Imagine unlocking the hidden potential within your social networking platform – the power to not only understand user behavior but to predict and optimize it. This book delves into the crucial world of Key Performance Indicators (KPIs) and unveils a practical, data-driven approach to maximizing engagement and growth. Explore a comprehensive methodology for identifying and assessing the most relevant KPIs, tailored specifically for social platforms. Discover how event sourcing can revolutionize your data analytics, providing a real-time, granular view of user interactions. Learn to harness the power of statistical methods like the Mann-Whitney U test to extract meaningful insights and validate your strategies. This book goes beyond theory, offering a step-by-step guide to building and implementing a sophisticated web application, a "Dashapp," designed for KPI monitoring and optimization. Master the art of data visualization with Python and Plotly, transforming raw data into compelling dashboards that reveal critical trends in user funnels, network dynamics, and retention rates. Whether you're a data scientist, a product manager, or a technology enthusiast, this book provides the knowledge and tools to transform your social platform into a data-informed powerhouse. Uncover practical techniques for data preprocessing, A/B testing, and metric optimization, enabling you to make informed decisions and drive sustainable growth. This is your roadmap to leveraging data analytics and achieving unparalleled success in the dynamic world of social networking, where understanding user behavior is the key to dominating the competition. Optimize your social networking platform with data-driven insights, leveraging event sourcing and powerful tools for enhanced KPI management and strategic decision-making. This book provides actionable strategies for data analysis and metric improvement, enabling you to achieve significant gains in user engagement and overall platform performance, guiding you through the intricacies of KPI selection, data visualization, and web application implementation for real-world impact. This is more than just a guide; it's a transformation, turning raw data into a strategic advantage, leading to increased user satisfaction and sustained platform growth, ensuring your social network not only survives but thrives in today's competitive digital landscape.
Inhaltsverzeichnis (Table of Contents)
- 1. Introduction
- 1.1. Goals and Remarks
- 1.2. Data Science
- 1.2.1. Data Analytics
- 1.2.2. Key Performance Indicator
- 1.2.3. North Star Metric
- 1.3. Clye
- 1.3.1. Explanation
- 1.3.2. Predefined Information and Structure
- 2. KPIs
- 2.1. Identification
- 2.2. Reasoning and Relevance of KPIs
- 3. Methods, Patterns and Tools
- 3.1. Methods and Patterns
- 3.1.1. Mann-Whitney U Test and p-Value
- 3.1.2. Eventsourcing
- 3.1.3. Experiments
- 3.1.4. Dashboard
- 3.1.5. Visualizations
- 3.2. Tools
- 3.2.1. Python
- 3.2.2. Plotly
- 3.2.3. Tools for Data Analysis
- 4. Data analysis execution
- 4.1. Data Preprocessing
- 4.2. Dashapp
- 4.2.1. Setting Options
- 4.2.2. Dashboard
- 4.2.3. Funnel
- 4.2.4. Network
- 4.2.5. Retention
- 4.3. Experiments
- 4.3.1. Design Testing Experiment
- 4.3.2. Email Experiment
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This thesis aims to identify relevant Key Performance Indicators (KPIs) for a social networking platform and to determine how these metrics can be evaluated and optimized using event sourcing-based data. The work explores methodologies for KPI identification and relevance assessment, and it details the technical implementation of a web application for data analysis and visualization.
- Identification and selection of relevant KPIs for a social networking platform.
- Methodology for determining the relevance of identified KPIs.
- Data analysis and visualization techniques using event sourcing.
- Implementation of a web application for KPI monitoring and optimization.
- Application of statistical methods (e.g., Mann-Whitney U test) for data analysis.
Zusammenfassung der Kapitel (Chapter Summaries)
1. Introduction: This introductory chapter establishes the thesis's objectives: identifying relevant KPIs for a social networking platform and optimizing their evaluation using event-sourcing data. It provides background on data science, data analytics, KPIs, North Star Metrics, and introduces the platform "Clye," laying the groundwork for the subsequent chapters' deeper dives into methodology and implementation. The chapter clearly defines the scope of the research and the approach taken throughout the thesis.
