Key Responsibilities:
Customer Insights & Segmentation:
Identify customer segments, analyze buying behaviors, demographics, and life stages.
Discover potential growth opportunities and develop targeted strategies for new customer segments.
Customer Lifecycle Analytics:
Extract insights for customer acquisition, engagement, retention, and reactivation strategies.
Collaborate with marketing and segment teams to develop campaigns that enhance revenue, product penetration, and engagement levels.
Optimization of Marketing Spend:
Evaluate customer lifetime value and optimize channel marketing spend to maximize ROI.
Implement optimization engines to target marketing offers effectively.
Customer Journey Mapping & Experience:
Map end-to-end customer purchase journeys and improve touchpoints using data analytics.
Drive initiatives to enhance the overall customer experience.
Data-Driven Transformation:
Advocate and lead efforts towards organizational change by implementing structured frameworks such as data governance models, KPI-driven performance tracking, and cross-functional analytics workshops.
Foster a customer-centric and data-driven decision-making culture by integrating data literacy programs and promoting the adoption of analytics in strategic planning..
Qualifications & Requirements:
Education & Experience:
Degree in Business, Statistics, Mathematics, Computer Science, or a related field, or equivalent experience.
Minimum of 5 years' experience in consultancy, market research, data analytics, or performance marketing, with exposure to large datasets and transaction-heavy platforms.
Prior experience in solving complex mathematical problems like optimization, dynamic pricing, or rank-ordering engines is preferred.
Technical Expertise:
Proficient in tools and platforms such as Google BigQuery, Python, Hadoop, Spark, HANA, Tableau, or similar.
Hands-on experience in optimization engines and targeting algorithms.
Retail & Digital Business Knowledge:
Familiarity with retail and e-commerce business models, including data architecture in these domains.
Key Competencies:
Strong problem-solving skills with a structured approach to tackling complex challenges.
Curiosity and passion for exploring and solving analytical problems.
Ability to manage ambiguity, work independently, and deliver high-quality results.
Excellent interpersonal, communication, and project management skills.
A collaborative mindset with the ability to work across functions and engage stakeholders effectively.
High-speed iterations and planned, well-organized data exploration.
Clarity of mind and clear plans for actions and contingencies.
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