There has been a great deal of research conducted in the past on utilizing social media analytics to derive consumer insights and understand their behaviors.However, when such studies are applied to real-world data in an industrial use-case, the results are often found to be incorrect and erroneous.This is a major barrier for companies that provide social media analytics-based solutions to customer-centric industries such as Food and Beverage (FnB).One of the key causes of this barrier is the failure to appropriately process and curate raw social data prior to analytics.In this study, we discuss the challenges we encountered when dealing with social data throughout our industrial experience and propose a standard solution -FOODIE.This is a framework specifically designed for the FnB industry to process social conversation data accurately and in a standard manner prior to perform various downstream tasks on it.The three stages of this paradigm are preparation, sifting, and evaluation.Through this framework, we have reduced the data to error ratio from 8.76% to 0.01% which is quite significant given the volume of the data.While this framework is designed for the FnB space, it can be customized to suit the needs of various other industries.

Electronic Commerce (E-Commerce) has become one of the most significant consumer-facing tech industries in recent years.This industry has considerably enhanced people's lives by allowing them to shop online from the comfort of their own homes.Despite the fact that many people are accustomed to online shopping, e-commerce merchants are facing a significant problem, a high percentage of checkout abandonment.In this study, we have proposed an end-to-end Machine Learning (ML) system that will assist the merchant to minimize the rate of checkout abandonment with proper decision making and strategy.As a part of the system, we developed a robust ML model that predicts if someone will checkout the products added to the cart based on the customer's activity.Our system also provides the merchants with the opportunity to explore the underlying reasons for each single prediction output.This will indisputably help the online merchants in business growth and effective stock management.

Electronic Retailing (E-tailing) is one of the most impactful technology trends of recent times. This industry has dramatically enhanced the quality of human lives allowing people to shop online while having the comfort of their homes. In developing countries like Bangladesh, this industry is still rising and creating a significant economic impact. However, there exist a lot of challenges such as the return of orders that affects the growth of an E-tailer and causes revenue losses. This study addresses this most common business challenge in the E-tail industry and performs predictive modeling using 4 different state-of-the-art data mining techniques to help the industry smoothen its curve of growth. Along with predictive modeling, this study also aims to find out the most important features that influence the return of orders.

The garment industry is one of the most dominating industries in this era of industrial globalization. It is a highly labor-intensive industry that requires a large number of human resources to produce its goods and fill up the global demand for garment products. Because of the dependency on human labor, the production of a garment company comprehensively relies on the productivity of the employees who are working in different departments of the company. A common problem in this industry is that the actual productivity of the garment employees sometimes does not meet the targeted productivity that was set for them by the authorities to meet the production goals in due time. When the productivity gap occurs, the company faces a huge loss in production. This study aims to solve this problem by predicting the actual productivity of the employees. To achieve this aim, a Deep Neural Network (DNN) model has been proposed to predict the actual productivity of the employees. The experimental results of this study have shown that the proposed model yields a promising prediction performance with a minimal Mean Absolute Error (=0.086) which is less than the baseline performance error (=0.15). Such prediction performance can indisputably help the manufacturers to set an accurate target, minimize the production loss and maximize the profit.