Key Responsibilities and Required Skills for Head of Marketing Optimization

🎯 Role Definition

The Head of Marketing Optimization leads the strategy, execution and scaling of experimentation, personalization, attribution and channel optimization across paid and organic channels. This role owns the optimization playbook — designing and running rigorous A/B and multi-variant tests, building measurement frameworks, partnering with product/engineering and paid media teams, and translating data into prioritised optimization roadmaps that materially improve acquisition efficiency, conversion rates and customer lifetime value. The role reports into Head of Growth or Chief Marketing Officer and is accountable for measurable uplift in key marketing KPIs (CAC, CPA, ROAS, CR, LTV) through disciplined testing, analytics and cross-functional enablement.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Growth/Product Analyst
Director of Conversion Rate Optimization (CRO)
Head of Performance Marketing

Advancement To:

VP of Growth
Chief Marketing Officer (CMO)
Head of Product & Growth

Lateral Moves:

Head of Acquisition
Head of Personalization / Product Experimentation

Core Responsibilities

Primary Functions

Develop and own the end-to-end experimentation and optimization strategy (A/B, multivariate, personalization) that aligns marketing, product and growth objectives to drive sustainable improvements in conversion rates, revenue and unit economics.
Build and manage a high-performing optimization team (experimenters, data scientists, CRO specialists, analytics engineers), including hiring, mentoring, performance management and career development to scale capability.
Design and prioritize a test roadmap using a hypothesis-driven framework that balances impact, confidence and effort; maintain an experiment backlog aligned to business KPIs (CAC, LTV, ARPU, retention).
Lead design, execution and rigorous statistical analysis of A/B and multivariate tests across landing pages, funnels, creative, pricing, messaging and paid media tactics to validate improvements and avoid false positives.
Implement and maintain experimentation platforms and marketing technology stack (e.g., Optimizely, VWO, Adobe Target, internal platforms) and ensure robust governance, QA and test management processes.
Define and operationalize a measurement framework for marketing (multi-touch attribution, incrementality testing, holdouts) to determine true channel and tactic ROI.
Partner with paid media leads to optimize bidding strategies, creative testing, audience targeting, and channel mix to maximize ROAS and reduce CAC across paid search, social, display, and programmatic channels.
Collaborate with product, engineering and data teams to instrument events, metrics and data pipelines (analytics, data warehouse) necessary for experiment validation and long-term measurement.
Own the analytics and experimentation cadence: set targets, report results, codify learnings, and translate findings into prioritized implementation recommendations that scale.
Drive a culture of experimentation and data-driven decision making across marketing and product organizations through training, playbooks, and democratizing testing capabilities.
Conduct advanced analysis (cohort analysis, uplift modeling, survival analysis) to understand retention drivers, monetization paths and the long-term impact of marketing initiatives.
Lead personalization strategy across channels, using segmentation, propensity models and dynamic content to increase relevancy and conversion rates.
Partner with creative and content teams to implement rigorous creative testing frameworks (headline, CTA, imagery, video variants) and correlate creative performance to downstream metrics.
Establish governance for test scheduling, traffic allocation, feature flags and rollbacks to minimize user experience regressions and technical risk during experiments.
Ensure experiments are instrumented for both short-term and long-term outcomes (first-order conversion metrics and downstream revenue/retention metrics) using reliable attribution windows and event tracking.
Drive cross-functional stakeholder alignment by presenting experiment results, trade-offs and recommendations to senior leadership, product and engineering teams to secure buy-in and implementation.
Build scalable dashboards and automated reporting (GA4, Looker, Tableau, BigQuery) to monitor experiment health, channel performance and top-of-funnel to bottom-line impact in near real time.
Define and manage optimization budget and resource allocation to maximize return on investment in experimentation tools, third-party vendors and data infrastructure.
Lead incrementality and holdout tests (geo, time-based, audience holdouts) to isolate marketing impact from organic trends and seasonality.
Develop and enforce data quality, tagging and analytics standards to ensure experiment validity and reproducible results across platforms.
Translate complex statistical results into clear, actionable recommendations and playbooks for campaign owners and product teams to implement at-scale.

