Key Responsibilities and Required Skills for TV Analyst

🎯 Role Definition

The TV Analyst is responsible for delivering rigorous audience and campaign measurement insights across linear and streaming television channels. This role synthesizes viewing and ad delivery data (e.g., Nielsen, Comscore, set-top box, server-side logs), builds repeatable reporting and dashboards, performs advanced analytics (forecasting, attribution, uplift testing), and provides strategic recommendations to media planners, buyers, sales teams, and cross-functional stakeholders to optimize reach, frequency, and ad spend efficiency.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Media Analyst / Media Reporting Analyst
Marketing Analytics Associate
Research Analyst (Broadcast or Digital Media)

Advancement To:

Senior TV Analyst / Lead TV Measurement Analyst
Media Analytics Manager / TV Analytics Manager
Head of Audience Insights or Director of Media Measurement

Lateral Moves:

Cross-platform Media Analyst (TV + Digital/Streaming)
Audience Strategy or Programmatic Insights Specialist

Core Responsibilities

Primary Functions

Aggregate, cleanse, and merge multi-source television and streaming datasets (Nielsen, Comscore, set-top box, server logs, ad-server) to produce accurate audience metrics, reach & frequency curves, and GRP/imp delivery analyses for campaign verification.
Design, build, and maintain automated dashboards and executive-level reports (Tableau, Power BI, Looker) that visualize key KPIs including reach, frequency, impressions, average audience, rating points, tune-in trends, and CPM/CPP performance.
Conduct campaign post-buy and in-flight measurement, verifying delivered impressions and spots against planned buys, identifying under/over-delivery, and recommending makegoods or reallocation strategies to optimize campaign ROI.
Perform advanced audience segmentation and propensity modeling to profile target viewers, identify high-value audience cohorts, and advise media planning on efficient audience targeting across linear and streaming environments.
Lead cross-platform attribution analyses that quantify TV-driven incremental lifts in site traffic, conversions, app installs, or brand metrics using methodologies such as MMM, incremental testing, synthetic controls, and exposed/unexposed cohort comparisons.
Translate Nielsen/Comscore reach curves into media plans by modeling projected reach, frequency, and audience overlap across networks, dayparts, and program types to inform flighting and budget allocation decisions.
Create predictive forecasting models for viewership, GRPs, and ad delivery under different buy scenarios and seasonal patterns using time-series analysis and machine learning techniques.
Validate and reconcile ad-server logs and third-party delivery metrics to ensure billing integrity, detect discrepancies, and support finance and vendor reconciliation processes.
Execute A/B and uplift test designs for TV-driven activation and measure incremental outcomes on owned metrics (site visits, conversions, store visits), documenting methodology, randomization, and statistical significance.
Partner with media planning and buying teams to translate analytical findings into measurable optimization recommendations, including daypart shifts, network substitutions, and creative rotation.
Develop and document standard operating procedures (SOPs) for data ingestion, transformation, QA checks, and reporting cadence to ensure reproducibility and data governance.
Serve as primary analytics liaison to sales, providing customized audience insights, reach forecasts, and verification reports for proposals and client reporting.
Monitor industry measurement updates (Nielsen panels, Comscore methodology changes, streaming measurement standards) and incorporate best practices to keep measurement frameworks current and compliant.
Conduct competitive and programming trend analysis to identify emerging content opportunities, audience migration between linear and streaming, and seasonal viewing behavior impacts on campaign performance.
Build and maintain viewability and fraud detection processes for TV-ad-adjacent digital reporting, ensuring holistic ad-quality measurement when converging TV and digital addressable buys.
Create comprehensive campaign post-mortems and insights decks that synthesize quantitative results with qualitative recommendations for future buys and creative strategy.
Collaborate with engineering and data teams to define data schemas, ETL requirements, and schedule automated pipelines for timely reporting and near-real-time monitoring.
Provide hands-on training, documentation, and support to media teams on interpreting TV analytics dashboards, KPIs, and the practical implications of reach, frequency, and GRP metrics.
Facilitate stakeholder workshops to align measurement goals, define success metrics for TV campaigns, and set up tracking frameworks for brand and performance objectives.
Continuously iterate on measurement frameworks by incorporating cross-device identity solutions, deterministic panel enhancements, and probabilistic match methods to improve audience attribution fidelity.
Manage vendor relationships with measurement providers (Nielsen, Comscore, Dynata, Kantar) and ad-technology partners to troubleshoot data issues, request custom extracts, and secure measurement innovations.
Provide ad-hoc executive briefs and scenario analyses for leadership that quantify the business impact of TV investments on KPIs such as sales lift, awareness, and incremental reach.
Ensure compliance with privacy standards and data security when handling household- or device-level viewing data, including anonymization and access controls.
Evaluate and recommend new analytics tools, methodologies, and vendor solutions to enhance speed to insight and measurement accuracy within the TV analytics tech stack.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Advanced proficiency in SQL for data extraction, transformation, and complex cohort analysis across large audience tables.
Strong Excel skills including Power Query, pivot tables, advanced formulas, and VBA or macros for repeatable analyses.
Experience building actionable dashboards and visual analytics in Tableau, Power BI, or Looker with attention to performance and user experience.
Familiarity with TV measurement platforms and datasets such as Nielsen, Comscore, set-top-box (STB), ACR, server-side logs, and ad-server reporting.
Knowledge of statistical analysis and experimentation techniques: A/B testing, uplift modeling, time-series forecasting (ARIMA, Prophet), and regression analysis.
Experience with scripting and data science tools (Python or R) for data preparation, modeling, and automation of reporting workflows.
Understanding of media metrics and planning language: GRP, TRP, reach, frequency, CPP, CPM, impressions, and household ratings.
Hands-on experience with cross-platform attribution methodologies (MMM, multi-touch, incremental measurement) and familiarity with identity resolution approaches.
Experience with cloud data platforms (BigQuery, Redshift, Snowflake) and ETL orchestration (Airflow, dbt) to manage pipelines and data quality.
Familiarity with ad ops processes, ad-server (e.g., Google Ad Manager) logs, and troubleshooting delivery/creative discrepancies.

