Connect Youtube Analytics to Claude

Multi-Client Youtube Analytics Health Radar

Using the Catchr MCP, analyze Youtube Analytics for [List of Client Accounts] over [Reporting Period] against [Comparison Period]. For each account, review Views, Average View Duration, and Estimated Minutes Watched and break the results down by Country and Insight Traffic Source Type. Focus on content formats and themes that earn meaningful engagement. Rank accounts as Healthy, Watch, or Critical using [Client Targets or Historical Baseline], and show the evidence behind each status. Return a client-ready table with the account, main change, supporting fields, business implication, recommended next action, and one sentence for the next client meeting. Separate observed changes from possible explanations, respect the reporting grain, and do not claim causation when Youtube Analytics only shows correlation.

Client-Ready Youtube Analytics Performance Brief

Using the Catchr MCP, review Youtube Analytics for [Client Account] during [Reporting Period] versus [Comparison Period]. Review Estimated Minutes Watched, Subscribers Gained, and Estimated Revenue and break the results down by Subscribed Status and Device Type. Explain what improved, what declined, and which changes are material for content, audience, reach, engagement, and profile performance. Produce an executive summary, three evidence-backed wins, three risks, and a prioritized action plan with owner and review date. Include a compact evidence table that ties every conclusion to the returned fields. State any unavailable field, incompatible reporting level, or small-sample limitation before recommending action, and write the final recap in language an agency account manager can use with the client.

Youtube Analytics Opportunity and Risk Map

Using the Catchr MCP, compare Youtube Analytics across [List of Client Accounts] for [Period] using [Materiality Threshold] and [Client-Specific Targets]. For every account, review Estimated Revenue, Red Views, and Annotation Clickable Impressions and break the results down by Date and Video Recording Date. Investigate audience growth, reach quality, and profile actions and posting patterns, distribution gaps, and repeatable content opportunities. Build an opportunity-and-risk map ranked by business relevance, confidence, and urgency. For each finding, show the account, observed change, supporting fields, plausible explanations, first validation step, and recommended client action. Keep each client separate before creating a portfolio summary, and avoid applying one account's baseline, currency, attribution rule, or reporting definition to another.

Morning Youtube Analytics Priority Queue

Using the Catchr MCP, pull Youtube Analytics for [List of Client Accounts] over [Recent Period] and compare it with [Baseline Period]. For each client, review Annotation Clickable Impressions, Card Teaser Impressions, and Annotation Click Through Rate and break the results down by Channel Branding Country and Channel Country. Flag changes beyond [Alert Threshold], separate high-value issues from low-volume noise, and rank the work I should handle today. Return a queue with the client, issue, exact evidence, likely impact stated cautiously, first validation step, recommended action, and a short update I can send to the client. Do not merge accounts until each result has been checked against its own target, currency, timezone, and reporting grain.

Monthly Youtube Analytics Client Report

Using the Catchr MCP, create a monthly Youtube Analytics report for [Client Account] covering [Current Period] versus [Previous Period]. Review Annotation Click Through Rate, Card Impressions, and Videos Added To Playlists and break the results down by Country Code and Video Dislike Count. Explain the most important movement in content, audience, reach, engagement, and profile performance, including what stayed stable and what needs attention. Write a plain-English report with an executive summary, a concise evidence table, three wins, three concerns, and three actions for [Next Period]. Attach an owner and success measure to each action. State the latest available date and any missing fields or intervals so the client can distinguish measured facts from assumptions or incomplete coverage.

Youtube Analytics Optimization Backlog

Using the Catchr MCP, analyze Youtube Analytics for [Client Account] over [Lookback Period]. Review Videos Added To Playlists, Dislikes, and Likes and break the results down by Video Like Count and Video Comment Count. Identify the largest opportunities related to content formats and themes that earn meaningful engagement and posting patterns, distribution gaps, and repeatable content opportunities, then create an optimization backlog ranked by expected impact, effort, and confidence. For every task, include the observed evidence, a specific action, an owner, a due date, a success metric, and a guardrail that would stop the test. Use [Minimum Volume] and [Materiality Threshold] to avoid reacting to noise, and label any recommendation that requires a field or business definition not available in the returned dataset.

Youtube Analytics Content Format Performance Review

Using the Catchr MCP, analyze Youtube Analytics for [Account or Entity] over [Reporting Period] versus [Comparison Period]. Review Likes, Comments, and Shares and break the results down by Video View Count and Video Public Stats Viewable. Concentrate on content formats and themes that earn meaningful engagement. Identify the entities and periods driving the largest change, distinguish material movement from normal variation using [Minimum Volume] and [Business Target], and explain what the available fields can and cannot prove. Return a ranked findings table with evidence, business implication, confidence, and recommended next action, followed by a short decision summary. Keep calculations at a compatible reporting grain and do not invent a metric when Youtube Analytics exposes only a related signal.

