Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
Everyone is wanting to learn more about how machine learning can be used in their business. What’s interesting though, is that many companies may already be using machine learning to some extent without really realising it. The lines between predictive analytics and machine learning are actually quite blurred. Many companies will have built up some machine learning capabilities using predictive analytics in some area of their business. So if you use static predictive models in your business, then you are already using machine learning, albeit of the static variety.
McDonalds mastered the upsell with one simple question at the time of purchase: “You want fries with that?”. A simple and relevant question at the right time that has likely generated millions of extra dollars in revenue through the years for the company. Ever since then, companies have tried to emulate their success by identifying complementary products in their offering and training sales staff to ask customers the right question at the right time.
In our previous blog post we looked at data analytics in collections and the expected change in performance. Strategically, data analytics drives operational execution, but the question remains: where do we start? In this blog post, I outline the 3 steps to building your own data-driven collections strategy.
Use versus abuse of statistics can often be characterised by the analytical approach adopted to the problem at hand. In this blog post, which is part of a series on Logical Fallacies to avoid in Data Analysis, I’ll be focusing on defining the motivated reasoning logical fallacy and how to avoid it in data analysis.