SEO and content

Did my SEO changes work? 28-day before and after check for each changed page

Compares clicks, impressions, CTR and position for each changed page 28 days before and after the change, removes the site-wide trend, confirms Google has recrawled the page, and says which changes to repeat or undo.

When to use it

When you changed titles, headings or content on some pages and want to know whether the change helped or hurt your organic search performance.

What you get

A before and after table per page (clicks, impressions, CTR, position) adjusted for the site-wide trend, with a verdict of improved, no change, worse or not recrawled yet, plus which changes to repeat or undo.

Does it change anything in my account?

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

Works with

Search Console, GA4

Fill in before sending

[site property][change date][target queries, optional][GA4 property]

I changed these pages on [site property] on [change date]: [list of URLs and what changed, e.g. new title, new H1, added FAQ, new internal links]. At least 28 full days must have passed since the change date before you judge the result. If they have not, tell me the earliest date I can run this. 1. For each URL, pull Search Console data for the 28 days before the change and the 28 days after it, at page level and at page,query level. Start both windows on the same day of the week, and check the period the tool returns for each call. 2. For each page, show clicks, impressions, CTR and average position before and after, and the change in each. 3. For the queries the change targeted [target queries, optional], show the same four numbers per query. If I gave none, use the five queries with the most impressions before the change. 4. To separate the effect of the change from overall demand, compare each page with the site total for the same two windows. A page that rose 20% while the whole site rose 20% did not really improve. 5. Use gsc_inspect_urls to check that Google has crawled each page since the change date. A page that has not been recrawled cannot show the effect yet. 6. If GA4 property [GA4 property] is connected, add organic sessions and key events for each page in both windows. Deliver a table per page with a one-word verdict: improved, no change, worse, or not recrawled yet. Call a page improved or worse only when its change is larger than the site-wide trend, and explain the threshold you used. Finish with the changes worth repeating on similar pages and the ones to undo. Read only. Do not change anything.

1. For each URL, pull Search Console data for the 28 days before the change and the 28 days after it, at page level and at page,query level. Start both windows on the same day of the week, and check the period the tool returns for each call.
2. For each page, show clicks, impressions, CTR and average position before and after, and the change in each.
3. For the queries the change targeted [target queries, optional], show the same four numbers per query. If I gave none, use the five queries with the most impressions before the change.
4. To separate the effect of the change from overall demand, compare each page with the site total for the same two windows. A page that rose 20% while the whole site rose 20% did not really improve.
5. Use gsc_inspect_urls to check that Google has crawled each page since the change date. A page that has not been recrawled cannot show the effect yet.
6. If GA4 property [GA4 property] is connected, add organic sessions and key events for each page in both windows.

Deliver a table per page with a one-word verdict: improved, no change, worse, or not recrawled yet. Call a page improved or worse only when its change is larger than the site-wide trend, and explain the threshold you used. Finish with the changes worth repeating on similar pages and the ones to undo.

Read only. Do not change anything.

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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.