quality-score-trend

name

quality-score-trend

description

Track each keyword's Google Ads Quality Score over time and flag the keywords whose QS is DECLINING — the quiet erosion that raises your CPC and drops your rank without any alert firing — then attribute the drop to the component responsible (Expected CTR, Ad Relevance, or Landing Page Experience). Use this skill whenever the user asks about Quality Score trend, QS dropping / falling over time, "why did my Quality Score go down", CPC creeping up for no obvious reason, keywords slipping in rank, or which component (expected CTR / ad relevance / landing page experience) is hurting a keyword — even if they don't say "Quality Score". This skill diagnoses WHERE QS is eroding and WHY (which component) ONLY; the ad-copy fix belongs to the complementary `rsa-strength-copy-diversity-audit` skill, the landing-page fix to `landing-page-cro-audit`, and the point-in-time keyword↔ad↔landing relevance snapshot to `keyword-ad-landing-alignment`.

Quality Score Trend

Goal (job-to-be-done)

Answer the question a media buyer only asks after the damage is done: which of my keywords are losing Quality Score, and what's dragging each one down? Quality Score sets your CPC and your ad rank, but Google never alerts you when it slips — a keyword can drift from QS 8 to QS 4 over a quarter, quietly paying more for a worse position the whole way, and you only notice when the spend looks off. This skill reads each keyword's historical QS across time buckets (weekly or monthly), classifies the direction (improving / stable / declining), and names the component — Expected CTR, Ad Relevance, or Landing Page Experience — that's pulling it down, so the fix can be routed to the right place.

• Who: media buyer / PPC manager / whoever owns account efficiency. When: a periodic QS health check, or the "is Quality Score eroding" item on the account-audit checklist.
• Decision it drives: which declining keywords to act on first (highest spend × steepest drop), and which fix to send them to — rewrite the ad, fix the landing page, or re-check relevance.
• The differentiator: it doesn't report today's QS chip (that's a point-in-time number anyone can read in the UI) — it reads the trajectory and attributes the decline to a component, so you fix the cause, not the symptom. A QS drop driven by Landing Page Experience is a different job than one driven by Ad Relevance, and this skill tells them apart.

Scope

• ✅ QS trend + component attribution over time — overall historical QS trended per keyword (and rolled up per ad group), the direction, and which of the three components is dragging it.
• ❌ The ad-copy fix (rewrite headlines, fix Ad Strength / pinning to lift Ad Relevance & Expected CTR) → complementary rsa-strength-copy-diversity-audit.
• ❌ The landing-page fix (page speed, message-match, mobile UX to lift Landing Page Experience) → landing-page-cro-audit.
• ❌ The point-in-time relevance snapshot (does keyword ↔ ad ↔ landing align right now?) → keyword-ad-landing-alignment. That is a still photo of relevance; THIS skill is the movie of QS over time.

Components (read these references as needed)

• Tools / data plan: references/tools.md — the historical-QS query (overall + 3 component buckets, segmented by date) and the historical-vs-live caveat.
• Framework / rubric: references/framework.md — the brain: building the QS series, the improving/stable/declining cut-offs, and the component-attribution logic that names the dragging component.
• Output schema: references/output.md — the JSON this skill emits.

Operate

Input: the reporting window, split into time buckets (weekly for a quarter, monthly for a year). Per keyword: metrics.historical_quality_score (the 1–10 overall) and the three component buckets — metrics.historical_search_predicted_ctr (Expected CTR), metrics.historical_creative_quality_score (Ad Relevance), metrics.historical_landing_page_quality_score (Landing Page Experience) — each segmented by segments.date, plus metrics.cost_micros and metrics.average_cpc to weight the finding by spend. See references/tools.md for the date-bucketing and the Porter-field / GAQL split.

Process: apply references/framework.md. For each keyword, order the buckets into a QS series; compare the recent value against its baseline and classify the direction against the cut-offs (gate on enough buckets and enough impressions — a 1-point wiggle on a thin keyword is noise, not a trend). For every keyword that's declining, walk the three component series and name the dragging component — the one that fell into (or deepened within) BELOW_AVERAGE over the same window the overall QS dropped. Weight the finding by the keyword's cost, so a QS slide on a high-spend keyword outranks the same slide on a $3 keyword. QS exists for Search keywords only (not Display / Shopping / Performance Max); a keyword with no impressions in a bucket has no QS for that bucket — skip the gap, don't read it as a zero.

Emit the JSON in references/output.md:

• synthesis — three strings: headline (the QS story — how many keywords are declining and the single steepest drop by spend), diagnosis (the account's dominant failing component — is the erosion mostly ad-side or landing-side?), action (the highest-impact keyword to fix, where / what / why, routed to the right fix skill).
• keywords[] — one per keyword: the QS series, qs_trend, the three component states + trends, the dragging_component, the spend-at-risk, a verdict, and the executable recommendation {where, what, why, route_to}.
• rollup — count of declining keywords, the biggest QS drops ranked by spend, and the dominant failing component across the account.

A renderer (the orchestrator's formats/*) turns this JSON into the human document. Emit pure data — no emojis, tables, markdown, or colors in the output.

Voice (don't copy the rules, link them): write every narrative line per ../../../_framework/writing.md — a question heading the data answers yes/no; the metric+delta carried as data, never spelled out in prose; first sentence answers the heading, then names the driver; one bridge line to the next section. Plain language for a non-technical owner ("your landing page is getting a worse grade from Google", not "post-click quality score decayed"), the technical term in parentheses.

Example (illustrative — NOT rules)

• Landing-side erosion: "insurance quote online" fell from QS 8 → QS 4 over the quarter while Ad Relevance held ABOVE_AVERAGE and Landing Page Experience slid AVERAGE → BELOW_AVERAGE. The dragging component is the landing page — route to landing-page-cro-audit, not a copy rewrite. It's the account's #1 finding because it spends the most of any declining keyword.
• Ad-side erosion: "cheap car insurance" drifted QS 7 → QS 5 as Expected CTR moved AVERAGE → BELOW_AVERAGE while the landing page stayed ABOVE_AVERAGE — the ad has gone stale, not the page. Route to rsa-strength-copy-diversity-audit.
• Stable, don't touch: "acme insurance" wiggled QS 9 → 8 → 9 with all three components ABOVE_AVERAGE — a 1-point integer wiggle inside a healthy keyword is not a trend; leave it.