Retail e-commerce in Bangladesh is growing at a rate of 72 percent every month. This industry currently employs over 35,000 people and over 25,000 small and medium businesses (SMEs). Up to 2018, there were 2,500 e-commerce business sites and 150,000 e-commerce pages, respectively. Currently, the number of deliveries per day is believed to be between 15,000 and 20,000 . Despite the fact that the e-commerce business has grown over time, it still faces a number of challenges including a shortage of data science and machine learning driven platforms, enterprise applications powered by AI, tools for developing conversation interfaces, etc. Further obstacles include a lack of trust in the e-commerce ecosystem, product quality control and a fear of shopping online. Another important flaw is the absence of a proper procedure for resolving customer complaints. Moreover, there is a reluctance to incorporate banking facilities in the e-commerce industry . As per Statista, Bangladesh is in the 47th position in the world of e-commerce and the share of e-commerce sales is a small percentage of total retail sales of the country.
The objective of this study is to analyze the online market places available in Bangladesh e.g. daraz, bikroy.com, chandal, foodpanda, etc. to analyze the currently offered technologies, products, compare prices with physical stores, analyze user reviews and sentiments, and identify the shortcomings related to technology, trust, financial transactions, and business policy.
The project aims to deliver a data-driven solution for improving the market places including fraud detection/prevention in the context of Bangladesh. The results will be made open source. This will help regulatory bodies, policy makers, e-commerce companies, consumers etc. and also educate aspiring data scientists in solving real-world problems.
Collecting the data from/about online market places in Bangladesh.
Conduct exploratory data analysis and sentiment analysis
Develop a product recommendation/ fraud detection system
Deploy the recommendation/ fraud detection system and summarize a comprehensive business insights from the study
1. Web Scraping
2. Data Cleaning
3. Sentiment Analysis
4. Bengali NLP
5. Machine Learning
6. Recommendation System
7. Cyber Security