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.
Partner with legal and privacy teams to ensure experiments comply with GDPR, CCPA and other privacy regulations and to manage consented personalization.
Run monthly/quarterly learning reviews and maintain a central repository of experiment results, hypotheses and standardized test templates.
Facilitate cross-functional optimization councils (growth, product, CX, analytics) to identify and prioritize high-impact opportunities.
Manage relationships with external vendors (experimentation platforms, analytics consultancies, CRO agencies) and evaluate new tools to accelerate testing velocity and capabilities.

Required Skills & Competencies

Hard Skills (Technical)

Deep expertise in experimentation methodologies: A/B testing, multivariate testing, holdouts, uplift modeling and power/sample calculations.
Strong statistical and analytical skills: hypothesis testing, confidence intervals, regression, Bayesian approaches and causal inference to validate incrementality.
Proficiency with analytics and data tools: SQL, BigQuery, Redshift or Snowflake for data extraction and transformation.
Programming for analysis (Python or R) to run advanced analyses, build models and automate experiment reports.
Experience with experimentation and personalization platforms (Optimizely, VWO, Adobe Target, LaunchDarkly or equivalent).
Familiarity with web and app analytics platforms: GA4, Adobe Analytics, Amplitude, Mixpanel for event instrumentation and funnel analysis.
Dashboarding and BI skills: Looker, Tableau, Power BI or equivalent to create executive-ready reporting and dashboards.
Knowledge of marketing platforms and ad stacks: Google Ads, Meta Ads Manager, DV360, trade desks and programmatic tools for channel-level optimization.
Hands-on experience with attribution modeling and multi-touch attribution tools, and the ability to implement and interpret incrementality tests.
Understanding of tag management systems and instrumentation (Google Tag Manager, Tealium) and data layer best practices.
Experience working with data engineering teams to design event schemas, pipelines and QA processes for experiment data.
Familiarity with product analytics instrumentation, user tracking, identity stitching and privacy-preserving measurement techniques.

Soft Skills

Strong strategic thinking with the ability to translate business goals into experimentable hypotheses and measurable outcomes.
Exceptional communication and storytelling skills to present complex analytical results to non-technical stakeholders and senior leadership.
Demonstrated stakeholder management and influence; able to build consensus across product, engineering, creative and commercial teams.
Leadership and people management: hiring, coaching, developing and retaining cross-functional optimization talent.
High level of curiosity, experimentation mindset and comfort with ambiguity; thrives in iterative, test-and-learn environments.
Prioritization and project management: balance competing requests and drive high-impact experiments to completion.
Problem-solving mindset: pragmatic, data-informed decisions and bias toward execution.
Change management skills to operationalize new processes and scale optimization practices across an organization.
Attention to detail and rigour in experimental design, QA and result interpretation to avoid common pitfalls and false positives.
Customer-centric mindset with an ability to align optimization efforts to user needs, experience and long-term value.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Marketing, Business Administration or related quantitative field.

Preferred Education:

Master's degree or MBA with specialization in Analytics, Data Science, Marketing Science, or a related discipline.

Relevant Fields of Study:

Statistics, Data Science, Applied Mathematics
Marketing Science, Behavioral Economics
Computer Science, Engineering
Business Analytics, Econometrics

Experience Requirements

Typical Experience Range:

8–15+ years in marketing analytics, growth, CRO or experimentation roles.

Preferred:

10+ years of progressive experience in performance marketing, analytics or experimentation with at least 3–5 years in a leadership role managing cross-functional teams and scaling optimization programs.
Demonstrable track record of driving measurable business impact through experimentation, personalization and attribution across B2C or B2B digital channels.
Experience in high-velocity environments (startups, scale-ups, or high-growth product teams) and working directly with engineering and product organizations to implement experiment infrastructure.