Unveiling Data-Driven Insights for Maven Market

Problem Statement:

Maven Market, a multinational grocery chain, seeks to gain actionable insights into its sales and returns data across its locations in Canada, Mexico, and the United States. The company aims to streamline decision-making processes and optimize performance by visualizing key metrics and trends.

Project Purpose:

The primary purpose of this project is to leverage Power BI to create a comprehensive dashboard for Maven Market, a multinational grocery chain. The dashboard aims to facilitate data-driven decision-making by providing actionable insights into sales performance, customer behaviour, and regional trends across Maven Market's locations in Canada, Mexico, and the United States. By consolidating and visualizing transactional data, the dashboard empowers stakeholders to optimize operations, enhance customer satisfaction, and drive strategic initiatives effectively.

Objectives:

1. Data Consolidation: Aggregate and integrate transactional data from multiple sources to create a unified view of Maven Market's operations.
2. Insight Generation: Utilize advanced analytics techniques and visualization tools to uncover actionable insights regarding sales trends, customer demographics, and product performance.
3. Performance Monitoring: Develop key performance indicators (KPIs) and metrics to monitor and evaluate the performance of Maven Market's stores, products, and customer segments.
4. Decision Support: Provide decision-makers with timely and relevant information to support strategic planning, resource allocation, and operational decision-making processes.
5. User-Friendly Interface: Design an intuitive and visually appealing dashboard interface that enables stakeholders to interact with the data seamlessly and gain insights effortlessly.
6. Goal Tracking: Enable tracking of performance against predefined targets and benchmarks to facilitate goal setting and performance evaluation.
7. Geospatial Analysis: Incorporate geospatial visualizations to analyse regional variations in sales performance and identify opportunities for expansion or improvement.
8. Trend Analysis: Enable trend analysis by comparing current performance metrics with historical data, allowing stakeholders to identify emerging trends and patterns.
9. Brand Management: Monitor the performance of different product brands and categories to optimize marketing strategies and inventory management.
10. Operational Efficiency: Identify areas for improvement in operational processes, such as inventory management, pricing strategies, and customer service, to enhance efficiency and profitability.

By achieving these objectives, the Power BI dashboard will serve as a valuable tool for Maven Market's management team, enabling them to make informed decisions, drive business growth, and stay competitive in the dynamic retail industry.

Steps involved in the dashboard creation:

Step 1: Connecting and shaping the data.

The initial phase is to connect the data source and clean/transform the data for further analysis.

1. Power BI Options and Settings: Updated Power BI options to deselect "Autodetect new relationships after data is loaded" and set Locale for import to "English (United States)".

2. Connecting and shaping the data: Connected to Maven Market raw files. Formatted data types, added calculated columns, and handled special cases such as merging columns and extracting information. Combined transaction data in 1997 and 1998 files into a single table named "Transaction_Data".

3. Data Quality Assurance: Ensured accurate data types and column names, verified data integrity, and confirmed relationships between tables.

4. Save and Apply: Saved the Power BI file as "MavenMarket_Report".

Step 2: Creating the Data Model.

The next phase involved setting up a robust data model ensuring seamless connectivity between various tables:

1. Table Relationships: Established primary-foreign key relationships between Transaction Data and lookup tables (Customers, Products, Stores, Calendar), maintaining one-to-many cardinality.
2. Data Formatting: Standardized date formats, and currency formats, and categorized relevant columns for enhanced readability and analysis.
3. Data Hiding: Concealed foreign keys and irrelevant columns from the report view for clarity.
4. Snowflake Schema: Implemented a snowflake schema connecting Stores to Regions for a comprehensive understanding of store distribution.

Step 3: Adding Calculated Columns DAX Measures.

In this phase, we augmented our Power BI report with additional calculated columns and measures to enhance data analysis and derive meaningful insights.

1. Data View: Calculated Columns: We enriched the data model with calculated columns as follows:

• Calendar Table: Added columns to identify weekends and determine month-end dates.
• Customers Table: Calculated customer age, priority status, abbreviated country name, and house numbers.
• Products Table: Categorized products into price tiers based on their retail prices.
• Stores Table: Calculated the years since the last remodel for each store.

2. Report View: Measures: We introduced measures to quantify key metrics and performance indicators:

• Quantified transaction volumes, returns, revenue, and costs.
• Calculated return rates, profit margins, and percentages of weekend transactions.
• Derived total counts, unique products, and year-to-date revenue.
• Computed revenue targets based on historical performance and set growth objectives.

These measures enable comprehensive analysis and decision-making, empowering Maven Market stakeholders with actionable insights into sales performance, profitability, customer demographics, and strategic planning.

Step 4: Building the Report.

The final phase focused on creating a visually appealing and intuitive report layout:

1. Topline Performance: Renamed the main tab and incorporated the Maven Market logo for branding.
2. Matrix Visual: Utilized a matrix visual to showcase Total Transactions, Total Profit, Profit Margin, and Return Rate by Product Brand, enriched with conditional formatting for better visualization.
3. KPI Cards: Added KPI cards to monitor the current month's transactions, profit, and returns against the previous month, facilitating trend analysis and goal tracking.
4. Map Visual: Integrated a map visual to display Total Transactions by store city, allowing for geographical analysis, with an option to filter by store country.
5. Column Chart: Introduced a column chart to illustrate Weekly Revenue Trending, with filters applied to focus on data for the year 1998.
6. Gauge Chart: Incorporated a gauge chart to compare Total Revenue against Revenue Target, enabling quick assessment of performance against set targets.

Conclusion:

This project successfully utilized Power BI to transform Maven Market's raw data into actionable insights. By organizing and analysing data on customer behaviour, product performance, and financial metrics, the project equips Maven Market with valuable tools for strategic decision-making. The addition of DAX measures enhances analytical capabilities, enabling stakeholders to optimize operations and identify growth opportunities. Overall, this project empowers Maven Market to adapt to market dynamics and sustain long-term success.