Elzboon

Elzboon is a Customer Relationship Management (CRM) system powered by AI 🤖.

In our project Elzboon, we apply machine learning algorithms to help businesses make insightful decisions based on their data. Our CRM system aims to assist businesses in increasing profitability and efficiently managing their customers.

Front-end: https://github.com/Ahmed-Abou-Emran/ELzoboon-CRM

Features

• Customer segmentation: Elzboon provides advanced customer segmentation capabilities, allowing businesses to group their customers based on various criteria such as demographics, purchasing behavior, and engagement levels. This helps businesses target specific customer segments with personalized marketing campaigns and improve customer satisfaction.

• Sales forecasting: Elzboon's AI-powered algorithms analyze historical sales data and market trends to provide accurate sales forecasts. This helps businesses plan their inventory, optimize resource allocation, and make informed decisions regarding pricing, promotions, and marketing strategies.

• Sentiment analysis of customer messages: Elzboon's AI algorithms can automatically analyze the sentiment of customer messages, including emails, chat logs, and social media interactions. This helps businesses identify customer satisfaction levels, detect potential issues or complaints, and respond promptly to ensure customer happiness.

• Summarization of customer messages: Elzboon's natural language processing capabilities allow for the automatic summarization of lengthy customer messages. This helps businesses save time by providing concise summaries of customer feedback, inquiries, or complaints, enabling them to address issues more efficiently.

• Tagging of customer messages: Elzboon provides automated tagging of customer messages based on predefined categories or custom tags. This allows businesses to categorize and prioritize customer inquiries, streamline customer support processes, and identify trends or common issues.

• ChatBot: Elzboon offers an AI-powered chatbot that can handle basic customer inquiries, provide product information, and assist with order tracking. The chatbot helps businesses provide round-the-clock customer support, reduce response times, and improve overall customer experience.

Nice To Have

1. Integration with Facebook store: Elzboon aims to integrate seamlessly with Facebook stores, enabling businesses to manage their online sales, inventory, and customer interactions from a centralized platform. This integration provides a unified view of customer data and streamlines the selling process.

Customer Segmentation

Customer segmentation is a crucial aspect of any business's marketing strategy. Elzboon's customer segmentation feature allows businesses to divide their customer base into distinct groups based on various factors, including demographics, purchasing behavior, geographic location, and engagement levels. By understanding different customer segments, businesses can tailor their marketing efforts, personalize communication, and create targeted campaigns to maximize customer engagement and satisfaction.

Sales Forecasting

Accurate sales forecasting is vital for effective business planning and resource management. Elzboon's sales forecasting feature utilizes historical sales data, market trends, seasonal patterns, and other relevant factors to provide accurate predictions of future sales. By having reliable sales forecasts, businesses can optimize their inventory levels, plan production and procurement, allocate resources efficiently, and make informed decisions regarding pricing, promotions, and sales strategies.

Dataset

The Online Retail II dataset is a valuable resource for conducting customer relationship management (CRM) analytics and machine learning tasks. Here are some additional details about the dataset:

• Source: The dataset was collected from an online retailer that sells various products to customers across different countries. It includes transactional data and customer information, making it suitable for analyzing customer behavior and implementing CRM strategies.

• Contents: The dataset contains approximately one year's worth of transactional data, spanning from December 2009 to December 2010. It includes over 1 million transactions made by around 5,900 customers.

• Attributes: The dataset includes several key attributes that provide insights into customer behavior and purchase patterns. Some of the essential attributes include:

  • InvoiceNo: A unique identifier for each transaction.
  • StockCode: A unique identifier for each product.
  • Description: The name/description of the product.
  • Quantity: The quantity of each product purchased in a transaction.
  • InvoiceDate: The date and time when the transaction was generated.
  • UnitPrice: The price of a single unit of the product.
  • CustomerID: A unique identifier for each customer.
  • Country: The country where the customer resides.

• Usage: The dataset is suitable for various CRM analytics tasks, including customer segmentation, sales forecasting, customer lifetime value analysis, and recommendation systems. It provides a rich source of information to understand customer preferences, purchasing behavior, and market trends.

• Data Preprocessing: As with any real-world dataset, the Online Retail II dataset may require preprocessing steps to handle missing values, data cleaning, and feature engineering. These steps are essential to ensure the accuracy and reliability of the analysis and machine learning models.

• Data Access: The dataset can be accessed from the UCI Machine Learning Repository at the following link: Online Retail II Dataset. The website provides detailed information about the dataset, including data format, attribute descriptions, and references.

By leveraging the Online Retail II dataset in the Elzboon CRM system, businesses can gain insights into customer behavior, make data-driven decisions, and implement personalized marketing strategies to enhance customer satisfaction and drive business growth.

If you have any further questions or need assistance with utilizing the dataset, feel free to ask!

Feel free to reach out if you have any questions or feedback. We're excited to help businesses leverage AI for effective customer management and decision-making.

Under supervision of Dr. Walaa Hassan El-Ashmawi, Faculty of Computer Science, Misr International University

Credits: Customer Segementation: https://www.kaggle.com/code/julienjta/e-commerce-segmentation-rfm-kmeans-4129 Sales Forecasting: Lead Scoring: