The case of professional golfer Nick Watney and how WHOOP saved the PGA Tour
When Nick Watney woke up on the morning of June 19, 2020, the RBC Heritage Golf Championship’s second tournament day, he went through his typical morning routine. As he checked his phone, he recognized that something was off. For about 1 year he had been using the WHOOP fitness strap to track his strain, recovery and sleep based on the wearable’s advanced technology that includes sensors measuring heart rate and motion. It has become a tool of his performance management system, including the analysis of sleep quality every morning through the metrics shown by the WHOOP app.
Specifically, one metric he inspected that Friday morning, the respiratory rate, made him wondering. The respiratory rate is the amount of breaths taken per minute during sleep and is calculated by the WHOOP sleep coach function besides several other metrics like the hours spent in the different sleep phases. …
For Telco companies it is key to attract new customers and at the same time avoid contract terminations (=churn) to grow their revenue generating base. Looking at churn, different reasons trigger customers to terminate their contracts, for example better price offers, more interesting packages, bad service experiences or change of customers’ personal situations.
Churn analytics provides valuable capabilities to predict customer churn and also define the underlying reasons that drive it. The churn metric is mostly shown as the percentage of customers that cancel a product or service within a given period (mostly months). If a Telco company had 10 Mio. …
In the article “Primer on Most Important Machine Learning Methods” I give an overview of common Machine Learning approaches that are differentiated and frequently used in the field. Three main dimensions are considered here:
- Human Supervision: Elaborates on how supervised, unsupervised, semisupervised and reinforcement learning works, specifically with regards to predefining the shape of the outcomes.
- Online vs. Batch Learning: Discusses the difference between incremental learning “on-the-fly” and training a model based on a static data set.
- Instance-based vs. Model-based Learning: Highlights the differences in the learning approaches of clarifying the explicit comparison with previous values versus developing a logic that is used by a model to generalize. …
Machine Learning is already present in our daily lives and an essential part of many products and services we use on a regular basis. Companies use Machine Learning to create new amazing offerings, make their existing products and services better, and solve a broad range of business problems. As companies hurry to use Machine Learning to their advantage, they focus large parts of their transformational efforts and budget on using these technologies to enable growth.
To successfully apply Machine Learning in a business context, it is key to understand the differences of the approaches, regardless of the professional background or previous experience with Machine Learning. Knowing when to use which method and how to get started, can help to detect and realize growth potential. When dealing with different types of challenges, being able to find the right technique for a specific use case is crucial to success. From classification of images or prediction of marketing campaign impact, knowledge about different Machine Learning approaches helps to head in the right direction. …
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