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Key Responsibilities and Required Skills for Acquisition Analyst

💰 $ - $

AnalyticsMarketingGrowthProduct

🎯 Role Definition

The Acquisition Analyst is responsible for measuring, modeling, and optimizing new customer and user acquisition across paid and organic channels. This role partners tightly with growth marketing, product, analytics, and finance to build attribution frameworks, perform cohort and lifetime value (LTV) analyses, and recommend channel-level spend allocation to maximize return on ad spend (ROAS) and minimize customer acquisition cost (CAC). The Acquisition Analyst turns raw data into actionable insights, automates recurring reporting, designs and analyzes experiments, and creates scalable measurement systems to support the organization’s growth strategy.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst (Marketing/Performance)
  • Marketing Analyst or CRM Analyst
  • Business Intelligence Analyst focused on user/product metrics

Advancement To:

  • Senior Acquisition Analyst / Senior Growth Analyst
  • Growth Manager / Performance Marketing Manager
  • Data Scientist (Growth/Experimentation)
  • Head of Acquisition or Director of Growth

Lateral Moves:

  • Product Analytics
  • Marketing Analytics / Media Strategy
  • Revenue Operations / RevOps

Core Responsibilities

Primary Functions

  • Develop, maintain, and continuously improve end-to-end acquisition reporting dashboards (daily, weekly, monthly) using SQL, Looker, Tableau, or Power BI to track CAC, ROAS, LTV, retention curves, and funnel conversion metrics.
  • Build and operationalize multi-touch and incrementality attribution models that reconcile marketing spend, impressions, clicks, and downstream conversions across channels (paid search, social, display, affiliates, programmatic, email).
  • Conduct cohort analyses and LTV modeling to inform long-term investment decisions and forecast customer value by acquisition channel, campaign, geography, and creative.
  • Design, implement, and analyze A/B and multivariate tests for landing pages, ad creative, bidding strategies, and onboarding flows to optimize conversion rates and downstream monetization.
  • Partner with Paid Media and Channel Marketing teams to set measurable goals, define key performance indicators (KPIs), and recommend budget reallocation based on predictive models and scenario planning.
  • Produce granular channel-level and creative-level performance analyses to identify high-performing segments, audiences, and placements; recommend targeting and bid adjustments to improve efficiency.
  • Implement scalable ETL pipelines and data quality monitoring for acquisition datasets, ensuring clean joins between ad platform, attribution provider, product analytics (e.g., GA4, Mixpanel), and CRM systems.
  • Create predictive acquisition models (propensity to convert, propensity to churn) using logistic regression, gradient-boosted trees, or similar techniques and embed outputs into campaign targeting strategies.
  • Reconcile marketing platform reporting (Google Ads, Meta Ads, DSPs) with internal conversion data and revenue recognition to eliminate discrepancies and provide a single source of truth for stakeholders.
  • Produce executive-level summaries and data-driven narratives for weekly growth stand-ups and monthly business reviews, translating technical analyses into clear strategic recommendations.
  • Monitor and alert on anomalies in acquisition funnels, ad spend anomalies, or sudden shifts in audience behavior; perform root cause analysis and corrective actions.
  • Collaborate with finance to align acquisition metrics with unit economics, CAC payback period, and forecasting models for quarterly planning and long-range financial models.
  • Manage data instrumentation and event taxonomy for accurate tracking of acquisition touchpoints, campaign UTM consistency, and customer lifecycle events across web and mobile.
  • Evaluate new acquisition channels and vendor partnerships through pilots and hold-out tests, tracking lift, cost efficiency, and scalability to recommend go/no-go decisions.
  • Create automated pipelines to enrich first-party acquisition data (CRM, transactional) with third-party or modeled signals (demographics, intent) to improve targeting and measurement.
  • Conduct competitive and market analyses to benchmark CAC, conversion rates, and funnel efficiency against industry peers and inform strategic positioning.
  • Train cross-functional teams in interpreting acquisition metrics, attribution caveats, and how to use dashboards and self-serve tools for campaign optimization.
  • Standardize naming conventions, UTM policies, and tagging processes to ensure consistent measurement and reliable attribution across paid, owned, and earned media.
  • Collaborate with legal and privacy teams to implement consent-aware measurement solutions and ensure compliance with platform and regional data protection regulations.
  • Produce lifetime value segmentation and pricing sensitivity analyses to determine which cohorts warrant higher acquisition spend and which require retention or upsell focus.
  • Translate qualitative channel feedback (creative, landing experience) into hypotheses and quantitative tests; close the loop by implementing findings back into campaign execution.
  • Lead cross-functional post-mortems for underperforming campaigns to identify learnings, process improvements, and guardrails to prevent recurrence.
  • Maintain a prioritized backlog of analytics experiments, instrumentation requests, and reporting improvements aligned to business OKRs.

