Machine Learning often referred to as “ML” is a division of Artificial Intelligence that helps create a form of intelligence allowing computers to mimic learning, thus dubbed Machine Learning. Although the term Machine Learning has been in use since 1959, it has gained popularity in recent years for its use in innovations made in various sectors including security and marketing.
Supervised Learning is the process of connecting an input to an output. An example of this is using flashcards to study for a test: where one side prompts a response, the other side supplies the response. Similarly, Machine Learning uses a supervised learning algorithm where data is examined and a corresponding output is created and adhered to. In an ideal situation, the algorithm would correctly select the correct values for the labels in unseen circumstances, thus essentially ‘thinking on its own’.
Ensuring that the correct output is given when prompted by an input is dependent on creating an effective algorithm. Creating an effective algorithm is critical for reinforcement learning. So this and Supervised Learning are probably the most human relatable concepts of machine learning but of course that’s what these methods are based on for many principles of Machine Learning and Artificial Intelligence were designed and implemented based on human interaction and psychological principles like how humans learn subjects or how we learn from various interactions and experiences in life this is exactly how machine learning works.
Clustering is the method of collecting filtered results based on the data output of the algorithm thus providing a specific group that meet the required parameters that are the input. This is in fact a common thing we see everyday. Particularly you see clustering every time you filter results for something you’re looking for online because when you filter search results of a product, for example, you could select a variety of parameters that fit your requirements and it then creates a list based on that input thus creating a smaller more specific list of products that better match your needs from the thousands of different products.
Machine Learning in Marketing?
Now, what role does Machine Learning play in Marketing? Most marketing firms have a goal of achieving the goal of the client thus the use of precise analytics and strategies to meet premium results, but in today’s world most firms now do this using software powered with Machine Learning to collect information relevant to a client’s needs; such as, if a client asked a marketing firm to market their New York restaurant the firm would need to collect a relevant audience to market to, so this firm would need to first find a niche that would be most likely to actually visit the restaurant and then market to that niche, in the old days most marketers would use mass marketing i.e (Mass news, newspapers, etc;) but we now know that precise niche marketing works better; so now what part does Machine Learning play? To start, Machine Learning uses several methods such as clustering and reinforcement learning and what those do is produce results based on the parameters provided so in the example of the restaurant lets say the venue is located in Manhattan and they want customers from Manhattan so when the firm starts researching a niche they know they want people who are in the Manhattan area they might also look for other factors like the age of the people and with those known parameters the A.I can produce the best output of people who meet the parameters and from that the firm knows how to market the clients content. In sum, Machine Learning plays an important role in the accuracy in marketing and will continue to play an even higher role in the near future.