Building a customer feedback analyzer

• Titel: Building a customer feedback analyzer
• Mitwirkende: Schrooten, Bernd [MitwirkendeR]
• Institutionen: Packt Publishing, [Verlag]
• Ausgabe: [First edition].
• Verlagsort: [Birmingham, United Kingdom]
• Verlag: Packt Publishing
• E-Jahr: 2025
• Jahr: [2025]
• Umfang: 1 online resource (1 video file (28 min.))
• Illustrationen: sound, color.
• Fussnoten: "DataLab" : DataLab is a learning solution designed to empower individuals and teams to explore data, build skills, and solve real-world problems. With interactive content, hands-on exercises, and a focus on practical application, DataLab helps learners turn data curiosity into confident capability—no matter where they start. - Online resource; title from title details screen (O’Reilly, viewed April 9, 2025)
• ISBN: 978-1-80602-569-5
• ISBN: 1-80602-569-8
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Intelligence artificielle ; Applications en marketing
• Sach-SW: Intelligence artificielle ; Applications industrielles
• Sach-SW: Service à la clientèle
• Form-SW: Instructional films
• Form-SW: Nonfiction films
• Form-SW: Internet videos
• Form-SW: Films de formation
• Form-SW: Films autres que de fiction
• Form-SW: Vidéos sur Internet
• K10plus-PPN: 1924459930
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4718960061
• Lokale URL Inst.: Zum Volltext

Abstract

Understanding customer feedback is essential for informed business decisions, but analyzing large volumes of text data can be challenging. By combining data analysis, sentiment analysis, and AI-driven text summarization, you can quickly extract insights and transform raw feedback into actionable intelligence. Integrating AI into your workflow can streamline the process, saving time and improving accuracy. In this code-along, we'll build a Customer Feedback Analyzer using real-world data from a hospitality business. You'll learn how to measure customer sentiment with Net Promoter Score (NPS), perform text analysis to uncover trends, and visualize results for better decision-making. We'll also use AI-assisted coding to generate Python scripts that automate analysis, making business intelligence tasks more efficient. By the end of this session, you'll have a practical tool to analyze customer sentiment and drive data-driven improvements.