Manager- Commerce Analytics

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Manager - Customer/ Marketing/ Commerce Analytics

Customer| Marketing, Sales & Service Practice

Practice: Customer

About Us: The Customer team consults Front Office Business Transformation for EYs global clients covering Sales, Marketing, Customer Service & Pricing pillars. Customer team leverages both Digital Transformation & Customer Experience domains to drive transformations for clients.

Role Summary

Manager- Customer, Marketing/ Commerce Analytics, will lead data-driven transformation initiatives that help clients strengthen customer engagement, optimize marketing performance, and elevate digital commerce outcomes. The Manager will be responsible for designing advanced analytics solutions, delivering meaningful insights, and guiding clients in integrating analytics into their commercial decision-making processes. This role requires strong consulting acumen, deep analytical expertise, and the ability to collaborate effectively with senior stakeholders across business and technology functions.

Key Responsibilities

• Analytics Strategy & Execution
• Develop and implement analytics strategies across customer, marketing, and commerce domains to support client growth objectives.
• Lead end-to-end delivery of analytics engagements- including requirements gathering, solution design, modelling, testing, and deployment.
• Drive customer analytics initiatives such as segmentation, behavioural analysis, lifetime value modelling, and personalization.
• Design and optimize marketing analytics frameworks, including attribution models, performance dashboards, and ROI measurement.
• Conduct commerce analytics to enhance conversion, pricing effectiveness, and product performance across digital channels.
• Advanced Analytics & Insights
  
  
Apply predictive modelling, statistical techniques, and machine learning to generate actionable insights.

Ensure analytical rigor through robust data validation, model governance, and documentation.

Synthesize complex analytics into clear business recommendations and present insights to senior client leadership.

• Client Collaboration & Stakeholder Engagement
• Partner with cross functionals teams to integrate analytics into decision-making processes.
• Serve as a trusted advisor to clients, driving alignment between analytical solutions and business objectives.
• Manage stakeholder expectations and facilitate workshops, presentations, and executive discussions.
  
  

• Team Leadership & Practice Development
• Lead and mentor teams of analysts, data scientists, and consultants to ensure high-quality delivery and capability development.
• Contribute to the development of accelerators, frameworks, methodologies, and best practices within the practice.
• Support business development activities, including proposal development, solution design, and thought leadership creation.
  

Required Skills & Qualifications

• MBA or master’s degree in Analytics, Data Science, Statistics, or a related field.
• 10+ years of experience in customer, marketing, or commerce analytics, preferably within a consulting environment
• Proficiency in SQL and one or more analytics/programming languages (Python, R, SAS).
• Expertise in BI and data visualization tools (Tableau, Power BI, Qlik).
• Knowledge of marketing analytics platforms (Google Analytics, Adobe Analytics, CDPs) and ecommerce/commerce analytics tools
• Knowledge of cloud-based analytics ecosystems (AWS, Azure, GCP) and modern data architectures.
  
  

Key Competencies

• Strong knowledge of statistical modelling, predictive analytics, and machine learning techniques.
• Ability to translate data into actionable insights and strategic recommendations.
• Excellent communication, presentation, and data storytelling capabilities.
• Strong stakeholder management and client-facing skills.
• Demonstrated leadership ability, with experience managing teams and complex engagements.
  
  

Preferred Qualifications

• Experience working with large-scale customer, marketing, or digital commerce clients.
• Knowledge of AI/ML applications such as recommendation engines, propensity models, and marketing mix modelling.
  
  

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