Product Data Analyst

Roles and responsibilities

The Retail Data Analyst who will be responsible for analysing and interpreting key data sets related to the mall's business performance, with a specific focus on tenant sales, footfall metrics, and customer demographics.

This role is critical in providing actionable insights to optimize the tenant mix, enhance customer experience, and improve overall mall profitability.

What you will do:

• Tenant Sales Analysis: analyze, and interpret sales data from all retail tenants to identify high-performing stores and those underperforming. Provide actionable recommendations for optimizing the tenant mix based on performance metrics.
• Gap Analysis: Identify gaps in the current tenant mix by analyzing sales performance, footfall data, and customer preferences. Recommend potential new tenants or categories to improve the overall mall offering.
• Footfall Metrics: Monitor and evaluate footfall data to understand traffic patterns, peak shopping hours, and areas of high or low traffic within the mall. Use these insights to suggest strategies for improving footfall in underperforming areas.
• Customer Demographics & Behavior: Analyze customer data to understand demographics, spending habits, and shopping behaviours. Provide insights on how tenant mix and mall offerings can be tailored to better meet customer needs.
• Reporting & Dashboards: Develop and maintain comprehensive reports and visual dashboards to communicate findings on tenant performance, footfall, and customer behaviour to mall management and key stakeholders.
• Strategic Recommendations: Collaborate with leasing and marketing teams to develop strategies for attracting and retaining high-performing tenants. Provide data-driven recommendations on lease renewals, promotions, and tenant placement.
• Benchmarking: Compare tenant performance against industry benchmarks and similar malls. Identify areas of improvement and potential competitive advantages.
• Cross-Promotion Effectiveness: Analyse the effectiveness of cross-promotional activities among tenants and mall-wide marketing initiatives. Suggest ways to enhance collaboration between tenants to drive sales.
• Loyalty Program Insights: Evaluate the impact of loyalty programs (e.g., Blue Rewards) on tenant sales and customer retention. Provide recommendations for program improvements based on data analysis.
• Ad-Hoc Analysis: Conduct specific analysis projects as needed to support the strategic goals of the mall management team.
  

Required skills to be successful:

Behavioural Competencies :

• Analytical Thinking
• Data-Driven Decision Making
• Communication and Presentation Skills
• Strategic Planning
• Attention to Detail
• Collaborative Teamwork
  

Technical Competencies :

• Proficiency in data analysis tools (e.g., Excel, SQL, Python, R) and data visualization platforms (e.g., Tableau, Power BI).
  

What Equips you for the role:

Minimum Qualifications and Knowledge:

• Bachelor's degree in data science, Business Analytics, Statistics, or a related field. A master's degree is advantageous.
• Minimum of 5 to 8 years of experience in retail data analysis, particularly in Mall or large retail environment.

Desired candidate profile

1. Data Collection and Cleaning

• Data Collection: Collecting data from various sources, such as databases, spreadsheets, APIs, web sing, and external datasets.
• Data Cleaning and Preprocessing: Identifying and handling missing, inconsistent, or erroneous data. This includes standardizing formats, removing duplicates, and transforming data to make it usable.
• Data Quality: Ensuring data accuracy, completeness, and consistency before conducting analysis.

2. Statistical Analysis and Interpretation

• Statistical Methods: Applying statistical techniques (e.g., regression analysis, hypothesis testing, correlation analysis, and ANOVA) to interpret data and identify trends and patterns.
• Data Modeling: Building simple predictive models or working with machine learning algorithms under the guidance of data scientists to provide insights from data.
• Trend Analysis: Identifying trends, outliers, and patterns in the data that could inform business decisions.

3. Data Visualization

• Data Visualization Tools: Proficiency with visualization tools like Tableau, Power BI, Google Data Studio, or Qlik to present complex findings in an accessible way.
• Creating Dashboards: Designing and building interactive dashboards for stakeholders to track key performance indicators (KPIs), business metrics, and other critical data insights.
• Charts and Graphs: Using charts, graphs, and heatmaps to present data trends in a way that is easy to understand for non-technical stakeholders.

4. SQL and Database Management

• SQL Proficiency: Writing complex SQL queries to retrieve, manipulate, and aggregate data from relational databases such as MySQL, PostgreSQL, SQL Server, or cloud databases like Google BigQuery or Amazon Redshift.
• Database Management: Understanding database structures, including tables, views, indexes, and how to optimize queries for performance.
• ETL (Extract, Transform, Load): Experience with data extraction and transformation processes to prepare data for analysis and reporting.

5. Excel and Advanced Spreadsheet Skills

• Excel Proficiency: Advanced skills in Microsoft Excel for data analysis, including formulas, pivot tables, and advanced functions such as VLOOKUP, INDEX/MATCH, and IF Statements.
• Data Manipulation: Using Excel for data cleaning, filtering, and analysis, as well as for presenting findings in a concise format.
• Macros and VBA: For automating repetitive tasks and improving efficiency in data processing.

6. Reporting and Presentation

• Report Creation: Writing detailed reports summarizing key insights, conclusions, and recommendations based on data analysis.
• Presentations: Creating clear, visually appealing presentations (using tools like PowerPoint or Google Slides) to communicate findings to business leaders and non-technical stakeholders.
• Storytelling with Data: The ability to present data insights in a narrative format, telling a compelling story that ties the data to business outcomes.

7. Business Acumen and Problem-Solving

• Understanding Business Objectives: Aligning data analysis with the organization’s business goals, ensuring that insights lead to actionable recommendations.
• Problem-Solving: Identifying business problems and finding solutions through data analysis. For example, identifying why sales are decreasing in a particular region or understanding customer behavior patterns.
• Critical Thinking: Analyzing data from multiple angles and challenging assumptions to arrive at meaningful insights.