Iowa-Liquor-Retail-Sales-Analysis

Problem Statement:

The Iowa Department of Commerce aims to enhance its understanding of liquor retail sales patterns across the state to improve economic strategies and support local businesses. Utilizing the public dataset available on the Google BigQuery platform, the objective of this project is to analyze historical liquor sales data to identify key trends, patterns, and factors influencing sales. The insights gained will assist the department in formulating data-driven decisions and policies to optimize sales performance and address regional disparities.

Objectives:

1. Advanced Sales Trend Analysis:
   • Conduct in-depth time series analysis to forecast future sales trends and identify cyclical patterns.
   • Utilize advanced statistical methods and machine learning algorithms to predict sales during peak periods, holidays, and special events.
2. Comprehensive Geographical Analysis:
   • Perform spatial analysis to assess sales performance across different counties and cities in Iowa.
   • Develop heat maps and other geospatial visualizations to identify top-performing regions and areas with potential for sales improvement.
   • Implement geospatial clustering techniques to uncover hidden patterns and correlations between population density and liquor sales.
3. Detailed Product Category Analysis:
   • Employ market basket analysis to determine the best-selling liquor categories and brands.
   • Use association rule mining to uncover frequently bought together product combinations.
   • Analyze changes in consumer preferences over time using trend analysis and sentiment analysis.
4. Enhanced Market Basket Analysis:
   • Conduct association rule mining and sequence analysis to identify purchasing patterns and product affinities.
   • Provide actionable insights and recommendations for potential promotional strategies based on frequent item sets and customer purchase sequences.
5. Customer Demographics and Segmentation:
   • Analyze demographic data to understand the profile of typical customers and tailor marketing efforts accordingly.
   • Use clustering algorithms (e.g., K-means, hierarchical clustering) to segment customers and identify significant shifts in customer demographics over the analysis period.
   • Implement customer segmentation models to personalize marketing strategies and improve customer engagement.
6. Advanced Sales Performance Metrics:
   • Calculate and interpret key performance metrics such as average transaction value, sales per capita, and market share.
   • Use benchmarking techniques to compare these metrics across different regions and time periods.
   • Develop dashboards and visualizations to track and monitor sales performance in real-time.
7. Impact of External Factors:
   • Utilize econometric modeling to examine the impact of external factors such as economic conditions, regulatory changes, and public health guidelines on liquor sales.
   • Implement scenario analysis to assess potential future impacts and develop contingency plans.

Expected Outcomes:

• Comprehensive reports and interactive dashboards detailing advanced sales trends, geographical performance, and product category insights.
• Data-driven recommendations for enhancing sales strategies, addressing regional disparities, and optimizing product offerings.
• Identification of potential market opportunities and areas for improvement in sales performance.
• Enhanced understanding of customer behavior and preferences to inform targeted marketing and promotional efforts.
• Forecast models and scenario analysis to support strategic planning and decision-making.

Tools and Technologies:

• Data Source: Public dataset on the Google BigQuery platform.
• Data Analysis and Visualization: SQL, Python, Power BI.
• Machine Learning and Statistical Analysis: Python (pandas, numpy, scikit-learn, statsmodels), R (caret, randomForest).

Approach:

• STEP 1: Define the Scope and Objectives
• STEP 2: Access and Load the Data
• STEP 3: Data Cleaning and Preparation
  • Checking for missing values
  • Handling missing values
    • Removing rows with missing ESSENTIAL values
    • Fill missing values in NON-ESSENTIAL columns
  • Remove Duplicates
  • Convert Data Types
• STEP 4: Exploratory Data Analysis (EDA)
• STEP 5: Advanced Analysis
• STEP 6: Visualization and Reporting
• STEP 7: Recommendations and Insights

Process: OBJECTIVE 1

• The objective is to analyze sales trends at the store level to forecast future sales trends and identify cyclical patterns.
• Accessed and Loaded the Data
• Checked for Missing Values
• Handled missing values
  • ESSENTIAL Columns are: 'date', 'sales_dollars', 'store_number'
    • Time series analysis
    • Forecast future sales
• Cleaned data using SQL.
• Built interactive dashboards in Power BI.

Results and Impact Outcomes:

• Identified top-selling products and underperforming regions.
• Uncovered seasonal sales trends.
• Provided actionable recommendations that led to a 12% increase in quarterly sales.

Metrics:

• Increased sales revenue by 12%.
• Improved data accuracy by 15%.

Visuals: [Insert Chart/Image] [Insert Chart/Image]

Challenges and Solutions Challenges: Handling inconsistent data formats.

Solutions: Implemented data validation techniques to ensure consistency.

Conclusion and Learnings Summary: The project provided valuable insights that directly contributed to improved sales strategies and revenue growth.

Learnings: Enhanced skills in data cleaning, EDA, and visualization.

Credits Collaborators: [Names of people who worked with you on the project, if any] Data Sources: [Sources of any data used, such as public datasets, company databases, etc.] Tools and Technologies: [List of tools and technologies used, if not mentioned already] Special Thanks: [Any individuals or organizations you wish to thank for their support or contributions]