Youtube Analytics Audience Growth and Engagement Map

Using the Catchr MCP, investigate Youtube Analytics for [Account or Entity] across [Period] with a focus on audience growth, reach quality, and profile actions. Review Shares, Average View Percentage, and Viewer Percentage and break the results down by Channel Related Playlists Likes and Channel Branding Moderate Comments. Compare relevant entities against [Target, Peer Group, or Historical Baseline], then locate the strongest opportunities and clearest deterioration. For every finding, show the returned values, comparison dimension, possible explanations, and next check required before action. Deliver a prioritized opportunity map with owner, action, expected signal of improvement, and review date. Treat associations as hypotheses unless the available Youtube Analytics fields establish a direct relationship.

Youtube Analytics Publishing Pattern Opportunity Scan

Using the Catchr MCP, review Youtube Analytics for [Account or Entity] over [Lookback Period] to assess posting patterns, distribution gaps, and repeatable content opportunities. Review Viewer Percentage, Card Click Rate, and Card Teaser Click Rate and break the results down by Channel View Count and Channel Video Count. Flag changes outside [Tolerance], rank them by materiality and confidence, and separate confirmed data observations from possible operational causes. Return an exception table with the affected entity, period, supporting fields, severity, owner, first validation step, and recommended response. End with three repeatable monitoring rules, including the threshold, minimum volume, and required field. Do not present an unusual value as proof of an error or business cause.

Youtube Analytics Data Quality Audit

Using the Catchr MCP, audit Youtube Analytics for [Account or All Connected Accounts] over [Period]. Review Card Teaser Click Rate, Ad Impressions, and Annotation Closable Impressions and break the results down by Video Caption and Video Content ACB Rating. Check completeness, unexpected nulls, duplicate identifiers at [Expected Grain], invalid values, incompatible aggregation levels, and date consistency where the returned fields support those tests. Return rule-level failure counts and a severity-ranked exception sample with account, field, observed value, expected condition, and reproducible validation step. Distinguish source-data issues from extraction or modeling issues, and do not infer uniqueness, join keys, or business meaning unless confirmed in [Data Contract or Source Documentation].

Youtube Analytics Freshness and Coverage Monitor

Using the Catchr MCP, monitor Youtube Analytics delivery for [Account or All Connected Accounts] across [Lookback Period]. Review Annotation Closable Impressions, Annotation Impressions, and Annotation Clicks and break the results down by Video Content AGCOM Rating and Video Content ANATEL Rating. Measure date coverage, latest available record, row or entity volume, missing intervals, and field population against [Expected Schedule] and [Freshness SLA]. Flag stale extraction, flatlined volume, schema coverage changes, and gaps beyond [Absolute and Relative Thresholds]. Return an account-level health summary plus an investigation table with first affected period, supporting fields, likely failure domain stated cautiously, owner, and next validation query. Separate genuine source inactivity from a pipeline issue whenever the evidence cannot distinguish them.

Youtube Analytics Drift and Reconciliation Review

Using the Catchr MCP, compare Youtube Analytics across [Current Window] and [Baseline Window] for [Account or All Connected Accounts]. Review Annotation Clicks, Card Clicks, and Card Teaser Clicks and break the results down by Video Content BBFC Rating and Video Content BFVC Rating. Profile distributions, null rates, distinct-value counts, volume, and material changes at the declared reporting grain. Reconcile related fields only when their definitions and aggregation levels are compatible. Flag abrupt shifts beyond [Drift Threshold], inconsistent totals, and values that violate [Business or Data Contract Rules]. Produce a reproducible alert table with evidence, severity, probable domain, and next check, followed by a concise assessment of which analyses may be unreliable until each issue is resolved.

Replace [variables] Facts ≠ hypotheses Action + follow-up metric

Connect Youtube Analytics to Claude

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How to connect Youtube Analytics to Claude ?

Authorize the Youtube Analytics account in Catchr

Add the Catchr MCP as a custom connector in Claude

Choose, adapt, and run a prompt.

A simple client question should not trigger an hour of data prep.

Pull another CSV

Rebuild the context

Summarize it by hand

Ask the account. Get to the explanation.

Prepare data before every question.

Connect once. Keep asking.

Twelve Youtube Analytics prompts for analyzing real account data.

Youtube Analytics to Claude FAQs

What Youtube Analytics data can Claude analyze through Catchr?

How granular can Youtube Analytics analysis be in Claude?

Can Claude compare Youtube Analytics performance across periods or accounts?

Can Claude investigate unusual changes in Youtube Analytics?

What types of prompts work best for Youtube Analytics analysis in Claude?

Can I combine Youtube Analytics with other data sources in Claude?

Can Claude compare multiple Youtube Analytics accounts or entities?

What do I need to connect Youtube Analytics to Claude with Catchr?

How recent is the Youtube Analytics data, and how much history can Claude analyze?

Can Claude change anything in Youtube Analytics through Catchr?

How much does it cost to connect marketing data to Claude with Catchr?

Can I try the Claude integration before choosing a plan?

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