Senior Analyst - Customer Health FTC)

We're the feel-good fashion brand making style accessible and fun for over 55 years, on our website, mobile app and over 300 stores in the UK.

By living our values - we play to win, customer obsessed, we are one and it starts with me - we deliver That New Look Feeling for our customers and each other.

Utilise advanced analytical techniques to understand customer behaviour, identify opportunities across acquisition, retention, loyalty and customer value, and turn data into recommendations that improve business decision-making.

• 40% staff discount plus friends & family discounts throughout the year
• Access to our reward platform for external discount and offers
• Virtual GP access for you and your children - it allows you to speak to a doctor at a time and date that suits you
• All employees are covered by our life assurance policy from day one
• Unlock extra leave with our buy more holiday scheme.
• Celebrate YOU! Enjoy an extra paid day off on your birthday each year
• Enhanced maternity, paternity and adoption leave, and shared parental leave (eligible after 2 years' service).
• Spread the cost of your commute with interest-free season ticket loans
• Do your bit for the environment and save money with our Cycle2
  
  Work scheme
• We're proud to partner with the Retail Trust and Fashion & Textile Children's Trust

• Data Mining: Use data mining techniques to combine multiple large customer, transaction, campaign and digital datasets into new data marts, analytical models and reusable insight assets.
• Descriptive Analytics: Interpret data and present findings to stakeholders in a clear and impactful manner to drive data-driven decision making. Deliver deep-dive customer insight and recommendations that explain customer performance and behavioural trends.
• Advanced Analytics: Apply statistical and analytical techniques such as segmentation, clustering, predictive modelling and campaign measurement. Working knowledge of data science techniques including random forest, k-means and linear regression.
• Optimisation: Collaborate with cross-functional teams to identify opportunities for optimisation. Support initiatives across customer acquisition, retention, loyalty, lifecycle and marketing performance.
• Collaborate: Support a given analytical principle and deliver an agreed analytics strategy. Create stakeholder-ready dashboards, reporting and insight packs while ensuring outputs are accurate, documented and governed.
• Development: Stay updated on industry trends and best practices in customer analytics, loyalty, CRM, marketing measurement and analytical techniques.
• Analytical mindset: Customer-focused mindset with the ability to think critically, challenge assumptions and solve complex business problems through data.
• Attention to detail: Ensure accuracy, consistency and reliability of analytical findings, maintaining high standards of quality and governance.
• Commercial curiosity: Demonstrate a strong interest in customer behaviour and how it impacts sales, loyalty, retention, profitability and long-term customer value.
• Clear communicator: Translate complex analytical concepts and findings into clear, impactful recommendations for both technical and non-technical stakeholders.
• Continuous learning: Proactively seek opportunities to expand analytical, technical and customer knowledge, staying up to date with emerging best practices.
• Strong Opinions Loosely Held: Be vocal and maintain your point of view while remaining open to new ideas, challenge and opposing perspectives.

• Proficiency in statistical analysis, customer analytics and data visualisation tools. Experience working with large customer, marketing, loyalty or digital datasets. (3+ years)
• Proven experience writing code in languages such as SQL, Python or R.
• Experience applying advanced analytical techniques including segmentation, regression analysis, clustering, predictive modelling and campaign measurement. Knowledge of data science and machine learning techniques such as random forest, k-means and linear regression.
• Strong communication, presentation and data storytelling skills, with the ability to translate complex analytical findings into clear and commercially relevant recommendations.
• Good understanding of customer profiling, customer value, customer lifecycle measurement and behavioural analytics.
• Experience creating stakeholder-ready dashboards, reporting solutions and insight packs using data visualisation tools.
• Knowledge of data…