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.
