Welcome to the Internal Recruitment Program

Role Profile:

The Customer Analytics Manager is the central owner of data, measurement, and advanced analytics across the loyalty ecosystem. This role is focused on generating high-quality insights, building scalable data models, and enabling data-driven decision-making, rather than owning campaign execution or program design.

Key Performance Areas include but are not limited to:

Data & Analytics Foundation

• Own the integrity, structure, and governance of customer and loyalty datasets across all markets
• Design and manage scalable data models for customer behavior, transactions, and engagement
• Partner with data engineering teams to improve data pipelines, tracking, and data availability

Measurement & KPI Frameworks

• Define and standardize loyalty and customer KPIs across acquisition, engagement, retention, and value
• Build automated dashboards and reporting frameworks to track performance across markets
• Ensure consistency and accuracy in all business reporting and executive insights

Advanced Analytics & Modeling

• Develop predictive models (churn, LTV, propensity, uplift) to support strategic decision-making
• Conduct deep-dive analyses to identify structural drivers of performance (not just campaign results)
• Apply statistical techniques and machine learning to solve complex business problems

Insight Generation (Strategic, Not Executional)

• Translate data into clear insights on customer behavior, program performance, and market trends
• Identify macro opportunities (e.g., frequency growth, retention levers, value perception gaps)
• Provide decision-support frameworks rather than campaign-level recommendations

Experimentation & Incrementality

• Design and govern experimentation frameworks (A/B testing, control groups, incrementality measurement)
• Validate the true impact of loyalty initiatives, promotions, and product changes

Data-Driven Culture Enablement

• Act as the analytics partner to loyalty, CRM, digital, and marketing teams
• Upskill stakeholders on data interpretation and KPI usage
• Drive adoption of data-led decision-making across the organization

Knowledge:

Holds a BA/BS in Business, Statistics or Mathematics (preferred), or Computer Science, Marketing or a related field.

• Expert knowledge of Google Analytics (Universal)
• Google accreditations preferred
• Expert knowledge in common analytics tools (Microsoft Excel, including Pivot Tables, Macros and VLOOKUPs,) and visualization tools (QlikView/Tableau or equivalents)
• Expert knowledge of lookalike modelling and datamining techniques
• Deep knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.)
• Expert knowledge of, and experience with a customer data platform (e.g. Exponea), Salesforce Marketing Cloud, or similar
• Familiarity with CRM systems
• Good understanding of the MarTech landscape and how it will evolve in the near future

Experience:

• Over 5 years of experience focused on customer analytics, ideally in a B2C retail business
• Experience of working in an omnichannel retail business is a valuable plus
• Track record of consistently delivering and optimizing routine business data, reporting and analysis (including management KPIs)
• Track record of completing ad-hoc/ bespoke analysis in support of business objectives
• Proven experience developing and implementing new processes and techniques for customer analytics is essential
• Experience with statistical computer languages (R, Python, etc.) preferred
• Experience in querying and manipulating datasets using SQL (advanced knowledge of SSQL required) or Python
• Solid experience and knowledge of technical and non-technical documentation best practices (Change Requests, Business Requirements, etc.) to support effective implementation
• Experience of creating reports and presentations to explore findings and make business recommendations
• Experience of managing primary research, via third-party agencies
• Experience working in a fast-paced and demanding environment, effectively interacting with many cross-functional teams