• No missing or duplicate dates were found in the supplied daily histories. Each search/AI comparison has 28 days.
• CTR is computed from summed clicks and impressions. The category counts total 3,040 not-indexed pages.
• Query and page exports contain 1,000 rows each. They are not guaranteed complete inventories. Segment tables cover their full report periods.
• Current page copy, example excluded URLs, a website change log, and conversions were not supplied. Missing information remains unverified.
• Source copies were read on November 2, 2026, at about 10:43 p.m. Eastern. These are historical report findings.

Calculation command: python3 native/wizard-runs/analyze_reports.py. Observed exit code: 0. The download package includes the evidence JSON, exact prompts, and calculation source.

This analysis follows the choices below. Codex analyzed the supplied report snapshots; Python recalculated the metrics. The HTML presents those completed findings. No separate ChatGPT/API run or website change was made.

Question: What could explain fewer clicks when search impressions increased?

Analyze the supplied Smash Meals website reports.

My question: What could explain fewer clicks when search impressions increased?

Focus and comparison: Compare September 6–October 3, 2026 with October 4–31, 2026. Each period has 28 days.

Source reports:
https://docs.google.com/spreadsheets/d/1_yz2qczhpLsWkSZ689fV3xyy9U1dUyQJ7OZT44oXad4/edit
Relevant tabs: Chart, Queries, Pages, Devices, Countries and Filters.

Use the daily Chart rows for period comparisons. Sum clicks and impressions; calculate CTR as summed clicks divided by summed impressions, never the average of daily CTRs. Show earlier, later, absolute change and percentage change. Express a CTR difference in percentage points. Use the full-period Queries, Pages and Devices tabs to identify investigation targets, not to claim which page caused a recent change. Do not invent page-by-date or query-to-page relationships. Consider search intent and whether the visible result answers the reader's need. No website change log or conversion data was supplied, so do not claim a particular edit caused an outcome or that clicks prove sales.

Recommendations: Recommend practical solutions in plain language. For each, explain the finding, why the proposed solution fits, the missing evidence, and what the business should check next.

Return a clear analysis with: (1) the question and source scope, (2) the evidence and any supported before/after comparison, (3) findings and possible explanations clearly separated, and (4) the recommended next steps with supporting evidence and what remains unknown.

Use the actual supplied reports. First identify the tabs and date coverage you can read. If you cannot access a report, ask me to attach it and do not invent its contents. Cite the report, tab, and row or range for every finding. Separate observed facts from possible explanations. Use plain language for a student learning website analysis. Produce the analysis now, including recommendations; do not stop at a plan for doing the analysis. Recommend only. Do not edit the website, publish content, or change any settings.

Arithmetic: python3 native/wizard-runs/analyze_reports.py. Observed exit code 0. Each comparison contains 28 daily rows. All three daily histories have no missing or duplicate dates. CTR = total clicks ÷ total impressions.

This analysis follows the choices below. Codex analyzed the supplied report snapshots; Python recalculated the metrics. The HTML presents those completed findings. No separate ChatGPT/API run or website change was made.

Question: How did indexed and not-indexed page counts change, and what should we check before drawing conclusions?

Analyze the supplied Smash Meals website reports.

My question: How did indexed and not-indexed page counts change, and what should we check before drawing conclusions?

Focus and comparison: Compare the August 4, 2026 and October 22, 2026 snapshots, then review the latest Critical issues counts.

Source reports:
https://docs.google.com/spreadsheets/d/1fRQfoHp3snoGP3ISd75HWsoMSaqZN-UUxkmtAIqAHRU/edit
Relevant tabs: Chart, Critical issues, Non-critical issues and Metadata.

Distinguish the number of indexed pages from search impressions. Explain the stated reasons and their counts. Do not treat the Critical issues sheet title as proof that every exclusion is harmful. Do not recommend removing all noindex tags, eliminating every redirect, or forcing every page into the index. Prioritize valuable pages with unintended exclusions after confirming URL intent. The export gives category totals rather than example URLs, response codes, current page content or Google-selected canonical details. State which checks would be needed for each proposed solution. A validation label alone does not prove a category has zero affected pages.

Recommendations: Recommend three priorities. For each, explain the evidence, the proposed solution, the benefit, what still needs checking, and how the business could measure the result.

Return a clear analysis with: (1) the question and source scope, (2) the evidence and any supported before/after comparison, (3) findings and possible explanations clearly separated, and (4) the recommended next steps with supporting evidence and what remains unknown.

Use the actual supplied reports. First identify the tabs and date coverage you can read. If you cannot access a report, ask me to attach it and do not invent its contents. Cite the report, tab, and row or range for every finding. Separate observed facts from possible explanations. Use plain language for a student learning website analysis. Produce the analysis now, including recommendations; do not stop at a plan for doing the analysis. Recommend only. Do not edit the website, publish content, or change any settings.

Arithmetic: python3 native/wizard-runs/analyze_reports.py. Observed exit code 0. Each comparison contains 28 daily rows. All three daily histories have no missing or duplicate dates. CTR = total clicks ÷ total impressions.

