Data Analyst at National Lottery Nigeria

JOB OVERVIEW:

• We are seeking a skilled and business-driven Data Analyst to join the Business Intelligence team at National Lottery Nigeria. This role is critical to unlocking customer insights, predicting possible churn, and driving data-backed decisions that improve customer engagement and revenue growth.
• The ideal candidate is not just technically sound, but also commercially minded – capable of bridging the gap between raw data and actionable business strategies. Your mission will be to own the story behind the numbers, providing insight into customer behaviour, churn triggers, player segmentation, and opportunities for retention, cross-selling, and lifetime value optimization.
• We are on a mission to transform how we use data to drive growth, player retention, and market leadership. We need someone who doesn’t just crunch numbers but brings clarity and direction when we act on them. If you’re passionate about turning data into growth, this role is for you.

KEY RESPONSIBILITIES:

• Business Intelligence Operations & Stakeholder Collaboration
• Translate stakeholder questions into data problems and convert data outputs into clear business narratives.
• Own data storytelling in weekly/monthly business reviews, highlighting wins, gaps, and strategic opportunities.
• Ensure reporting structures are accurate, real-time, and decision-ready.

Customer Value Management and Business Insight:

• Analyze customer data to cover growth drivers, behavioural patterns, and value gaps.
• Deliver actionable recommendations to increase player retention.
• Design and maintain dashboards that highlight key metrics on player activity, conversion, reactivation, and engagement trends.
• Utilize statistical methods and tools to analyse large datasets. Interpret trends, patterns, and anomalies, providing actionable insights for decision-making.
• Implement Extract, Transform, Load (ETL) processes to transform raw data into usable formats. Streamline and automate data workflows for efficiency.

Predictive & Advanced Analytics:

• Build predictive models to identify high-risk churn segments, likely-to-deposit users, and inactive-to-active transition opportunities.
• Segment customer base using RFM, cohort, or clustering analysis for targeted campaigns and promotions.
• Use predictive modelling/analysis to segment customers by their likelihood of engaging with specific products or promotions.

Strategic Business Support:

• Partner with Product, Marketing, and Customer Experience teams to optimise player journeys and improve funnel performance.
• Generate revenue-impact reports with clear business recommendations, not just observations.
• Perform A/B testing and impact analysis of new features or campaigns to evaluate success.