'analytics' 검색 결과

Build Python text analysis with Azure AI Language to detect sentiment, names, key phrases, languages, and personal information. Examples also cover healthcare terminology extraction, combined analyses, authentication, and handling results for multiple documents.

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• Analyzing sentiment about specific aspects of a review
• Detecting and redacting personal information
• Extracting names and key phrases from text

Plan, implement, and audit website or app tracking for marketing and product decisions. Covers GA4, Google Tag Manager, event naming, campaign tracking parameters, and validation to check that important user actions are recorded correctly.

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• Plan tracking for signups and purchases
• Review GA4 and Google Tag Manager settings
• Standardize campaign tracking parameters

Design and audit analytics tracking so recorded actions support business decisions. Define events and conversions, investigate duplicate or missing records, and document tracking rules, traffic sources, consent checks, and validation steps.

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• Designing events around business decisions
• Investigating mismatched purchase counts
• Reviewing traffic source tracking

Design dbt data transformations with clearly organized models, data quality tests, documentation, and incremental processing that updates only part of a dataset. Use it to structure analytics tables and organize a dbt project.

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• Organizing a dbt project into model layers
• Adding data quality tests
• Designing incremental updates for large tables

Investigate Entra ID reconnaissance by searching Graph API logs for tool signatures and unusual directory queries. Use KQL, a log query language, to correlate suspicious requests with sign-ins and turn useful searches into Sentinel detection rules.

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• Investigating Graph API activity after suspected credential theft
• Checking whether offensive tool activity is visible in logs
• Creating and tuning scheduled Sentinel detection rules

Open the hosted context-mode Insight dashboard in your default browser to access analytics for AI-assisted engineering teams, including productive rates per engineer, retry waste, blockers, and views tailored to different roles.

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• Opening the team's development analytics dashboard
• Reviewing productive rates and retry waste per engineer
• Checking blockers and views tailored to team roles

Route product questions to a specialist for prioritization, research, analytics, experiments, or implementation. Support recurring discovery by tracking interviews and assumption tests, checking how customer needs connect to proposed solutions, and returning results with a concise digest.

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• Prioritizing features with an explicit framework
• Planning product experiments and sample sizes
• Tracking weekly interviews and assumption tests

Design a shared data platform across Google Cloud, AWS, and Azure for analytics and AI. The skill turns requirements into architecture diagrams, infrastructure code, deployment instructions, and validation checks using Apache Iceberg and BigQuery data agents.

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• Designing a data platform across multiple clouds
• Planning data ingestion and AI serving components
• Preparing infrastructure code and deployment instructions

Find the Wren CLI workflow for answering data questions, connecting databases or SaaS sources, generating model definitions, adding business metrics, or building a shareable analytics app. Guides are retrieved from the installed CLI version.

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• Set up Wren and connect a database
• Load HubSpot or Stripe data through dlt
• Generate model definitions and add business metrics

Publish a validated analytics report or dashboard on Sites and receive a shareable URL. The published data is a fixed snapshot, with access checks and optional creator-only editing of presentation text and layout.

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• Sharing a completed analytics report through Sites
• Publishing a dashboard snapshot
• Updating an existing report Site

Create and maintain a reusable skill that records metric definitions, trusted data sources, and analytical caveats. Preserve supporting evidence and unresolved conflicts so future Data Analytics work can consult and cite the same documented context.

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• Save shared metric definitions for future analyses
• Document trusted sources and conflicting definitions
• Update or repair an existing semantic layer

Query trading activity, capital flows, and positions across Polymarket, Hyperliquid, and Meteora. The skill covers wallet scoring, large trades, and liquidity pool analytics through 63 API endpoints, paid per request with USDC on Solana without API keys.

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• Tracking large trades and capital flows on Polymarket
• Comparing Hyperliquid traders and their trading results
• Examining Meteora pools and liquidity providers

Provides authoritative guidance on Amazon Neptune Database and Neptune Analytics for graph, knowledge-graph, and relationship-heavy workloads — fraud detection / fraud rings, agentic memory / chatbot context across sessions, recommendations, identity resolution, Gremlin / openCypher / SPARQL queries, supernode / slow traversal, Neo4j to Neptune migration / APOC compatibility, Neptune Database vs Analytics engine selection, PageRank / community detection, GraphRAG, and connectivity from Lambda / EC2 / applications. Creates and modifies Neptune Database clusters/instances and Neptune Analytics graphs on explicit user confirmation; blocks destructive operations (delete, reset-graph, failover, major upgrade) and redirects to change-control.

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Choose and audit startup metrics using Croll and Yoskovitz's "Lean Analytics". Use when the user mentions "what metrics should we track", "KPIs", "north star metric", "One Metric That Matters (OMTM)", "vanity metrics", "analytics dashboard", "DAU/MAU", "churn benchmark", or "measure product-market fit". Also trigger when choosing metrics for a startup or feature, auditing a dashboard for vanity metrics, setting metric targets and baselines, or instrumenting a product by business model and stage. Covers good-vs-vanity metrics, the One Metric That Matters, metrics by business model, the five startup stages, and benchmarks. For the build-measure-learn loop, see lean-startup. For fixing activation and retention, see improve-retention.

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When the user wants to monitor, triage, or reduce their app's crash rate — including setting up Crashlytics, prioritizing which crashes to fix first, interpreting crash data, and understanding how crashes affect App Store ranking. Use when the user mentions "crash", "crashlytics", "crash rate", "ANR", "app not responding", "crash-free sessions", "crash-free users", "symbolication", "stability", "firebase crashes", "app crashing", or "crash report". For overall analytics setup, see app-analytics.

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Use for VidSeeds MCP analytics and research - YouTube analytics, channel intelligence, video autopsy, outliers, competitors, trending/breakout discovery, keyword research, comment sentiment, and best-practices tools. Outcomes only; not internal scoring details.

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Collect, download, and verify App Store Connect Analytics reports with asc. Use when users need to discover analytics report requests, select report instances by processing date or granularity, download every segment, or verify downloaded files against Apple's size and MD5 metadata before analysis.

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When the user wants to set up, audit, or optimize analytics tracking (GA4, events, conversions). Also use when the user mentions "Google Analytics," "GA4," "event tracking," "conversions," "attribution model," "gtag," "data layer," "GA4 setup," "conversion tracking," "event setup," "User ID tracking," or "CTA attribution." For traffic insights, use traffic-analysis.

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End-to-end analytics implementation for web and SaaS: GA4, Google Tag Manager, event taxonomy, conversion tracking, and UTM strategy. Use when building a tracking plan, debugging missing events, setting up GTM, or auditing existing analytics.

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People analytics across workforce metrics, predictive modeling, and employee insights. Use when building turnover models, analyzing engagement surveys, running pay equity regressions, or scoring flight risk.

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Analytics engineering across data modeling, dbt, transformation, and semantic layers. Use when building dbt models, designing star schemas, writing staging or mart SQL, configuring data tests, or optimizing warehouse queries.

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Interpret learning analytics data and translate dashboard findings into actionable teaching decisions. Use when reviewing LMS data, quiz patterns, or engagement metrics.

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Generates protocol-based analytics infrastructure with swappable providers (TelemetryDeck, Firebase, Mixpanel). Use when user wants to add analytics, track events, or set up telemetry.

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Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising strategies.

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