A Python package designed to simplify retail sales data analysis for small to medium-sized businesses. This tool offers a set of pre-built functions that make it easy to identify market segments, predict future sales, and analyze seasonal revenue trends.

Small to medium-sized businesses (SMBs) often lack the resources for in-house data teams or complex analytics tools. sales_analyzer is here to bridge that gap by providing easy-to-use, specialized functions that allow businesses to extract valuable insights from their sales data without requiring deep expertise in data science.

• Tailored for SMBs: No need for expensive or complex tools. Our package is designed specifically for small to medium-sized businesses to help them make data-driven decisions with ease.
• Easy-to-use functions: Simple, pre-built functions for common retail sales tasks so you can get started right away.
• Cost-effective: Instead of hiring a full-time data analytics team or paying for expensive software, this package offers an affordable, one-stop solution to meet your business’s analytical needs.
• Actionable Insights: Gain a better understanding of your market segments and sales trends, which can inform inventory management, marketing strategies, and customer outreach.

While existing Python packages such as Pandas and Scikit-learn provide powerful general-purpose tools for data manipulation and machine learning, they require significant customization and specialized knowledge to be applied effectively to retail sales analysis. sales_analyzer complements these tools by streamlining common retail-specific tasks. It provides a suite of pre-built, easy-to-use functions specifically tailored to sales data, so businesses don't need to spend time customizing solutions for their needs.

$ pip install salesanalyzer_mds

• segment_revenue_share: Segments products into three categories: cheap, medium, expensive, based on price, and calculates their respective share in total revenue.
• predictSales: Predicts future sales based on the provided historical data and the target.
• sales_summary_statistics: Calculates a variety of summary statistics that provide insights into overall sales performance, customer behavior, and product performance.

salesanalyzer_mds can be used to extract sales data insights from available data.

1. Set up imports

from salesanalyzer_mds.sales_summary_statistics import sales_summary_statistics
from salesanalyzer_mds.segment_revenue_share import segment_revenue_share
from salesanalyzer_mds.predict_sales import predict_sales
import pandas as pd     # additional import to handle your sales data

2. Load your sales data as pandas DataFrame

3. Retrieve the insights:

Summary statistics

sales_summary_statistics(your_sales_data)

The sales_summary_statistics() function returns a pandas DataFrame with:

• 'total_revenue': The total revenue generated by all sales.
• 'unique_customers': The number of unique customers.
• 'average_order_value': The average value of an order (sum of revenue per invoice).
• 'top_selling_product_quantity': The product with the highest quantity sold.
• 'top_selling_product_revenue': The product with the highest total revenue.
• 'average_revenue_per_customer': The average revenue generated by each customer.

Segment revenue share

segment_revenue_share(your_sales_data, 
                      price_col='UnitPrice', 
                      quantity_col='Quantity',
                      price_thresholds=None)      # replace column names with your data column names

The segment_revenue_share() funtion returns a pandas DataFrame showing the total revenue share for each price segment: 'cheap', 'medium', 'expensive'. Custom price thresholds can be set by the user other set automatically.

• Custom price thresholds can be set using the price_thresholds parameter.
• If not specified, thresholds are automatically determined based on the data.

Predict sales

predict_sales(your_sales_data, 
              new_data,     # new sales data to base the predictions on
              numeric_features = ['UnitPrice'],
              categorical_features = ['Description', 'Country'], 
              target = 'Quantity', 
              date_feature = 'InvoiceDate')

The predict_sales() function returns a DataFrame with prediction values, and a printed out MSE score.

1. Clone the repository and navigate into the project root directory.

2. Create a new environment with Python 3.10:

   conda create -n salesanalyzermds python=3.10
   conda activate salesanalyzermds

3. Install Poetry by following these instructions, and then run the following bash command to install the necessary dependencies:

   poetry install

To test the salesanalyzer-mds package, follow the steps below:

1. Execute the tests using pytest from the root project directory:

pytest tests/

2. To assess the branch coverage for this package:

pytest --cov=salesanalyzer_mds --cov-branch

This package relies on the following dependencies as outlined in pyproject.toml:

• python = ">=3.10"
• scikit-learn = ">=1.6.1"
• pandas = ">=2.2.3"
• pytest = ">=8.3.4"
• jupyter = ">=1.1.1"
• myst-nb = ">=1.1.2"
• sphinx-autoapi = ">=3.4.0"
• sphinx-rtd-theme = ">=3.0.2"

• Yeji Sohn
• Daria Khon
• Franklin Aryee

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

salesanalyzer_mds was created by Yeji Sohn, Daria Khon, Franklin Aryee. It is licensed under the terms of the MIT license.

salesanalyzer_mds was created with cookiecutter and the py-pkgs-cookiecutter template.