2. KPIs: This chapter focuses on the identification and rationale behind selecting specific KPIs for the social networking platform. It delves into the criteria for choosing relevant metrics, outlining a systematic process to justify the choices made. This chapter presents the core set of metrics used in the later data analysis and lays the foundation for interpreting the results presented in subsequent sections.
3. Methods, Patterns and Tools: This chapter details the methodological approach and technical tools employed in the thesis. It explains the statistical methods, such as the Mann-Whitney U test and p-value calculations, used for data analysis. It also discusses the event-sourcing architecture, emphasizing its role in data collection and processing. Further, it introduces the dashboard and visualization tools (Plotly, Python) used for data representation and interpretation.
4. Data analysis execution: This chapter presents the practical application of the methods and tools described in the previous chapter. It covers data preprocessing techniques, the implementation details of the Dashapp (a web application), and the different dashboards created (Funnel, Network, Retention). This chapter moves from theory to practice, demonstrating the application of the chosen methods and the analysis of actual data from the platform. The design and email experiments conducted to test hypotheses are also detailed here.
Schlüsselwörter (Keywords)
Key Performance Indicators (KPIs), Social Networking Platform, Event Sourcing, Data Analytics, Data Visualization, Mann-Whitney U Test, p-Value, Web Application, Python, Plotly, A/B Testing, Data Preprocessing, Metric Optimization.
Häufig gestellte Fragen
What is the purpose of this document?
This document is a comprehensive language preview outlining the structure and content of a research thesis. It includes the table of contents, objectives, key themes, chapter summaries, and keywords.
What is included in the table of contents?
The table of contents provides a detailed overview of the thesis's structure, including chapters on introduction, KPIs, methods, patterns, tools, and data analysis execution. Each chapter is further broken down into sub-sections for specific topics such as data science, Clye (the platform being studied), KPI identification, statistical methods, data preprocessing, and the development of a Dashapp.
What are the main objectives and key themes of the thesis?
The thesis aims to identify relevant Key Performance Indicators (KPIs) for a social networking platform and determine how these metrics can be evaluated and optimized using event sourcing-based data. Key themes include KPI identification, relevance assessment, data analysis and visualization, web application implementation, and the application of statistical methods.
Can you summarize the chapters?
Chapter 1 (Introduction): Sets the objectives, provides background on data science, data analytics, KPIs, North Star Metrics, and introduces the platform "Clye." Chapter 2 (KPIs): Focuses on the identification and rationale behind selecting specific KPIs for the social networking platform. Chapter 3 (Methods, Patterns and Tools): Details the methodological approach and technical tools employed, including statistical methods (Mann-Whitney U test, p-value), event-sourcing architecture, and visualization tools (Plotly, Python). Chapter 4 (Data analysis execution): Presents the practical application of the methods and tools, covering data preprocessing, Dashapp implementation, and the analysis of actual platform data. It also details experiment designs.
What are the keywords associated with this research?
Key Performance Indicators (KPIs), Social Networking Platform, Event Sourcing, Data Analytics, Data Visualization, Mann-Whitney U Test, p-Value, Web Application, Python, Plotly, A/B Testing, Data Preprocessing, Metric Optimization.
What is "Clye" in the context of this thesis?
"Clye" refers to the specific social networking platform being analyzed in the thesis. The research focuses on identifying and optimizing KPIs for this platform using event sourcing-based data.
What is a Dashapp, and how is it used in this context?
A Dashapp is a web application developed for data analysis and visualization. In this thesis, it's used for KPI monitoring and optimization, featuring dashboards for funnel analysis, network analysis, and retention analysis.
What statistical methods are mentioned in the document?
The document specifically mentions the Mann-Whitney U test and p-value calculations as statistical methods used for data analysis in the thesis.
What programming languages and tools are used?
The programming language Python is explicitly mentioned, along with the data visualization library Plotly. The document also refers to the use of "tools for data analysis" in general, likely encompassing a broader range of software and libraries.
- Arbeit zitieren
- Robin Lamprecht (Autor:in), 2021, Identification and Calculation of Relevant KPIs to Optimize a Social Networking Platform Using Eventsourcing Based Data Analysis, München, GRIN Verlag, https://www.grin.com/document/1254429
