The Machine Age of Customer Insight
Bibliographic record
Abstract
The upcoming machine age offers a unique opportunity to gain novel, in-depth customer insights and to unleash enormous potential in various business areas. The abundance of data and the pace of progress in transforming data into actionable knowledge affects players across nearly all industries. This book offers a short pit stop in the race for customer insights and insight-based decision making through machine learning tools. It summarizes recent developments in business and academia concisely and offers readers proven practical guidance in what is about to become the new normal. \n \nOutstanding authors from innovative firms and renowned universities provide a comprehensive overview of the transformation of customer insights, the tools needed to generate these insights, and the success factors to thrive in the new age. Their contributions underpin the key message: The machine age of customer insight requires well-founded, data-based decision making, consistent execution and—more than ever—continuous and fast learning. This book aims to provide support to those who feel the need to make the most important first step: to embark on this learning journey. \n \nWe organized the journey in three stages. The first part addresses the question: How is the field of customer insights being transformed? First, Einhorn and Löffler from Porsche illustrate the transformation process and highlight the importance of dynamic capabilities, particularly in the automotive industry. Then, Picareta, Weissheim, and Klöhn from Salesforce show how intelligent applications have become a crucial factor for success in modern sales organizations. Next, Neudecker et al. from Kantar look at how new technologies such as voice and facial coding can contribute to a better understanding of customer emotions. Guedes, Akinwale, and Fontecha from Credit Suisse provide an overview on how machine-driven content marketing can assist in targeting customers in the banking industry. Finally, Ottawa from Deutsche Telekom highlight the emergence of 5G and its importance in collecting customer data. \n \nThe second part of the book explores the question: Which tools are necessary to generate customer insights? First, Lantz from the University of Michigan provides an overview of analytical tools that can be applied to gain customer insights. Then, Wang, Czerminski, and Jamieson from Harvard University explain some of the key features of deep neural networks and aspects of their design and architecture. Next, Hartmann from the University of Hamburg showcases how the power of decision tree ensembles can be harnessed based on a practical use case. Kwartler from Harvard Extension School distinguishes and defines text analytics and natural language processing and shows their value-adding practical application. Finally, Hofstetter from the University of Lucerne presents a concise six-step data scraping process to exploit the business value of online data. \n \nThe third part of the book explores the question: How can the management of customer insights lead to success? First, Jakobi, von Grafenstein, and Schildhauer from Humboldt University Berlin argue that a well-designed privacy and data protection process is a key element for customer experience management. Then, Temkin from Qualtrics explores how success in the experience economy can be guaranteed by utilizing experience data. Next, Khan from SAP examines the data value equation and shows how it can generate business value. Zimmermann from the University of St. Gallen provides an overview of competition data science platforms and assesses their business potential. Blache et al. from Deutsche Bank introduce the KontoSensor as a tool for processing data which creates value for both businesses and customers. Finally, Frank from Ted Frank Strategic Story Consulting shows how applying story telling techniques contributes to a better understanding of data. \n \nThe machine age of customer insight is not only an exciting era of its own—it is also a key element for transforming customer insights into business value. The current book affirms everyone who considers this era as a great opportunity while hopefully convincing those who are still skeptical.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".