Quick walkthrough Machine Learning
If we travel back to 1623, when Germany’s Wilhelm Schickard invented the first mechanical calculating machine, he would have never imagined that this computing machine (now evolved as Computer) would be capable enough to compete with humans one day. It’s not just all, it also holds the capability to defeat humans. We all are aware about the famous chess tournament held at New York in the year 1997 when IBM supercomputer named Deep Blue defeated the world chess champion Garry Kasparov. So, what has given so much intelligence to a simple calculating machine? Well, It’s the so called Machine Learning. It is an algorithm or AI (Artificial Intelligence) application that helps system to learn automatically and improve on the basis of experience without being programmed to do so. It works similar to the way we humans learn after we are born – We learn either on our own as we grow up or our parents, teachers and elders teach us. Similarly, even these computing machines also learn.
When we humans train and teach machines with the help of available datasets that are well labelled and classified, we call it as Supervised Machine Learning. If machines learn themselves with the help of available datasets that are not categorized, we call it as Unsupervised Machine Learning.
Many of us wonder how machines learn by themselves or how machines can think. Do they really have something like brains to think and make decisions? Well, the answer is Yes! They have got neural networks (similar to what we have) that help them think and decide. We all know that our brains have Neurons and Central Nervous System helping us perceive and understand different situations and actions around us and respond accordingly. Similarly, there are networks of Neurons for Computers/machines and various algorithms utilized by these Neural Networks act as Central Nervous System helping machines read the data or information available to them and take appropriate decisions accordingly. One of the first algorithms developed is Nearest neighbour and it allowed Computers use various Pattern Recognition techniques. It could be used to easily map the route for any traveller starting at any city and ensure that they visit all different cities during their tour. Nowadays, Gmail Spam Folder, Facebook Messenger request suggestions etc. use such algorithms to work.
The cognitive computing power of Machine Learning enables different businesses to offer optimized and efficient services to their customers. Many enterprises are really struggling to utilize the massive amount of data that they generate every day. Machine learning, one of the fields of computational science is giving them a way with its pattern recognition techniques to gather required insights that bring predictive accuracy, greater understanding and prescriptive intelligence to enterprises’ datasets. Thus, Machine Learning helps solve even the most complex problems by making accurate predictions without any explicit programming.
How Machine Learning can help business grow and enhance customer experience?
As we already discussed that machine learning has got different techniques that seek out opportunities to optimize decision making based on the predictive value of large scale datasets. It uses its computing power to process even the exponential growth of such datasets. This is proving out to be a big change-maker for many enterprises and is fueling their growth. As per Forbes Insights and Lattice, a predictive marketing solutions provider – 86 percent of the companies that have been leveraging Machine Learning for more than two or more years have seen their marketing ROIs increase up to 50 percent. Let’s take a look at how Machine learning can really help enterprises grow.
- Machine Learning powered by statistical algorithms help building a model that can create real time optimal pricing for various enterprises using their historical product prices, preferences, customer behavior, order history and competitor prices.
- As customers interact with various websites in different ways, machine learning can generate a personalized form of engagement for customers by analyzing their past behaviors. It provides customers with better recommendations and hence allow them to make their customized decisions easily. This kind of personalization has played a crucial role in the success of Amazon, Netflix and other such large companies.
- Predictive Analytics technique of Machine Learning improves customer retention for many enterprises. It reinforces brand loyalty by almost eliminating the users’ need to go with other options.
- Machine Learning helps develop more accurate customer personas. Most of the enterprises develop personas based on transaction data and have a good understanding of their customers but they often don’t have a good understanding of the motivators (like loyalty, quality, price, etc.) that can lead to purchase. Machine learning provides ability to layer the customer data and discover more about them, hence identifying the motivators that drive the actions of customer.
- Different machine learning techniques can tap in-to the data to inform customer service team about their potentially upset customers. This helps customer service know about their unsatisfied customers so that they can try to retain them by contacting them with an apology or providing some quick offer based benefits.
- Machine Learning helps in better analysis of sales data. Sales team of different enterprises have got the ability to access metrics from social media platforms, site visits, etc. to make their decisions. But all of these data are quite unstructured and difficult to organize in real-time. So machine learning has revolutionized this process by helping find valuable info in much faster way with the help of algorithms. As a result, sales team can easily utilize these insights to focus on sales growth.
- Machine Learning helps attract more customer attention. Yes, the word – “Machine Learning” is buzzing around customers. So, if they see any enterprise utilizing it in their processes, it automatically pulls them towards the enterprise and hence fueling the business growth.
- Machine Learning can help improve fraud detection by automatically searching for suspicious or fraudulent transactions and then separating them from legitimate ones. This is achieved by monitoring some specific features in a given data set and building the models that can be utilized to flag all suspicious transactions.