Empowering Retail Intelligence

Objectives:

1. Comprehensive Data Analysis: To uncover underlying patterns and trends in consumer shopping data, do exploratory data analysis (EDA).

2. Python Integration: Make use of Python to utilize sophisticated analytical tools for a more nuanced understanding of the data, as well as for data pretreatment and feature engineering.

3. Power BI interaction: Create visually appealing and engaging dashboards for efficient data sharing by implementing a smooth interaction with Power BI.

4. Customer Segmentation: To enable focused marketing efforts, use clustering algorithms to segment customers based on their purchase habits.

5. Collaboration and Knowledge Sharing: Establish a platform for insight sharing and encourage a data-driven culture inside the company to promote cooperation between the retail and data teams.

Sales Performance Report

Executive Summary: This report provides a comprehensive analysis of sales performance based on key metrics and insights derived from transactional data. The insights outlined below aim to guide strategic decision-making and operational improvements to drive business growth and profitability.

1. Total Revenue and Quantity:

• Total Revenue: $251.51 million
• Total Quantity Sold: 299,000 units
• Average Price per Unit: $841.97
• Revenue per Transaction: $2.53 thousand

Analysis: The figures indicate a healthy revenue stream with a significant number of transactions. The balanced pricing strategy, reflected in the average price per unit, suggests effectiveness in sales efforts. Evaluating revenue per transaction helps in optimizing strategies to increase transaction value further.

2. Monthly Sales:

• Highest in October, lowest in February
• Sales decline observed after the peak in October

Analysis: Understanding seasonal trends is crucial for planning inventory management and marketing campaigns effectively. Identifying reasons for the decline post-October can lead to strategies for sustaining or boosting sales during slower months.

3. Sales by Category:

• Clothing: $114 million
• Shoes: $67 million
• Other categories contribute to the remaining revenue

Analysis: Knowing high-performing categories allows for targeted marketing efforts, product assortment optimization, and resource allocation to maximize profitability.

4. Revenue by Payment Method:

• Cash: $112 million (44k transactions)
• Credit: $88 million (35k transactions)
• Debit card: $50 million (20k transactions)

Analysis: Understanding preferred payment methods informs decisions related to payment processing systems and may indicate a need for promoting alternative methods to encourage customer spending.

5. Sales Performance by Year and Location:

• Highest Sales in 2022: $115.44 million
• Mall of Istanbul generates highest revenue: $50.87 million

Analysis: Identifying high-performing years and locations helps in replicating successful strategies and allocating resources effectively to maximize revenue across different regions.

6. Customer Demographics:

• Average Customer Age: 43
• Average Basket Size: 3 units
• Transactions per Customer: 99k
• Female Sales Contribution: 59.72%

Analysis: Understanding customer demographics and behavior guides marketing efforts, product assortment decisions, and enhances customer experience to increase sales.

Practical Uses of Insights:

Promotional Planning:

Practical Use: Plan targeted promotions and discounts based on seasonal sales trends to maximize revenue.

Value: Implement promotions during slower months like February to stimulate sales. For instance, offer discounts or bundle deals to encourage higher transaction volume.

Inventory Management:

Practical Use: Adjust inventory levels based on category performance to optimize stock levels and minimize holding costs.

Value: Allocate more inventory space and resources towards clothing and shoes, which are the top-performing categories generating $114 million and $67 million respectively in revenue.

Payment Method Optimization:

Practical Use: Encourage the use of preferred payment methods to streamline transactions and improve customer satisfaction.

Value: Implement incentives such as discounts or rewards for customers using credit or debit cards, as these methods contribute $88 million and $50 million respectively in revenue.

Location Strategy:

Practical Use: Allocate marketing budgets and resources to high-performing locations to further increase sales.

Value: Focus marketing efforts on Mall of Istanbul, the top-performing location generating $50.87 million in revenue, to drive additional foot traffic and sales.

Customer Engagement:

Practical Use: Tailor marketing messages and promotions to resonate with the average customer age demographic.

Value: Develop marketing campaigns that appeal to the average customer age of 43, targeting products and messaging that align with their preferences and interests.

Customer Retention:

Practical Use: Implement customer loyalty programs or personalized marketing initiatives to increase customer retention.

Value: Increase customer engagement and loyalty by offering exclusive discounts or rewards to frequent shoppers, who make an average of 99k transactions per customer.

Some of the visuals using Python Code are given below and explore the rest by clicking on python logo in the dashboard.