How Machine Learning and Analytics is Making Data Smarter
Learn How Combining Data with Machine Learning Will Derive Analytical Insights
Organizations across varying industries recognize the need to improve their analytics and machine learning capabilities in order to derive insights into current and potential customers. As reported in a recent article in Entrepreneur, “Today, the importance of machine learning and big data to businesses cannot be overemphasized; both are revolutionizing business operations and consistently providing lots of new opportunities.”
Machine learning is a type of data analysis that doesn’t require explicit programming and uses algorithms to automate the construction of analytical models. Machine learning algorithms can generate predictions from data on the fly and are capable of learning from every piece of information they observe. According to Entrepreneur, when big data is combined with machine learning, organizations can vastly improve business intelligence (BI).
Big Data and Machine Learning: A Powerful One-Two Punch
With so much data being generated every day, from smartphones to IoT, it’s no secret that organizations have plenty of data to examine, synthesize and classify to help improve BI. If organizations combine all this data with machine learning, they can derive deep analytical insights about their customers. According to Entrepreneur, the fusion of big data and machine learning enables the discovery of insights around business patterns, market forces, and customers’ behavior—all of which can lead to smarter, faster decisions reached in real-time.
For example, organizations can target customers on an individual basis by learning their particular priorities and interests. They can then personalize product offerings to each individual customer rather than mass marketing approaches to large groups of customers. Consumers are much more likely to purchase a specific product or service if it is tailored to their exact needs.
In addition, organizations can use machine learning and big data to discover individual behavior patterns and predict future customer conduct. They can detect if a customer is getting ready to churn and take corrective action, whether through a personalized offering or customized approach, in an effort to retain them. They can figure out if a customer is looking to change or upgrade their services and offer them the right cross-sell or up-sell opportunity. But, unless the data is analyzed and managed properly, none if this is possible.
An All-Inclusive Approach to Managing Big Data
To leverage machine learning, organizations need an all-inclusive, big data platform. The platform should be built to handle not one, but rather many steps, from data acquisition and preparation to data analysis and operationalization, in real-time. The platform should also be able to dissect and analyze substantial amounts of data to provide the highest value and validity of insights. And when handling vast amounts of data, the platform should have the capability to move through the data at faster speeds so as to only take minutes, rather than hours.
The platform should apply machine-learning algorithms for segmentation, classification, recommendation, regression and forecasting. Organizations can then create reports and dashboards for results analysis, easy visualization, and enterprise-wide collaboration.
By combining ample amounts and varieties of data, machine learning can provide organizations with data that is individually tailored to each customer. Let’s look to the telecommunications industry for a more specific example. An all-inclusive big data platform should be able to predict every customer’s contract length expectancy, likelihood to renew, likelihood to churn, likelihood to upgrade services, etc. Since customers are not static, different combinations of analytics are constantly updated to incorporate their changing circumstances as well as changes in external and market conditions.
With the right all-inclusive platform, organizations can successfully leverage machine learning and big data to improve the customer experience and stay ahead of the competition.
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