Brand and Market Analysis Project

Overview

This project provides a comprehensive and extensible framework for brand and market analysis, enabling businesses to make data-driven decisions. It is designed to be highly modular, customizable, and user-friendly, supporting a wide range of analyses from brand performance to predictive forecasting.

Core Features

• Modular Architecture: The project is organized into a bma package with distinct modules for each analysis task, making it easy to extend and maintain.
• Configuration-Driven: The analysis pipeline is controlled by a central config.yaml file, allowing for easy customization of parameters without changing the code.
• Multi-Source Data Ingestion: The data_ingest module can load data from various sources, including local files (CSV, Excel, JSON) and URLs.
• Automated Data Cleaning: The cleaning module provides a pipeline for standardizing column names, parsing dates, and handling missing values.
• Advanced Analysis: The analysis module includes functions for brand and client performance, discount impact, cost efficiency, and customer segmentation.
• Interactive Dashboard: An interactive Streamlit dashboard (dashboard_streamlit.py) allows for visual exploration of the data and analysis results.
• Extensible Framework: The new architecture with the AnalysisOrchestrator makes it easy to add new analysis modules and integrate them into the pipeline.

Project Structure

The project has been refactored into a modular bma package:

• bma/orchestrator.py: Contains the AnalysisOrchestrator class, which runs the entire analysis pipeline based on the configuration.
• bma/data_ingest.py: Handles loading data from various sources.
• bma/cleaning.py: Provides data cleaning and preparation functions.
• bma/analysis.py: Contains the core analysis functions.
• bma/forecast.py: For time series forecasting.
• bma/sentiment.py: For sentiment analysis.
• bma/recommendations.py: For generating strategic recommendations.
• bma/scenario.py: For scenario simulation.
• brand_market_analysis.py: The main entry point for running the analysis from the command line.
• dashboard_streamlit.py: The Streamlit dashboard for interactive analysis.
• config.yaml: The central configuration file for the analysis pipeline.

Usage

Installation

Ensure the following libraries are installed:

pip install -r requirements.txt

Running the Analysis

The analysis can be run from the command line using the brand_market_analysis.py script:

python brand_market_analysis.py config.yaml path/to/your/data.csv

Interactive Dashboard

To start the interactive Streamlit dashboard, run the following command from the project root:

streamlit run bma/dashboard_streamlit.py

Future Enhancements

This project is designed for continuous improvement. Future enhancements will focus on:

• Advanced Forecasting Models: Integrating more advanced forecasting models like Prophet and ARIMA.
• Machine Learning-Powered Recommendations: Evolving the recommendation engine to use machine learning for more nuanced advice.
• Competitive Benchmarking: Adding a dedicated module for comparing brand performance against competitors.
• Enhanced Scenario Simulation: Expanding the scenario simulation capabilities to model more complex business decisions.

Conclusion

This in-depth analysis tool offers actionable insights for businesses, guiding strategies related to pricing, inventory management, and targeted marketing campaigns in the retail landscape. The new modular and configuration-driven architecture makes it a powerful and flexible tool for any data analyst or business strategist.

Acknowledgments

We extend our gratitude to the contributors and the open-source community for their invaluable input.