Judge Keyword Viability Under AI Answers

Impressions can hold while clicks fall because the answer now sits above the results, so a plan built on volume keeps commissioning pages for queries that no longer send anyone anywhere. How much traffic is actually lost is genuinely disputed, and published figures vary widely by vertical and by who did the measuring, so the verdict has to come from your own data and a live check rather than from a headline statistic. The useful split is between queries a machine can finish and queries where the searcher still has to reach a page to act.

Le fichier de compétence

---
name: keyword-viability-under-ai-answers
description: Use when deciding whether a query still deserves a page now that AI answers and feature blocks sit above the results.
---

# Judge Keyword Viability Under AI Answers

Impressions can hold while clicks fall because the answer now sits above the results, so a plan built on volume keeps commissioning pages for queries that no longer send anyone anywhere. How much traffic is actually lost is genuinely disputed, and published figures vary widely by vertical and by who did the measuring, so the verdict has to come from your own data and a live check rather than from a headline statistic. The useful split is between queries a machine can finish and queries where the searcher still has to reach a page to act.

## Ce qu’il vous faut d’abord

- a candidate query list with the page type each would need
- live SERPs checked logged out in the target country, on mobile as well as desktop
- your own Search Console click-through history for comparable queries where an AI answer already appears

## Méthode

1. Check each candidate live and logged out and record three things: whether an AI answer appears, whether it cites sources, and how far the first organic result sits below it. Do this on mobile too, where the displacement is far larger.
2. Classify by answer completeness. A definition, a conversion, a date or a single fact can be finished in place; a decision that depends on price, availability, fit, personal circumstances or seeing the thing cannot.
3. For queries you already rank for, compare your click-through rate before and after the AI answer appeared on that query. Your own history is the only measurement that reflects your snippet, your vertical and your market.
4. Keep answerable queries only where being cited has value on its own, and write in the plan that the return is visibility rather than sessions, so nobody measures it as traffic six months later.
5. Prioritise queries the searcher has to complete on a site: pricing, availability, configuration, booking, and comparisons against constraints only they know. Those retain clicks because the answer cannot be finished in a paragraph.
6. Decide the measurement for each retained query before it is published - which number should move, and by when - because impressions and citations will both move without clicks following.
7. Re-check the affected queries quarterly. Feature rollout is uneven across countries and query types, and a term that was safe last quarter can lose its clicks with no ranking change at all.

## Ce que ça produit

A per-query verdict of keep, keep for citation only, or drop, with the SERP evidence and the date it was checked.

## Là où ça dérape

- Dropping a whole topic because one query in it shows an AI answer, when the commercial queries underneath are untouched.
- Using a published click-loss percentage instead of your own click-through history, when the spread across published studies is wider than the decision you are making.
- Assuming a citation brings traffic - citation frequency and click-through are separate things and need separate tracking.

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Extrait de la bibliothèque de compétences QuQi - https://www.quqi.io/fr/skills/keyword-viability-under-ai-answers

Ce qu’il vous faut d’abord

• a candidate query list with the page type each would need
• live SERPs checked logged out in the target country, on mobile as well as desktop
• your own Search Console click-through history for comparable queries where an AI answer already appears

Méthode

1. 01 Check each candidate live and logged out and record three things: whether an AI answer appears, whether it cites sources, and how far the first organic result sits below it. Do this on mobile too, where the displacement is far larger.
2. 02 Classify by answer completeness. A definition, a conversion, a date or a single fact can be finished in place; a decision that depends on price, availability, fit, personal circumstances or seeing the thing cannot.
3. 03 For queries you already rank for, compare your click-through rate before and after the AI answer appeared on that query. Your own history is the only measurement that reflects your snippet, your vertical and your market.
4. 04 Keep answerable queries only where being cited has value on its own, and write in the plan that the return is visibility rather than sessions, so nobody measures it as traffic six months later.
5. 05 Prioritise queries the searcher has to complete on a site: pricing, availability, configuration, booking, and comparisons against constraints only they know. Those retain clicks because the answer cannot be finished in a paragraph.
6. 06 Decide the measurement for each retained query before it is published - which number should move, and by when - because impressions and citations will both move without clicks following.
7. 07 Re-check the affected queries quarterly. Feature rollout is uneven across countries and query types, and a term that was safe last quarter can lose its clicks with no ranking change at all.

Ce que ça produit

A per-query verdict of keep, keep for citation only, or drop, with the SERP evidence and the date it was checked.

Là où ça dérape

• Dropping a whole topic because one query in it shows an AI answer, when the commercial queries underneath are untouched.
• Using a published click-loss percentage instead of your own click-through history, when the spread across published studies is wider than the decision you are making.
• Assuming a citation brings traffic - citation frequency and click-through are separate things and need separate tracking.

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