Skill UI

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

This guide provides comprehensive patterns and best practices for designing high-impact Key Performance Indicator (KPI) dashboards. It covers everything from establishing a KPI framework (Strategic, Tactical, Operational) to defining departmental metrics (Sales, Marketing, Finance) and implementing best-practice layouts for executive summaries and SaaS metrics. Use this skill to translate raw data into actionable business insights.

A comprehensive tool for analyzing social media campaign performance. It calculates key metrics such as Engagement Rate, ROI, CTR, and provides industry benchmarks across major platforms (Instagram, Facebook, LinkedIn, etc.). Use this when auditing campaigns, optimizing ad spend, or measuring overall marketing effectiveness.

A comprehensive toolkit designed to track and analyze content performance, engagement metrics, and Return on Investment (ROI) across major social media platforms. By utilizing specialized Apify Actors, this skill extracts deep insights from Instagram, Facebook, YouTube, TikTok, and more, helping users identify high-performing content, monitor growth, and measure marketing campaign effectiveness.

This expert guides users through comprehensive customer research, whether analyzing existing assets (transcripts, surveys, tickets) or gathering new intel from online sources (Reddit, G2, forums). It helps uncover deep customer pains, desired outcomes, and underlying needs (Jobs to Be Done) to ground product strategy, marketing copy, and positioning in real-world evidence.

A comprehensive tool to scan all connected digital marketing platforms (e.g., Google Ads, Meta, TikTok) for statistically significant deviations from established performance baselines. It detects performance drops (traffic, conversions, ROAS) or unexpected growth opportunities, classifying anomalies by severity (Critical, Warning, Info) and providing probable root causes and immediate action plans. Essential for proactive campaign management and cost control.

This tool allows users to export comprehensive marketing data—including KPIs, contact records, campaign results, and performance snapshots—to external data stores. It supports integration with BigQuery for advanced data warehousing, Google Sheets for stakeholder collaboration, and Supabase for custom application development. The process includes mandatory PII handling, schema transformation, and detailed execution summaries to ensure compliance and data integrity for downstream analytics and reporting.

A comprehensive tool for importing structured data from various sources (CSV, JSON, Google Sheets) into connected platforms like CRMs, email marketing systems, or custom data stores. It manages the entire import pipeline, including automated field mapping, rigorous data validation, deduplication against existing records, and critical compliance verification (GDPR, CCPA). Features a mandatory execution gate for risk management.

This tool performs deep, multi-touch attribution analysis to calculate comprehensive Return on Investment (ROI), Return on Ad Spend (ROAS), and Customer Acquisition Cost (CAC) across all marketing channels. It supports various attribution models (e.g., linear, time-decay) and projects long-term value using LTV, providing data for budget optimization, executive reporting, and identifying efficiency gaps.

Utilize Monte Carlo simulation to model complex marketing scenarios and predict revenue outcomes with quantified probability distributions. This tool is essential for assessing downside risk, understanding potential upside, and validating the probability of hitting specific revenue targets when testing budget shifts, channel mix changes, or new campaign launches.

This guide details the rigorous standards for creating empirical tables and figures in academic marketing science (JAMS style). It covers building conceptual models, structural path coefficient tables, measurement validity checks, and creating highly actionable 'managerial exhibits' that translate statistical estimates into decision-making insights, ensuring all work is self-contained and adheres to APA guidelines.

A comprehensive methodology guide for designing rigorous multi-country or cross-cultural studies, essential for international marketing research. It covers critical steps such as country selection criteria, ensuring construct, translation, and sampling equivalence, and planning for measurement invariance (MGCFA). It guides researchers through advanced designs including cross-cultural experiments, multi-level estimation, and handling secondary export/entry-mode data.