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Key Responsibilities and Required Skills for Marketing Performance Analyst

💰 $70,000 - $120,000

MarketingAnalyticsData Science

🎯 Role Definition

The Marketing Performance Analyst is responsible for turning marketing data into actionable insights that improve ROI across paid, owned, and earned channels. This role blends quantitative analytics, attribution expertise, experimental design, and storytelling to inform media planning, budget allocation, and campaign optimization. The ideal candidate partners with media, product, and finance teams to measure channel effectiveness, build scalable reporting, and develop measurement frameworks that enable continuous improvement and growth.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Digital Marketing Analyst / Digital Analyst
  • Marketing Data Analyst / Reporting Analyst
  • Paid Media Specialist / Media Analyst

Advancement To:

  • Senior Marketing Performance Analyst
  • Marketing Analytics Manager / Head of Marketing Analytics
  • Performance Marketing Director / Growth Analytics Lead

Lateral Moves:

  • Paid Media Manager / Programmatic Lead
  • Customer Insights Manager / Consumer Analytics
  • Product Analyst / Growth Product Manager

Core Responsibilities

Primary Functions

  • Design, develop, and maintain centralized performance dashboards (Looker, Tableau, Power BI, Data Studio) that aggregate cross-channel KPIs (CAC, LTV, ROAS, conversion rates) to provide daily/weekly/monthly visibility for marketing, finance, and executive stakeholders.
  • Own the end-to-end paid media reporting process: ingest campaign-level data from Google Ads, Meta, DV360, trade desks and affiliate platforms, normalize and reconcile with backend conversion events, and deliver a single source of truth for spend and performance.
  • Build and maintain robust SQL-based ETL pipelines, including data modeling and scheduled queries, to join marketing activity to CRM and product datasets for cohort-based performance analysis.
  • Implement and validate measurement instrumentation (GA4, server-side tracking, Google Tag Manager) to ensure accurate event capture and consistent attribution across web, app, and offline touchpoints.
  • Lead multi-touch attribution analysis and recommendation: evaluate last-click, linear, position-based, rules-based, and probabilistic models to advise on the most business-appropriate attribution framework.
  • Conduct Advanced analytics (regression, uplift modeling, propensity scoring) to quantify channel incremental lift and recommend budget allocation changes based on incremental ROI.
  • Develop and execute media mix modeling (MMM) to measure offline and online media impact, control for seasonality and promotions, and forecast budget scenarios to inform annual planning.
  • Run experimentation programs (A/B and holdout tests) to validate creative, channel, bid strategy and landing page hypotheses; translate statistical results into clear go/no-go recommendations.
  • Create predictive models to forecast campaign performance, customer acquisition costs, and LTV by channel using time-series and machine learning techniques (Python, R, or equivalent).
  • Optimize bidding and budget allocation by delivering prescriptive recommendations (e.g., reallocating incremental spend, adjusting bids by placement/time/geo) informed by real-time and historical performance patterns.
  • Reconcile marketing spend to financial reporting, work with finance on media accruals, and ensure consistency between marketing reporting and GAAP/management reporting.
  • Translate complex analyses into concise, compelling narratives and visualizations for senior leadership, demonstrating the impact of marketing tactics on revenue and customer metrics.
  • Define, document, and maintain marketing measurement playbooks, naming conventions, and KPI definitions to ensure organization-wide consistency and governance.
  • Partner with product and engineering teams to implement server-side measurement and user-level identifier stitching to improve cross-platform attribution and LTV measurement.
  • Troubleshoot data integrity issues, validate attribution windows, and resolve discrepancies between ad platforms and analytics systems through root-cause analysis and process improvements.
  • Manage ad-hoc analytical requests and provide rapid, high-quality answers to urgent business questions (e.g., campaign pause decisions, promo effectiveness, audience overlaps).
  • Segment users and customers by behavior, acquisition channel, and value to inform targeting, creative personalization, and retention strategies.
  • Lead audience measurement and overlap analysis for lookalike and retargeting strategies; provide guidance on audience build logic and performance expectations.
  • Audit third-party measurement and tracking vendors (e.g., conversion APIs, tags, pixels), coordinate fixes, and verify results of vendor-driven attribution or tracking solutions.
  • Establish SLA-driven reporting cadences with internal stakeholders and media agencies; ensure reports are actionable, timely, and aligned to business objectives.
  • Conduct competitive and market benchmarking to contextualize campaign performance and inform strategic shifts in channel mix or creative investment.
  • Maintain and improve marketing data governance, privacy compliance (GDPR, CCPA), and consent-aware measurement approaches, ensuring analysis respects user privacy choices.
  • Continuously identify opportunities to automate manual reporting tasks, reduce time-to-insight, and improve the scalability of marketing analytics processes.