Soft Skills

Strong stakeholder management: ability to present technical findings clearly to non-technical clients and senior leadership.
Strategic thinking with an orientation toward business outcomes and media optimization.
Excellent written and verbal communication skills; experience preparing executive-level insights decks and recommendations.
Curiosity and intellectual rigor; deep attention to data quality, reproducibility, and methodological transparency.
Project management skills, ability to prioritize competing requests, and deliver under tight timelines.
Collaborative mindset: work effectively across media planning, sales, engineering, and product teams.
Problem-solving oriented, with a bias toward experimentation and continuous improvement.
Comfortable working in ambiguity and translating loosely defined business questions into measurable analyses.
Teaching and mentorship aptitude to upskill media teams on analytics literacy.
Ethical judgment and privacy-conscious approach when handling household and device-level data.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in a quantitative field such as Statistics, Economics, Mathematics, Data Science, Computer Science, or Marketing Analytics.

Preferred Education:

Master’s degree in Data Science, Applied Statistics, Marketing Science, Media Analytics, or Business Analytics.
Professional certifications in analytics, data visualization, or media measurement platforms.

Relevant Fields of Study:

Statistics / Applied Mathematics
Economics / Econometrics
Computer Science / Data Science
Marketing / Media Studies
Business Analytics

Experience Requirements

Typical Experience Range:

2–5 years for mid-level TV Analyst roles; 5+ years for senior or lead positions.

Preferred:

Demonstrated experience with television audience measurement (Nielsen/Comscore) and multi-source TV/streaming analytics.
Proven track record of driving media optimization and attribution analyses that materially improved campaign outcomes.
Prior experience in an agency, broadcaster, network, streaming platform, or advertiser analytics function is highly desirable.