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In the world of internet marketing, search engine optimization is a popular term. Getting higher rankings on the search engine and thereby getting more views to advertiser’s site is basically what it is. However, those views will not mean a lot if they do not lead to sales or conversion. Search advertisers join an online auction in order to get a slot in the search engine results pages. This means, in Pay per Click online model, advertisers have to pay to the search engine for the number of clicks on the advertisement they posted. Therefore, predicting conversion likelihood for advertisers is highly crucial for the sake of the revenue. Besides, being able to understand and track the conversion rates not only allows advertisers to measure the performance of the web pages but also to identify areas for improvement. In this study, machine learning is used for predicting the conversion rate using search term queries in google shopping ads. The purpose is to analyse if a pattern on search term queries to predict conversion rate can be observed. Search term queries with a high probability of better conversion rate can be advertised more. While the bids on low-conversion segments can be lowered to reduce the costs. For this purpose, extracting features from the text to represent it in a way that can be understood by the machine like term frequency and paragraph vector are tested on machine learning and deep learning model. The results show that different patterns of search term queries do not lead to a predictable conversion rate in the specific use case. Thus, the search term itself is no indicator for good or bad conversion rate.
Hypothesis Extraction from Academic Papers Using Neural Networks for Ontology Theory Learning
(2019)
In this study, we investigated a new use case of deep learning. We applied deep learning to extract causes and effects from the hypotheses of the scientific papers. The research presents a variety of RNN models, including RNN models with CRF layer for labelling the sequences. We used such models as Bi-LSTM, LSTM, SimpleRNN and GRU. The experiments were conducted with GloVe vector representation and character level vector representation of words. Moreover, along with RNN models, we evaluated various hyperparameters and model setups to achieve the highest performance scores. In the end, we obtained promising results and shared our thoughts on the future prospects of the following studies.
COVID-19 pandemic influence on the social protection systems of selected Global South countries.
(2022)
Social protection which is a set of policies and programmes designated to improve people’s life quality and protect them from poverty and social vulnerabilities is something that has been severely impacted by the sudden Coronavirus pandemic that began in late 2019. The following paper will analyse how the social protection systems of Brazil and Rwanda performed in the times of the crisis and its main assumption is that the social protection system in Brazil has failed in the face of COVID-19 pandemic due to deficient and callous governing while the inclusive and efficient policymaking in Rwanda enabled the country’s social protection system to conduct an effective crisis response.
The study will examine what are the country-specific social protection factors that contributed to different crisis responses in the instances of Brazil and Rwanda. The key results are that while the COVID-19 pandemic has disclosed the weaknesses of social protection systems in both Brazil and Rwanda, the first had a failed response because of the dismissive attitude of political leaders which resulted in unsatisfactory consequences in the social protection functionality while the latter performed admirably in the times of a crisis because of a fast institutional healthcare response and communication between the government and the citizens. The research method of this study is literature analysis and review as well as a cross-country comparison.
Abstract
The world as we know it today is characterized by a massive process of digitalization in every prospect. Communication patterns, economy and business mechanisms as well as life styles are changing towards a higher utilization of technology, digital devices and the internet. With the bust of the dot.com bubble, the previously static and company dominated web changed into an interactive playground where everybody can become a publisher of content: the Web 2.0 was born. Social media developed soon which is competing with traditional media like TV, newspaper or radio.
Marketers worldwide quickly discovered the potential of using these social media to promote their brands, products and services. Social media has the huge advantage that it is able to track and store all data of a user’s history and actions. This makes it possible to target specific customer groups and provide them customized content. Nevertheless marketing approaches differ from country to country.
Therefore this thesis discusses the potential of social media marketing (SMM) for foreign companies in China in order to improve customer relationships. With market saturation of smartphones, the best and most cost-effective way in China to do SMM is via mobile social networks as they grant the closest access to targeted audiences. For this a “9-step social media action plan” is developed. The action plan is especially suitable for small and medium sized foreign enterprises who utilize the Chinese messenger app WeChat as their first marketing tool.
Die vorliegende Studie untersucht erstmalig, inwieweit Personen, die hinsichtlich des Delikts der Kinderpornografie registriert sind, ebenfalls aufgrund von Sexualstraftaten zum Nachteil von Kindern polizeilich auffällig werden. Die Untersuchung bezieht sich auf alle bei der Polizei Berlin erfassten Tatverdächtigen der Kinderpornografie zwischen 2012 und 2017 (1.569 Fälle).
This study was focused to determine the causal relationship between free cash flow (FCF) and profitability of public listed pharmaceutical companies in Germany. Based on the review of the past empirical studies it was found that contradictory evidences are present in the past studies regarding the relationship of FCF and the variables of profitability of publicly listed companies. To conduct this study, quantitative research was performed, where quantitative data was empirically testing to find evidence regarding the impact of FCF on variables of profitability of public listed pharmaceutical companies in Germany. A sample of 10 years panel data from 2011 to 2020 was selected for 10 German publicly listed pharmaceutical companies. The data was collected from the annual financial reports of the listed companies. Panel regression analysis with random effect was conducted to analyze the data. The results of the panel regression analysis suggested that FCF can have a significant positive impact on net profit margin (NPM) and return on asset (ROA) as variables of profitability. Therefore, the causal relationship of FCF with the two variables of profitability namely; NPM and ROA of the 10 German public listed pharmaceutical companies was found to be positive and significant. However, the causal relationship of FCF with the two variables of profitability namely; gross profit margin (GPM) and return on equity (ROE) of the 10 German public listed pharmaceutical companies was found to be insignificant. The FCF can increase the profitability in terms of NPM and ROA of German public listed pharmaceutical companies. Hence, the causal relationship between free cash available and profitability in term of ROA and NPM of the pharmaceutical public listed companies in Germany were found to be significant and positive. Therefore, the upwards movement of FCF can have a significant positive influence on profitability of pharmaceutical public listed companies in Germany in terms of ROA and NPM.