Job description:
General Position Definition
• This role will drive analytics and insights for the Global CRM & Loyalty teams
• The ideal candidate is passionate about customer analytics - specifically CRM and Loyalty
• The candidate will have strong quantitative skills (like statistics, mathematics, machine learning) and has applied those skills in solving real world problems in marketing
• Incumbent is responsible for working on a range of technologies and tools collaborating directly with the marketing stakeholders & other partners
Purpose
• Leverage analytics, data and deep customer insights to identify and execute innovative programs that continually push CRM initiatives to the next level
• Build and maintain a test and learn roadmap (A/B Testing) and work to continually iterate and optimize CRM efforts in order to increase the effectiveness of all channels
• Measure impact of customer engagement initiatives across all channels (email, SMS, push notifications, direct mail, etc.) and optimize campaign spends
• Support development of the contact strategy with ad-hoc analysis and projects.
• Lead the design and development of CRM reporting dashboards; establish templates and processes for regularly sharing out CRM progress and results with key stakeholders
• Develop and evolve advanced segmentation strategies for targeted campaigns driving incremental revenues
Requirements:
Minimum 5-7 years of relevant experience in Marketing / Customer Analytics
Preferred experience in Retail or E-commerce managing CRM and Loyalty Data
Good interpersonal communication skills and influencing skills
Eagerness to learn and ability to work with limited supervision•
Strong experience in specialized analytics tools and technologies (including, but not limited to):
- Azure Databricks, Alteryx
- Power BI, Spotfire or other visualization tools
- Python, R
Working collaboratively across multiple sets of stakeholders – business SMEs, IT, Data teams, Analytics resources, etc. to deliver on project deliverables and tasks
Statistics / Mathematics: Data Quality Analysis, Data identification, Hypothesis testing, Univariate / Multivariate Analysis, Cluster Analysis, Classification/PCA, Factor Analysis, Linear Modeling, Affinity & Association, Time Series
Number of Vacancies: