Marketing Analyst

What does a Marketing Analyst do?

Marketing Analysts turn data from ads, emails, search, social, and the website into clear recommendations that grow revenue efficiently. You connect channels to business outcomes, explain what is working, and guide where to invest next.

Day-to-day activities:

• Pull data from sources (ad platforms, web analytics, CRM) using spreadsheets, SQL, and BI tools.
• Define and track KPIs such as CAC, ROAS, CTR, conversion rate, LTV, and retention.
• Build dashboards that the team can trust and use.
• Run and evaluate A/B tests to improve conversion and ROI.
• Create attribution and cohort views to understand where value comes from over time.
• Communicate findings and recommended actions to marketing and product partners.

Typical deliverables:

• Weekly performance report with key insights and next steps.
• Channel performance dashboard (paid, organic, email, affiliates).
• A/B test plans, results readouts, and decisions.
• Attribution and LTV analyses used for budget allocation.
• Forecasts for traffic, leads, sales, or spend efficiency.

Where you can work

Marketing Analysts are needed across:

• Industries: e-commerce, SaaS, fintech, marketplaces, media, gaming, healthcare, education, B2B and B2C.
• Teams: Performance marketing, growth, lifecycle/CRM, product marketing, and central analytics.
• Company sizes: Startups (wear many hats), mid-size (channel owners + cross-functional), enterprises (domain specialization, advanced tooling).

Hiring expectations by level

• Junior/Associate: Comfortable with spreadsheets, basic SQL, clean charts, and channel metric definitions. Can maintain dashboards and run simple experiments with guidance.
• Mid-level: Owns reporting stack for multiple channels, drives test pipeline, builds attribution and cohort views, and communicates insights that change plans.
• Senior/Lead: Sets measurement strategy, ensures data quality, mentors others, partners with leadership on budget and forecasting, and aligns analytics with business goals.

Salary ranges

Approximate total compensation ranges:

• Junior: $45k–$70k
• Mid-level: $70k–$105k
• Senior: $100k–$150k+ (often higher with bonuses/equity)

Varies by country/company; treat as rough ranges.

Skill map you will build

• Spreadsheet Proficiency: Fast analysis, clean tables, error-free formulas.
• SQL: Querying warehouse tables for accurate, reproducible metrics.
• UTM Taxonomy: Clean campaign tracking from the start.
• Channel Metrics: Standard KPIs, normalization, and comparability.
• A/B Testing: Design, power, guardrails, and analysis.
• Attribution Models: Rule-based and data-driven trade-offs.
• Cohort and LTV Analysis: Retention curves and payback periods.
• Forecasting Basics: Practical models for pipeline and spend.
• Data Visualization: Clear charts for non-analysts.
• BI Dashboards: Source-of-truth reports used by teams.

Practical projects for your portfolio

• Channel Performance Dashboard: Build a multi-channel dashboard with CAC, ROAS, and conversion funnels. Outcome: A single source of truth and 2–3 actionable insights.
  • Mini task: Add a spend anomaly alert and explain the threshold logic in notes.
• Attribution Comparison Study: Compare last-click vs position-based vs data-driven proxy (e.g., time-decay rules). Outcome: Budget reallocation recommendation with quantified impact.
  • Mini task: Show a scenario table of how reallocating 10% spend changes ROAS.
• Cohort and LTV Analysis: Build monthly cohorts and estimate payback period. Outcome: Clear LTV curve and when CAC breaks even.
  • Mini task: Add sensitivity analysis for different discount rates or retention assumptions.
• A/B Test Readout: Design and analyze a test (e.g., landing page headline). Outcome: Decision memo with effect size, confidence, and next steps.
  • Mini task: Include a pre-registered hypothesis and minimal detectable effect calculation.
• Forecasting Spend and Conversions: Create a simple forecast with seasonality. Outcome: 90-day forecast with best/base/worst scenarios.
  • Mini task: Document assumptions and error bands.

Learning path

1. Foundations (1–2 weeks): Spreadsheets, channel metrics, UTM taxonomy. Practice cleaning data and calculating KPIs.
2. Querying Data (2–4 weeks): Learn SQL to join tables and aggregate by channel, campaign, and date.
3. Dashboards (1–2 weeks): Build a reliable BI dashboard; focus on definitions and refresh logic.
4. Experiments (1–2 weeks): A/B testing design, guardrails, and readouts.
5. Attribution & LTV (2–3 weeks): Compare models, build cohorts, and estimate payback.
6. Forecasting & Communication (1–2 weeks): Create simple forecasts and present insights that drive decisions.

Interview preparation checklist

• Explain CAC, ROAS, LTV, incrementality, and when each matters.
• Walk through an A/B test end-to-end (hypothesis, power, analysis, guardrails).
• Describe a time you changed budget allocation with data.
• Write a SQL query joining channel spend to conversions by week.
• Show a dashboard you built and how it improved decisions.
• Discuss pitfalls: attribution bias, seasonality, and selection effects.
• Practice a 5-minute insight briefing with a clear recommendation.

Common mistakes and how to avoid them

• Messy UTM tags: Define a simple taxonomy and enforce it; stop data drift early.
• Metric soup: Publish a KPI glossary and use consistent definitions.
• Overfitting attribution: Compare models and look for stable decisions across them.
• Underpowered tests: Estimate sample size before launching and set guardrails.
• Pretty dashboards, weak actions: Always end with 2–3 concrete recommendations.
• No data QA: Schedule checks for freshness, outliers, and duplicates.

Next steps

Pick a skill to start in the Skills section below and complete one mini project this week.