5+ years of Data / BI experience with a diverse background in Retail, E-commerce, and Finance. Skilled in creating reporting structures and transforming data into actionable insights. Proficient in Excel, SQL, Python, an…
5+ years of Data / BI experience with a diverse background in Retail, E-commerce, and Finance. Skilled in creating reporting structures and transforming data into actionable insights. Proficient in Excel, SQL, Python, and Power BI for analysis and visualisation. Proven track record of driving revenue growth, cost optimisation, and operational efficiency.
Led the Data division of the E-commerce (D to C) business Slurp.
Conducted a customer lifetime value analysis, enabling targeted marketing strategies.
Achieved a 4% increase in 2nd shop-rate post reallocated budgeting.
Conducted Basket Analysis to pinpoint Loss-leader, Loss-lagger, and Hero products, enabling the Marketing team to change promotional strategies and resulting in a revenue increase of £100K.
Performed competitor analysis, revealing subscription model potential.
Facilitated setup of wine subscription, yielding £70K revenue in 6 months.
Utilised Python code to automate data cleaning and transformation processes, eliminating manual intervention.
Saved 1 hour per day.
Increased targeted marketing effectiveness by 35% by developing customer segmentation models using Azure Databricks and Power BI.
Created interactive Power BI dashboards, enhancing data accessibility.
Built a comprehensive Data Dictionary and reporting infrastructure from the group up improving business efficiency by 50%.
As a Sr Business Data Analyst for Freixenet Copestick's (FXC) E-commerce business Slurp -
Conducted a customer lifetime value analysis, enabling targeted marketing strategies. Achieved a 4% increase in 2nd shop-rate post reallocated budgeting.
•
Conducted Basket Analysis to pinpoint Loss-leader, Loss-lagger, and Hero products, enabling the Marketing team to change promotional strategies and resulting in a revenue increase of £100K.
•
Performed competitor analysis, revealing subscription model potential. Facilitated setup of wine subscription, yielding £70K revenue in 6 months.
•
Utilised Python code to automate data cleaning and transformation processes, eliminating manual intervention. Saved 1 hour per day.
•
Increased targeted marketing effectiveness by 35% by developing customer segmentation models using Azure Databricks and Power BI.
•
Created interactive Power BI dashboards, enhancing data accessibility.
•
Built a comprehensive Data Dictionary and reporting infrastructure from the group up, Improving business efficiency by 50%.
Conducted detailed customer analysis and built a Power BI dashboard, identifying targeting opportunities that led to a 10% increase in market penetration for key products and segments.
Automated ETL tasks using the Panoply cloud data platform, saving 15+ hours of manual work per week.
Implemented stored procedures and views to streamline data retrieval and improve query runtime by 30%.
Utilised SQL queries to extract, manipulate, and summarise data from databases; handled random samples and aggregated data for 20+ analytical reports.
Managed an average of 10 ad hoc requests per week without missing a deadline.
Developed Excel dashboard resulting in a 50% increase in trend visibility.
Conducted trend analysis across streams, identifying Fail patterns and reasons leading to a 25% improvement in departmental quality.
Data extrapolation, consolidation, and cleaning using advanced Excel functions saving 7+ hours of manual work per week.
Worked with some of the biggest names in the UK banking sector including Barclays, Lloyds, Santander, Clydesdale and Yorkshire Bank across different sites as a Business analyst.
Designed a process checklist to review before submitting cases.
Trained and supported the external auditors on internal policies improving the quality to hit 95% SLA target.
Created and maintained project documentation, including business requirement documents, process flows, and user manuals.
Preparing high-level reports highlighting quality concerns and recommending appropriate action plans intended to reduce the number of outcome errors.
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