Profile and fix my services

Clusters of the real Google Search and Maps queries behind your listing, compared with your services and categories, plus a previewed fix.

When to use itWhen your Business Profile gets views but you suspect your services and categories do not match what people really search for on Google and Maps.

For my Business Profile location [location name], pull the search terms people used on Google Search and Maps to find the listing over the last 3 months.

Cluster the terms into:
- brand searches
- service searches, with one cluster per service
- "near me" and neighborhood or district searches
- problem-based searches, such as a symptom or an issue the customer has

For each cluster show total impressions and the top 5 terms.

Then read my listing details: primary and additional categories, and the services list. Compare them with the clusters and tell me:
- service clusters with real demand that are missing from my services list
- services I list that get almost no searches
- whether an additional category would match a strong cluster. Search the category names to confirm they exist.

Draft the new service entries, with a name and a description of up to 300 characters for each, written around the words people actually search.

Show me the exact changes as a preview first. Do not update the listing until I approve.

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Build a seasonal content and ad calendar from keyword demand

A 12-month calendar showing when each keyword cluster peaks, when to publish content and when to raise or lower ad budgets.

When to use itWhen you want to time content and ad budgets around real seasonal demand instead of guessing when interest peaks.