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Milan Laser Hair Removal · 3 weeks ago

Director of Marketing Analytics & Insights

Omaha Metropolitan Area

Full-time

Onsite

Lead/Staff, Director/Executive

5+ years exp

Milan Laser Hair Removal is a leader in laser hair removal with over 400 locations nationwide. They are seeking a Director of Marketing Analytics to drive data strategy and measurement excellence, ensuring that marketing investments are accurately measured and optimized. This role will be pivotal in building analytics capabilities and providing insights that inform marketing decisions.

BeautyHealth CareLaser

Comp. & Benefits

No H1B

Responsibilities

Own and lead the testing roadmap for all marketing channels by partnering with the VP of Growth and Brand to build structured A/B testing and incrementality testing programs across channels (paid search, paid social, creative, landing pages, CRM, etc.) to optimize performance

Partner with channel owners (Paid Search, Paid Social, Brand, Creative) to design experiments, analyze results, and translate findings into strategic recommendations and scaled optimizations

Use holdout testing, geo experiments/matched market tests to measure incremental impact of marketing investments and validate attribution models

Create feedback loops that translate test learnings into optimization roadmaps, budget recommendations, and strategic insights for leadership

Define and evolve Milan's marketing measurement framework, ensuring accurate tracking, attribution, and reporting across all channels, campaigns, and customer touchpoints

Lead the development and implementation of measurement triangulation that accurately credits marketing channels throughout the customer journey, from awareness through conversion

Develop flag for tracking implementation issues across digital and offline channels to ensure data accuracy, completeness, and reliability from proper tagging, pixel management, and quality assurance processes

Partner with cross-functional teams, especially IT, Operations, and Finance, to ensure marketing data is integrated with business systems (CRM, scheduling, revenue)

Lead daily, weekly, monthly insights for marketing

Act as the objective insight lead of all channel reporting data across paid and owned (CRM, organic) marketing channels

Partner to build automated reporting that surface key performance metrics (traffic, leads, appointments, conversions, CAC, ROAS, LTV) across channels, campaigns, markets, and time periods

Design self-service analytics reporting and partner with IT and Finance to implement those reports that empower marketers to access data and extract insights independently while supporting data governance and accuracy standards

Stay on top of analytics platform updates, new measurement technologies, and privacy regulations (iOS updates, cookie deprecation, consent management) to ensure Milan's measurement capabilities remain robust and compliant

Develop analytics documentation and reports that keep stakeholders informed and ensure data accuracy across the marketing organization

Partner with the VP of Brand to develop deep customer and marketing insights that inform strategy, planning, and optimization across the marketing organization

Partner with the Manager of Conversion Rate Optimization (CRO) to analyze customer journey data to understand conversion paths, channel interaction effects, and opportunities to improve the path to purchase

Build predictive models for customer behavior (conversion probability, lifetime value, churn risk) that enable smarter targeting, budget allocation, and retention strategies

Conduct market-level analysis to identify growth opportunities, competitive dynamics, and efficiency variations across Milan's 400+ locations and leverage that into localized strategies

Use cohort analysis, customer segmentation, and funnel analysis to uncover trends, diagnose performance issues, and identify optimization opportunities

Translate complex data into clear, actionable insights and recommendations for marketing leadership, executives, and cross-functional partners

Partner with marketing and finance leadership to develop data-driven budget allocation strategies that maximize return on investment across channels, markets, and campaigns

Build forecasting models to predict marketing performance, plan budget needs, and scenario-plan growth strategies based on different investment levels

Monitor marketing efficiency metrics (CAC, ROAS, payback period, contribution margin) and provide ongoing recommendations for budget reallocation and optimization

Partner with Finance to ensure marketing performance data aligns with financial reporting and supports accurate revenue forecasting and business planning

Qualification

Marketing AnalyticsAttribution ModelsData Visualization ToolsStatistical ConceptsCRO Testing PlatformsDigital Marketing ChannelsData Engineering ConceptsPredictive AnalyticsLeadershipCommunication SkillsProblem Solving

Required

High school education or equivalent (e.g., GED) required

Bachelor's degree in Statistics, Mathematics, Economics, Data Science, Marketing Analytics, or related quantitative field OR equivalent combination of education/experience combined (one year of experience equals one year of education) required

Minimum of five years of experience in marketing analytics, data science, business intelligence or FP&A

Proven success leading and implementing attribution models, measurement frameworks, and analytics infrastructure for marketing organizations required

Deep expertise in marketing analytics platforms (Google Analytics/GA4, Adobe Analytics) and data visualization tools (Tableau, Looker, Power BI) required

Strong track record designing and analyzing marketing experiments (A/B tests, incrementality tests, geo experiments) with rigorous statistical methodology required

Fluency in statistical concepts (hypothesis testing, regression analysis, confidence intervals, experimental design) and ability to apply them to marketing problems required

Deep understanding of digital marketing channels, customer journey dynamics, and marketing performance metrics (CAC, ROAS, LTV, conversion rates) required

Comfort with data engineering concepts, ETL processes, and working with large datasets across multiple systems and platforms required

Demonstrated ability to translate complex analytical findings into clear, actionable recommendations for non-technical stakeholders required