Automated classification of web contents in B2B marketing

Recent growth in digitization has affected how customers seek the information they need to make a purchase decision. This trend of customers making their purchase decision based on the information they collect online is increasing. To accommodate this change in purchase behavior, companies tend to share as much information about themselves and their products online, which in turn drives the amount of unstructured data produced. To get value for this huge amount of data being produced, the unstructured data needs to be processed before being used in digital marketing applications. When it comes to the companies serving business to customers (B2C), plenty of research exists on how the digital content could be used for marketing, but for the companies serving business to business (B2B) a huge research gap presides. B2C marketing and B2B marketing might share some analytical concepts but they are different domains. Not much research has been done in the field of using machine learning in B2B digital marketing. The lack of availability of labeled text data from the B2B domain makes it challenging for researchers to experiment on text classification models, while several methods have been proposed and used to classify unstructured text data in marketing and other domains. This thesis studies previous works done in the field of text classification in general, in the marketing domain, and compares those methods across the dataset available for this research. Text classification methods such as Random Forest, Linear SVM, KNN, Multinomial Naïve Bayes, and Multinomial Logistic Regression dominates the research field, hence these methods are tested in this research. In the used dataset surprisingly, Random Forest Classifier performed best with an average accuracy of 0.85 in the designed five-class classification task

E-Learning Benefits and Applications

According to Forbes e-learning is a €100 billion industry today and the number is growing regularly. It is already proving very popular in universities and it is estimated that by 2019 roughly half of the universities classes will be offered online. Major multinational companies are also using some form of technology in training their staff. But still the question remains how effective it is to move educational materials online in order to engage students more and get good results as compared to face-to-face learning/training. The aim of this thesis is to study the background and benefits of e-learning and to create an online course using one of the platforms available in Helsinki Metropolia University of Applied Sciences. The benefits of e-learning can be in form or cost of productivity. The thesis is divided into different sections: overview of e-learning, cost benefits, a case study of one company in India and preparing an online course in the open platform edX together with dental hygiene students. The outcome of this project is a report including a detailed description of how to create an online course in Metropolia edX platform. The created online course is intended to aid new dental hygiene students. Soile Vedenpää and Nelli Sarantila prepared the course materials. The case study was prepared with the help of Shila Ghimire who is the deputy finance manager at M’Cons Media Marketing Private Limited, India. This study can be furthered by learning about online study environments implemented in other universities and collecting feedback and analysing the effectiveness of the online course prepared during this project

Changing role of EMS -analyses of non-conveyed and conveyed patients in Finland

Background: Emergency Medical Services (EMS) and Emergency Departments (ED) have seen increasing attendance rates in the last decades. Currently, EMS are increasingly assessing and treating patients without the need to convey patients to health care facility. The aim of this study was to describe and compare the patient casemix between conveyed and non-conveyed patients and to analyze factors related to non-conveyance decision making. Methods: This was a prospective study design of EMS patients in Finland, and data was collected between 1st June and 30th November 2018. Adjusted ICPC2-classification was used as the reason for care. NEWS2-points were collected and analyzed both statistically and with a semi-supervised information extraction method. EMS patients’ geographic location and distance to health care facilities were analyzed by urban–rural classification