Machine Learning Case Study

Machine learning is a relatively recent technology advancement (1990s) whereby an algorithm or program scans through hundreds of thousands of data items such pictures and ‘learns’ from the data set and develops recognition for certain key elements. In short, instead of fully finished programs these programs tend to continue to take shape through training mechanisms. Businesses that do not adapt to/incorporate these changes will cease to be functional in the coming years. Pretty bold statement. Let us see why I see this happening sooner than later!! 1. How are traditional industries using machine learning to gather fresh business insights?

Let us start with two extreme examples.

1) Sporting industry uses machine learning to run through tons

However today we have far larger and diversified data sets. This has been one factor that assisted the development of machine learning. The classical statistical techniques were altered to exempt the constraints and pre set assumptions used in traditional statistics. This is what enables more accurate results as mentioned above. 1930 and 40s brought people such as Alan Turing who laid the foundation for artificial intelligence. Advances in computing power during the 1970s and 80s finally made it possible for people to explore and develop machine learning the way we know it

Traditional managers will have to get comfortable with their own variations on A/B testing, the technique digital companies use to see what will and will not appeal to online consumers. Frontline managers, armed with insights from increasingly powerful computers, must learn to make more decisions on their own, with top management setting the overall direction and zeroing in only when exceptions surface. Machine learning must be looked at in three main stages: description, prediction, prescription. The third stage being most important as the first two we have already understood and applied extensively in the market today. Here the C-suite must be directly involved in the crafting of the objectives that the algorithms attempt to optimize. Example used in article: an international bank concerned about the scale of defaults in its retail business recently identified a group of customers who had suddenly switched from using credit cards during the day to using them in the middle of the night. That pattern was accompanied by a steep decrease in their savings rate. After consulting branch managers, the bank further discovered that the people behaving in this way were also coping with some recent stressful event. As a result, all customers tagged by the algorithm as members of that micro segment were automatically given a new limit on their credit cards and offered