Role: Marketing Analyst
Duration: 12 Months
Bonus: Company Plan
Salary range: £43,000 - £53,000Project Overview:
As a Marketing Analyst, you have the opportunity to shape the brand experience through quantitative and qualitative marketing insights. You will work closely with the marketing team to understand the impact on various activities on customer acquisition and retention and provide recommendations to improve it.
As a key member of the team, you will work with various cross functional teams to measure business performance and build actionable insights to grow the business.Overall Responsibilities:
- Conduct end-to-end analysis that includes specifying requirements, data collection and processing, analysis, presenting outcomes, and making business recommendations to multiple levels of stakeholders.
- Derive analytical insights on customer acquisition and retention, and use them to shape the Marketing strategy and drive the business growth.
- Regularly share campaign performance and optimisation recommendations with senior marketing leaders through custom and recurring reporting.
- Ability to understand the business implications of data and using it to build a strategic story that drives decisions across the Hardware Marketing organisation.
- Lead or partner cross-functionally to identify opportunities for improvement or growth.
- Define business metrics and develop automated reports and dashboards.
- Solid relevant work experience in marketing analytics, eCommerce product analytics or related fields.
- Experience with statistical programming (e.g., R, Python, etc.) and database languages (e.g., SQL)
- Excellent oral and written communication skills.
- Bachelor's degree in quantitative discipline (e.g., Statistics, Bioinformatics, Economics) or equivalent practical experience.
- Extensive relevant work experience in marketing analytics, eCommerce product analytics or related fields.
- Experience in analysing large data sets and applying statistical modeling to solve business problems and deliver tangible results.
- Experience with advanced explanatory and predictive quantitative models including time series, survival, Bayesian models, as well as Machine Learning (classification and clustering) models.
- Experience with dashboarding tools (e.g. Tableau).