This analysis follows the choices below. Codex analyzed the supplied report snapshots; Python recalculated the metrics. The HTML presents those completed findings. No separate ChatGPT/API run or website change was made.

Question: What do AI-feature visibility and overall search performance tell us when we consider them together?

Analyze the supplied Smash Meals website reports.

My question: What do AI-feature visibility and overall search performance tell us when we consider them together?

Focus and comparison: Compare September 6–October 3, 2026 with October 4–31, 2026. Each period has 28 days.

Source reports:
https://docs.google.com/spreadsheets/d/1bZlIQBizt2RR4xrTiw4TX_qze5c70Tb78APuGBDOZEA/edit
https://docs.google.com/spreadsheets/d/1_yz2qczhpLsWkSZ689fV3xyy9U1dUyQJ7OZT44oXad4/edit
Relevant tabs: AI report: Chart, Pages, Countries, Devices and Filters. Search report: Chart for context..

This AI-feature export measures impressions only. Sum daily impressions for the requested periods and show earlier, later and percentage change. Do not invent AI clicks, CTR, conversions or sales. Do not add AI-feature impressions to total search impressions. The AI history begins May 18 even though Filters says Last 16 months; do not treat earlier missing dates as zeros. The Pages table covers the full report period, so use it for investigation targets rather than per-period growth claims. Identify which content topics and customer questions may merit review, but do not infer page quality solely from the URL. Recommend helpful content and source checks rather than promising AI rankings. Do not claim that AI-feature growth caused a search-click decline.

Recommendations: Recommend three priorities. For each, explain the evidence, the proposed solution, the benefit, what still needs checking, and how the business could measure the result.

Return a clear analysis with: (1) the question and source scope, (2) the evidence and any supported before/after comparison, (3) findings and possible explanations clearly separated, and (4) the recommended next steps with supporting evidence and what remains unknown.

Use the actual supplied reports. First identify the tabs and date coverage you can read. If you cannot access a report, ask me to attach it and do not invent its contents. Cite the report, tab, and row or range for every finding. Separate observed facts from possible explanations. Use plain language for a student learning website analysis. Produce the analysis now, including recommendations; do not stop at a plan for doing the analysis. Recommend only. Do not edit the website, publish content, or change any settings.

Arithmetic: python3 native/wizard-runs/analyze_reports.py. Observed exit code 0. Each comparison contains 28 daily rows. All three daily histories have no missing or duplicate dates. CTR = total clicks ÷ total impressions.

This analysis follows the choices below. Codex analyzed the supplied report snapshots; Python recalculated the metrics. The HTML presents those completed findings. No separate ChatGPT/API run or website change was made.

Question: Which customer questions in the reports could support useful new content without duplicating existing pages?

Analyze the supplied Smash Meals website reports.

My question: Which customer questions in the reports could support useful new content without duplicating existing pages?

Focus and comparison: Start with the gluten-free meal delivery query and the celiac-safe-meal-delivery page as separate investigation targets.

Source reports:
https://docs.google.com/spreadsheets/d/1_yz2qczhpLsWkSZ689fV3xyy9U1dUyQJ7OZT44oXad4/edit
https://docs.google.com/spreadsheets/d/1bZlIQBizt2RR4xrTiw4TX_qze5c70Tb78APuGBDOZEA/edit
Relevant tabs: Search: Queries, Pages and Filters. AI features: Pages and Filters..

Connect each observed query or page to a likely reader question and useful outcome. Treat intent as an interpretation, not a verified customer statement or measured keyword volume. Separate branded and non-branded search where supported. Evaluate whether to review an existing page before proposing a new article, and avoid near-duplicate topics. The Queries and Pages tables are separate aggregates, not a query-to-page join. They cover the whole report period and cannot show a page's before/after change. Do not invent current titles, page copy, medical assurances or conversion performance. For each recommendation show the observed evidence, the proposed content direction, and what page copy or additional data must be reviewed. If asked for a content before/after, label the proposed version as a recommendation and do not fabricate an original page or achieved result.

Recommendations: Create a short evidence-led content and SEO brief with priority, affected page or topic, reader need, proposed action, supporting report rows, and a future measurement.

Return a clear analysis with: (1) the question and source scope, (2) the evidence and any supported before/after comparison, (3) findings and possible explanations clearly separated, and (4) the recommended next steps with supporting evidence and what remains unknown.

Use the actual supplied reports. First identify the tabs and date coverage you can read. If you cannot access a report, ask me to attach it and do not invent its contents. Cite the report, tab, and row or range for every finding. Separate observed facts from possible explanations. Use plain language for a student learning website analysis. Produce the analysis now, including recommendations; do not stop at a plan for doing the analysis. Recommend only. Do not edit the website, publish content, or change any settings.

Arithmetic: python3 native/wizard-runs/analyze_reports.py. Observed exit code 0. Each comparison contains 28 daily rows. All three daily histories have no missing or duplicate dates. CTR = total clicks ÷ total impressions.