Projects & Essays

An Introductory Deep Dive in Machine Learning and Artificial Intelligence in Hedge Funds

Artificial Intelligence in asset management can provide better sources of uncorrelated income through different investment allocations. A.I. can lower the crowding effect and support an asset manager in many investments’ decisions. It reduces annualized volatility and has a better risk-adjusted return. A.I. shows the adaptivity and self-learning capability that add value along the entire values chain. However, the inherently flexible nature of machine learning methods is also the biggest challenge. Technologies must go hand in hand with expertise and experience, must be applied thoughtfully in the proper context so that an Asset Manager can create an edge in his investment philosophies.

Keywords: Hedge Funds, Machine Learning, Trading, Investing, Alternative Investments

An Introductory Deep Dive into Data Sharing

Data-driven markets rely on access to data as a resource for products and services. The accessibility of information is steadily increasing, with the quality and quantity of data available. Companies involved in the data economy have a vested interest in accessing data from other market participants. However, the data- sharing economy shows that companies still seem reluctant to share their data. The critical question, therefore, is how to incentivize data sharing. On this quest of the emergence of new intermediates, platform companies such as Snowflake, DAWEX, and the international data spaces (IDS) lay foundations on how these new mediators can play an essential role in the data-sharing economy, trying to solve the problem by increasing the willingness to share data.

Keywords Data-driven markets · Data Sharing economy · Access to data · Platforms · Industrial IoT

Harnessing the Power of Customer Segmentation through Database Marketing

Firms face increasing complexity and competition in today’s business world; this requires the development of innovative methods to identify customer needs and improve customer satisfaction and retention. Research shows that customer relationship management (CRM) is a widely recognized strategy for the acquisition and retention of customers. The goal of CRM is to create a long-lasting and profitable relationship with customers. Nowadays, companies have access to a treasure trove of data related to customer demographics transactions; however, they lack the skills to interpret and leverage this information. At Varian Medical Systems, a radiation oncology treatments and software maker, such a tool might exist; however, it is not yet easily accessible and very complicated to utilize. This study aims to bridge the gap between Varian’s current customer analysis in Excel and a manager-friendly dashboard that can facilitate a customer-centric approach to CRM. Building on the company’s existing approaches to customer analysis, the study seeks to address the following question: How can Varian better understand and identify customer segments and provide more targeted and personalized marketing that can be effectively communicated?

The goal is to create a common baseline for data and reports to enable the organization to increase efficiency across the business. At the moment data is pulled and analyzed by Finance, Marketing and Service in different economic areas, depending on their business focus. An interactive dashboard will increase efficiency within these departments and provide reporting standards across all divisions. Since so much effort is put into data retrieval from various sources which is then merged into Excel sheets manually, the organization does not spend time on analyzing the data; as a result, the ‘big picture’ relating to software service customers gets completely lost. Moreover, the practice of database marketing will help Varian to extract meaningful clusters of customers to improve and enhance its marketing actions.

Index Terms: Keywords: Database Marketing, Customer Segmentation, Visual Analytics

Keywords: customer-centric marketing, explorative dashboard, visual data-mining contractual business setting, customer behavior segmentation, CLV, RFM model