Most of the leading organizations today are increasingly relying on machine learning tools for automating their business processes and applying more advanced techniques using Artificial Intelligence. Some of the biggest technology giants such as Google, Amazon, Microsoft, Facebook, and Salesforce are increasingly using machine learning and AI methods to gain a solid competitive advantage. Machine learning is enabling businesses to expand their top line growth for optimizing core processes while improving customer satisfaction and boosting employee engagement.
Let’s look at how machine learning is transforming some of the businesses of today and leading to a new revolution in this digital era.
What is Machine Learning?
Machine learning is the ability of the computer programs in analyzing big data and extracting information from it and learn from it to make its own predictions. This technique makes use of algorithms to continuously learn from the data to gather insights, without the need to be programmed. Thus, the machine learns from the information gathered through the algorithms. With the enormous amounts of data being generated, it’s almost impossible to perform manual data analysis anymore, which has led to the increase in the usage of machine learning techniques.
For example, Amazon which has over 250 million customers including tens and millions of products relies on machine learning for making exact product recommendations., depending on their browsing habits and buying behavior of the consumer. Similarly, Target makes use of machine learning for forecasting offline shopping behaviors of customers. IBM is creating a revolution in healthcare with its use of machine learning and cognitive computing. Google is transforming the road experience for users with machine learning through its driverless cars to make the roads safer for its customers.
Key reasons for increasing adoption of machine learning among enterprises
Machine learning is increasingly being used in predictive analysis among enterprises, where it’s possible to predict customer behaviors which have higher probabilities for driving sales and marketing results. According to a recent survey, Accenture Institute for High Performance provided the results, which indicated that companies with over $500 m in sales are relying heavily on machine learning to attain high sales growth.
The survey revealed further that about 76% of the companies are aiming at high sales growth with the application of machine learning. About 40% of the companies which were surveyed were already implementing machine learning to boost their marketing and sales performance. A Mckinsey study conducted recently showed that more than dozen European banks are targeting machine learning by replacing it with statistical modeling techniques.
Here are some of the factors which are responsible for the increasing usage of machine learning:
Enormous growth of unstructured data
There is a vast amount of data that is generated regularly and most of the companies rely on almost 80% of this data to make business-related decisions. Apart from CRM, inventory management data, transporting, accounting and numerous other types of data, customer service records, call logs, e-mail records, and supply chain data comprise of predictive patterns, which were earlier missed by the companies. Today, enterprises are able to harness the power of machine learning to gain valuable insights from unstructured data to become more competitive in the market.
Extrapolation of current historical data
Weather forecasting, supply chain network optimization along with advanced simulation techniques and forecasting abilities have all enabled to the growth of machine learning among businesses. There is more emphasis on generating realistic stimulated data today using historical data which has also led to the widespread adoption of machine learning in different areas of business today.
IOT, embedded devices, and devices generating real-time data
With the growth of IoT platforms, along with embedded systems, there is a massive amount of real-time data that is available which can be used for optimizing business operations and has led to a greater usage of predictive demand forecasting. All this has led to the explosive growth of machine learning and created a necessity among enterprises to adopt this approach to boost operational efficiency. In addition, the growth of cloud storage systems has provided a safe and secure mechanism for companies to manage their data efficiently, while bringing down costs of infrastructure making machine learning more accessible to all.
How is Machine Learning creating greater value for enterprises?
Offering customized services to customers
Businesses are now able to offer better customer services by reducing the costs which make the application of machine learning more enticing to them. They can combine historical customer-related data, algorithms and natural language processing to learn continuously using these interactions. As a result, machine learning and AI-powered applications such as chatbots seemed to have gained more popularity than humans among US customers today.
Machine learning makes use of pattern recognition based on its algorithms along with models that use historical data and real-time information to detect anomalies and exceptions in many cases. This can help them spot out fraudulent transactions. Historical data related to the transactions can be leveraged to create algorithms that may be able to find out fraudulent behavior.
Enhancing customer retention and loyalty
Now it’s possible for companies to find out about customer actions, transactions, data related to social sentiment, to recognize customers who might have a high probability of leaving the brand in favor of their competitors. Using profitability data, enterprises can now devise strategies and come up with the best actions for retaining and personalizing the customer experience. For example, mobile phone companies are using machine learning to anticipate the customer behavior and provide personalized offers to them based on their preferences and mobile usage pattern.
Efficient supply chain management
Machine learning and predictive demand forecasting have been increasingly used by manufacturers today to offer an integrated solution for supply chain planning and management. As a result, they can use key data-driven plans that can easily adapt to the changes in demand, supply, inventory and customer requirements. Besides, machine learning also helps with the analysis of data related to logistics to forecast and reduce the risks associated with supply chain management.
With advancements made in machine learning, companies are able to use more cutting-edge applications that are helping them increase business efficiency, intelligence, agility and be more focused on their customers. Machine learning is helping them transform some of the core business processes and offering an enormous growth potential for all kinds of enterprises.