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.
  • Mentor junior analysts on acquisition analytics best practices, SQL, and experimental design.
  • Partner with customer success and product teams to surface activation and onboarding bottlenecks that impact acquisition ROI.
  • Document analytics processes, assumptions, and model logic in a central knowledge base for reproducibility.
  • Evaluate and recommend third-party analytics and attribution vendors (MMPs, CDPs) to augment internal capabilities.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL for data extraction, aggregation, window functions, and performance optimization across large datasets.
  • Proficiency with at least one scripting language for analysis and automation (Python or R); experience building ETL scripts or notebooks.
  • Experience with BI and dashboarding tools: Looker, Tableau, Power BI, or Google Data Studio.
  • Familiarity with product analytics platforms: Google Analytics (GA4), Mixpanel, Amplitude.
  • Knowledge of advertising platforms and measurement APIs: Google Ads, Meta Ads Manager, DV360, The Trade Desk, Amazon Ads.
  • Strong understanding of attribution methodologies: last-touch, multi-touch, algorithmic attribution, and experimental (incrementality/holdout) measurement techniques.
  • Experience building LTV and CAC models, cohort analysis, and retention/engagement curve modeling.
  • Statistical analysis skills including hypothesis testing, regression analysis, and A/B test design and interpretation.
  • Experience with data warehousing concepts and tools (BigQuery, Redshift, Snowflake) and familiarity with SQL-based analytics stacks.
  • Proficiency with spreadsheet modeling and visualization in Excel or Google Sheets for ad-hoc financial modeling and deck preparation.
  • Experience with instrumentation, event tracking, UTM governance, and analytics QA processes.
  • Familiarity with data privacy and consent frameworks (GDPR, CCPA) and their impact on measurement strategies.

Soft Skills

  • Strong business acumen with a growth-oriented mindset and the ability to translate analytics into actionable marketing strategies.
  • Excellent communication and storytelling: synthesize complex analyses into concise recommendations for senior leadership.
  • Collaborative and cross-functional: works effectively with marketing, product, engineering, and finance teams.
  • Detail-oriented with strong problem-solving skills and a bias toward data-driven decision making.
  • Project management skills: able to prioritize competing analytics requests and deliver on time.
  • Comfortable with ambiguity and iterative experimentation in fast-paced growth environments.
  • Influential: ability to challenge assumptions and persuade stakeholders with evidence-based insights.
  • Curiosity and continuous learning orientation toward new channels, tools, and measurement techniques.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master’s degree in Analytics, Data Science, Business Analytics, or MBA with quantitative coursework.
  • Relevant certifications: Google Analytics, SQL, Looker, or statistical/analytics bootcamps.

Relevant Fields of Study:

  • Economics
  • Statistics
  • Computer Science
  • Marketing Analytics
  • Data Science

Experience Requirements

Typical Experience Range:

  • 2–5 years in acquisition, growth analytics, marketing analytics, or related data roles for mid-level hire; 0–2 years for entry-level roles with strong internship experience.

Preferred:

  • 3+ years of hands-on experience with acquisition reporting, attribution modelling, A/B testing, and building dashboards supporting paid media teams.
  • Proven track record of optimizing CAC and improving ROAS through analytical insights and experimentation.
  • Experience working in ad-tech, e-commerce, marketplace, SaaS, mobile app, or subscription businesses is highly valued.
  • Demonstrated experience collaborating with marketers and engineers to deliver measurement solutions and scalable analytics pipelines.
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