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)

  • SQL (advanced query writing, window functions, performance tuning) — regularly used to build analytics-derived datasets.
  • Google Analytics / GA4 (event model, funnels, enhanced measurement, user-scoped metrics).
  • Digital ad platforms: Google Ads, Meta Ads Manager, DV360, The Trade Desk, Bing Ads — campaign setup, reporting, and optimization.
  • Tag management and measurement: Google Tag Manager, server-side tagging, Conversion API implementations and validation.
  • Data visualization tools: Looker/Looker Studio, Tableau, Power BI — dashboard design, charting best practices, performance optimization.
  • Programming for analytics: Python or R (pandas, scikit-learn, statsmodels) for ETL, modeling, and automation.
  • Attribution and measurement methods: multi-touch attribution, MMM, uplift/causal inference techniques, holdout testing frameworks.
  • Statistical methods: hypothesis testing, confidence intervals, regression analysis, A/B test power calculations.
  • ETL/process tools and cloud warehouses: BigQuery, Snowflake, Redshift, Airflow — data pipeline orchestration and optimization.
  • Excel & Google Sheets (advanced formulas, pivot tables, macros) for quick analysis and stakeholder-ready summaries.
  • CRM and backend systems familiarity (Salesforce, HubSpot, or equivalent) for join keys and LTV calculations.
  • Basic machine learning techniques for forecasting and predictive segmentation.
  • API integrations and automation (e.g., Ads APIs, Google Analytics API) to extract and refresh datasets programmatically.
  • Data governance and privacy-aware analytics (consent management, first-party data strategies, hashing/PII controls).

Soft Skills

  • Strong business acumen with the ability to link marketing activity to revenue and customer lifetime value.
  • Excellent communication and storytelling — translating complex analysis into clear, executive-ready recommendations.
  • Stakeholder management and cross-functional collaboration — ability to influence product, finance and agency partners.
  • Problem-solving and critical thinking — structured approach to ambiguous or incomplete data problems.
  • Attention to detail and data quality mindset — ensuring clean, validated inputs and outputs.
  • Prioritization and time management — balancing long-term measurement projects with rapid ad-hoc requests.
  • Curiosity and continuous learning — keeping current with evolving ad tech, analytics tools, and measurement methodologies.
  • Leadership and mentoring — able to coach junior analysts and lead cross-team measurement initiatives.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Marketing, Statistics, Economics, Mathematics, Computer Science, Data Science, or related quantitative field.

Preferred Education:

  • Master’s degree in Data Science, Analytics, Business Analytics, Statistics, or MBA with strong quantitative focus.

Relevant Fields of Study:

  • Marketing Analytics
  • Statistics / Applied Mathematics
  • Data Science / Computer Science
  • Economics / Business Analytics

Experience Requirements

Typical Experience Range: 2–6 years of hands-on experience in marketing analytics, digital analytics, or media measurement.

Preferred:

  • 3+ years working directly with paid media reporting and attribution.
  • Demonstrable experience building dashboards and automated pipelines using SQL and a BI tool.
  • Prior exposure to experimentation frameworks (A/B testing) and statistical inference.
  • Experience in an agency or in-house performance marketing environment and working with external media vendors/agencies.
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