The Role of Machine Learning in Businesses
In today’s digital age, the various tools of technology are being utilized in our everyday lives. From reading the online weather reports to business decisions, we have been relying on technology to improve efficiency. These day-to-activities are powered by an innovative idea called machine learning that has been unlocking the door to further advancements. Owing to the popularity of machine learning, it has turned out to be a buzzword for businesses.
Before we delve more into the depths of machine learning and its impact on businesses, let’s find out more about what this term means.
What is Machine Learning?
Machine learning is a type of artificial intelligence that enables computers to learn without programming them explicitly. In simpler words, this technology allows computers to learn by focusing on the development of programs that provide computers the ability to teach themselves and grow when these programs are exposed to new data. These programs are designed to detect patterns in the new data and make changes to the algorithm accordingly to provide the most relevant results to users.
From the basic machines to the advanced computers, access to big data has been one of the primary breakthroughs that have led to the increased effectiveness of machine learning.
Such advancement has been beneficial to businesses in a number of ways. Machine learning helps in bringing enhanced predictive accuracy to business data. As a result, this helps in adding value to business decisions and increasing profitability considerably.
Business Implications of Machine Learning
The value of machine learning has been strategically changing the way businesses operate. Let’s take a look at some of the business implications of machine learning.
#1. Improved Predictive Analysis
Various insurance companies use complex statistical models that utilize customer details to predict claims. With machine learning, these companies can do away with such complicated models for these tasks.
For example, certain auto insurers have already started using driving habit as one of the major factors to price a vehicle. In fact, many ask buyers to install certain devices within the vehicles to monitor and collect metrics related to their driving habits. This data is used to predict the possibilities of accidents, create personalized insurance plans and so on.
Apart from that, machine learning also helps doctors predict diseases accurately and administer the right treatment accordingly.
#2. Relevant Search Results
One of the best examples of machine learning is improved search capabilities of search engines. Machine learning algorithms implemented in the ‘Search’ field of e-commerce sites can help in retrieving improved and more relevant results for buyers. A learning search function will better understand what the user meant and will produce results accordingly instead of focusing on just what the user typed.
Such improved search increases the possibility of conversion and sales significantly, thus generating revenue for the business.
#3. Improved Customer Service
Meeting the rising customer expectations is one of the most effective ways that businesses implement for better outcomes. Customers are quite likely to have queries about the products and/or services they pay for. Responding quickly to their feedback and concerns is the best way to provide guidance. That being said, it may be challenging for small businesses to monitor these and provide real-time response. This is where machine learning becomes relevant.
Intelligent chatboxes can be used to manage the basic customer service queries. Machine learning will enable these chatboxes to assist customers and guide them through the processes that they are doubtful about. These can also be used for social media requirements.
#4. Real-Time Personalization for Buyers
Delivering the right messages at the right time to the customers is key to enable them to move through the sales funnel. Marketing campaigns rely on timeliness and relevance. Machine learning can help in providing timely insights about customer details and preferences.
Tracking buyers’ digital behavior can help in identifying the topics that customers are interested in. As a result, businesses can help in providing personalized services to their clients.
#5. Defining Target Markets
Using big data to facilitate machine learning can not only help in accurately identifying the target market, but also the customer profiles relevant to the products and services provided by a business. The ability to narrow down customer profiles can help in creating well-targeted marketing campaigns.
Industries That Have Implemented Machine Learning
Machine learning has been gaining momentum over the years owing to its effectiveness. It is increasingly being implemented in a number of industries. Financial services have a treasure trove of big data that can be utilized to provide a comprehensive platform for machine learning. This can provide investment firms to produce predictive models regarding when to trade, risk assessment analysis, asset allocation and so on.
The retail industry has had a lot to benefit from machine learning. The heaps of customer data collected has helped in predictive inventory planning, targeting customers according to their preferences and so on.
Machine learning and big data have been beneficial for the marketing and advertising sector. Machine learning has been instrumental in transforming conversions to sales. Recently, a survey was conducted on the enterprises with at least $500M in sales. It was found that 76% of the companies have been targeting machine learning for higher sales growth and 40% of the enterprises are already using machine learning for the same purpose.
Apart from that, manufacturing, healthcare, travel and other industries have also been utilizing machine learning to improve their business operations.
Collecting Data for Machine Learning
Since machine learning relies largely on business intelligence, collecting customer data is a major part of this process. There are various data accumulation methods that businesses can implement. For example, offering WiFi to customers visiting the business premises in exchange of basic customer details can prove to be extremely beneficial.
In addition to that, conducting online surveys and quizzes that accompany discount vouchers or similar offers at the end can be quite effective too. Business transactions with customers can provide access to a pool of valuable data.
In addition to big data, machine learning is also powered by the increased adoption of Internet of Things (IoT). The implementation of the large-scale data sets accumulated by these technologies will fuel further augmentation of machine learning in businesses.
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