With each passing day, machine learning’s business implications are becoming clearer. Machine learning is a branch of artificial learning in which systems identify patterns from data, learn from insights, and make autonomous decisions with very little human intervention.
As the number of smart devices connected to internet increase, so will the data generated by them. This deluge of data is also known as Big Data, and machine learning applies complex algorithms to understand patterns in Big Data to make decisions. Machine learning can provide real-time insights based on data, giving businesses a competitive edge over their peers. In this article, let us take a look at how machine learning is going to influence businesses across the spectrum.
Where is machine learning used?
Currently, machine learning is being used across industry verticals for business success. Here are some examples:
Three scenarios in which you can implement machine learning immediately
You can start using machine learning to consume and analyze data from unlimited sources. You can also rapidly process analyses and make predictions related to sales and marketing campaigns. In addition, you can use machine learning tools to evaluate past behaviors of customers. According to Forbes, 84% of marketing organizations currently use some form of machine learning or AI to enhance their services.
Use cases
Example 1: Azure Machine Learning can be used to analyze customer churn and minimize it as well. This is more cost-effective than other traditional and time-consuming methods to minimize customer churn. Interactive Pricing Analytics Pre-Configured Solution (PCS) is a Microsoft Azure machine learning solution that helps to determine the pricing elasticity of every product that you may sell. In other words, this tool can be used to offer contextually relevant pricing.
Example 2: Salesforce Einstein is a great example of what machine learning and AI can do to enhance existing CRM solutions. Salesforce Einstein can be used to implement predictive lead scoring, and the tool looks at various demographic and behavioral data sets. It can also help recommend products to your customers based on their interests, and to cross-sell and up-sell products more effectively.
Most businesses rely on corrective maintenance to fix machines and applications. Corrective maintenance requires one to wait until an issue arises, but the costs in downtime, unscheduled maintenance requirements, and labor can increase the overall expenditure exponentially. Some businesses have begun to use preventive maintenance, which urges customers (and their own staff) to replace spare parts regularly or to ensure certain security and upgrading protocols for software tools. Even scheduled downtime and under-utilization of spares before their full lifetime can result in unnecessary losses.
Machine learning helps businesses to undertake predictive maintenance at the right time, whether onsite or for customers. It is the smartest way to ensure that equipment and systems are used to their full lifetime and that problems are identified before they cause issues. You can implement predictive maintenance to reduce over-corrective maintenance, scheduled downtime, and labor costs by analyzing user data and identifying when interventions need to be taken. Specific benefits include:
Azure Machine Learning and Microsoft Azure AI platform can help in the predictive maintenance of both onsite infrastructures and provide support for customers.
An important function of machine learning in businesses is to detect fraud and enhance security. Machine learning technology can be used to manage portfolios, engage in algorithmic trading, underwrite loans, and detect financial fraud.
Here are a few ways you can implement machine learning to enhance security:
Implementing machine learning in your business
Before you implement a machine learning model, follow these steps for a customized solution:
Machine learning does something for every business
With proper planning, you can implement machine learning to enhance sales and marketing campaigns, make better product recommendations and build new products. You can also protect yourself and your customers from financial fraud, and ensure IT security. In addition, you can quickly increase CSAT metrics by offering predictive maintenance to enhance customer satisfaction. There are many other scenarios in which you can use machine learning, based on your unique business situation. To learn how you can custom-implement machine learning to suit your specific needs, contact us today.
