On a similar note as numerous other cutting-edge technologies of the modern-day era, machine learning was once science fiction. Nevertheless, AI and machine learning technologies have moved from the stuff of science fiction to a staple of modern businesses, as companies across almost all industries have begun to implement these technologies to make their tasks and operations more efficient, feasible, and precise than ever before.

Powered by data science, machine learning brings many new opportunities for businesses of all industrial sectors. For example, retailers are utilizing machine learning to secure the right merchandise to the suitable shops at the right time, healthcare professionals are using it to diagnose and treat their patients accurately, and researchers are making the most out of AI and machine learning technologies to create new, more effective medicines. And that is just a fragment of all machine learning applications that are constantly emerging, as all industries from banking and retail, to energy and utilities, to travel and hospitality, and more, increasingly put AI and machine learning technologies to work.

Professionals agree that machine learning technologies permit organizations to perform their everyday operations on a scale and scope previously impossible to achieve. This makes it easier to help them speed up the work pace, reducing mistakes and improving their accuracy, thereby assisting employees and customers alike. Even more, innovation-oriented companies are tirelessly working to find new ways and harness machine learning not only to drive efficiencies and improvements but to fuel new business opportunities that can separate their companies in the ultra-competitive marketplace.

For that reason, understanding the business opportunities and possibilities of AI and machine learning technologies is vital for companies to plot a course for the most efficient ways of utilizing AI and ML in their business. Below, we’ll share the top ten applications of machine learning technologies in today’s business world and how they can create new opportunities for your business by solving problems and delivering tangible business benefits.

Powering Up Ticketing Management Systems 

Despite the fact that ticket management systems don’t resolve all customer service concerns and still require many manual processes along the way, they are definitely huge time-savers for companies by consolidating information and gathering data for future use. Ticketing management systems make handling customer requests easier by consolidating tickets and their history, ticket categorization, tracking ongoing requests to ensure that they’re adequately handled and successfully closed, sending alerts when they’re overdue, allowing customer service agents to mark when they’ve taken over requests, and offering unmatched customer service personalization by storing prior data about each customer and their experience with customer support. Put differently, ticketing management systems are interactive tools that help organizations systematize information, alert agents of work that needs to be done, and provide real-time status updates regarding completed tickets.

For the reasons stated above, each organization that deals with more than just a handful of customer tickets a day should have a ticketing management system to save money and time. Nevertheless, to get the most out of them by automating processes even further and improving ticket analysis, sophisticated ticketing systems need to be equipped with artificial intelligence, especially machine learning. So, when you decide to leverage machine learning-powered models like Y Meadows to work in concert with your existing ticketing system, your ticketing management system will be able to: 

• Automatically route tickets, as once they get tagged by machine learning, they will be instantly routed to the right agent or department; 
• Automatically tag tickets with more accuracy by topic, sentiment, department, urgency, and more. 
• Incorporate a customer feedback system to follow up with your customers and notify them when you’ve instituted changes or acknowledge that you’ve received their feedback. 
• Draw relevant info and understand difficult worded requests to resolve them automatically.
• Resolve simple requests based on prior interactions, like updating knowledge bases and resetting passwords. 
• Manage team member performance. 
• Comprehend the voice of the client, follow their entire customer journey, and target specific individuals. 
• Gather and analyze customer ticket data to drive better business decisions, thus improve your products or services, decrease customer churn, and increase profit margins.

Better Business Decision Support 

Providing better business decision support is yet another area where AI and machine learning technologies can help organizations turn the plethora of collected data into actionable insights that provide value. Here, algorithms trained on historical data and other relevant data sets can analyze incoming information and run through multiple possible scenarios at a speed and scale impossible for humans to make better decision recommendations on the best course of action. Machine learning technologies don’t replace people but rather help them do things better, faster, and more effectively. 

Some examples of how machine learning technologies are used for better business decision support in different industry sectors include: 

• In agriculture, ML-enabled decision support tools integrate data on energy, climate, water, resources, and other essential factors to help farmers make better decisions on crop management. 
• In the healthcare sector, clinical decision support tools that use machine learning guide healthcare professionals on diagnosis and treatment options, improving caregiver efficiency and patient outcomes. 
• In business, machine learning-powered decision support systems help management foresee trends, identify problems, speed up decisions, and notice new business opportunities along the way.
  

Enhancing Customer Recommendation Engines 

Among other things, AI and machine learning technologies can power up customer recommendation engines to enhance the overall customer experience and provide hyper-personalized experiences to customers, giving fresh opportunities to companies to grow their reach even more. In this particular use case, machine learning-driven algorithms process data points about each client, like the client’s previous purchases, and other data sets like the company’s current inventory, demographic trends, and the client’s purchasing history to determine which products or services should be recommended to that client. 

For instance, colossal eCommerce corporations like Walmart and Amazon utilize ML-powered recommendation engines to personalize and expedite the entire shopping experience. Another well-known deployer of this machine learning opportunity is Netflix, which uses the client’s viewing history to deliver highly personalized recommendations. YouTube also uses recommendation engine technology to help users instantly find other videos that fit their preferences.

Customer Churn Modeling 

Another way companies utilize AI and machine learning technologies is to anticipate when a customer relationship is starting to sour and find ways to address it and fix it. In this manner, the latest machine learning capabilities help organizations deal with one of the oldest historical business issues—customer churn. 

In this business opportunity case, unique algorithms identify patterns in enormous volumes of demographic, historical, and sales data sets to pinpoint and understand why the business loses particular customers. Afterward, the company can use AI and machine learning technologies to analyze customer behavior and alert which customers are at the risk of taking their business elsewhere, uncover the reasons behind their wish to spend their money elsewhere, and determine which steps the company should proactively take in order to retain them. 

Churn rate is a crucial performance indicator for all businesses, but it’s particularly significant for service and subscription-based companies. Examples of corporations that use machine learning for customer churn modeling include big media companies, movie and music streaming corporations, SaaS companies, and major telecom companies. 

Nevertheless, if you think that only Fortune 500 enterprises are incorporating machine learning technologies to reduce their churn rates—think again. Having a low churn rate is essential for organizations of all sizes, so if you run a subscription-based or service company, contact the Y Meadows experts, try out the demo version of our software, and find out what our solution can do to keep your customers happy and retain them with ease. 

Setting Up Dynamic Pricing Options 

Organizations can mine through their historical pricing data together with data sets on a host of other variables to comprehend how particular dynamics—from weather and seasons to time of the day—impact the demand for their products and services. ML algorithms can learn from that data and combine that insight with additional consumer and market data to assist companies in dynamically pricing their offerings based on their numerous variables. This strategy presents a genuine business opportunity for thriving companies, as it ultimately helps businesses maximize their revenue. 

The most visible example of dynamic pricing with the help of AI and machine learning technologies happens in the transportation industry when conditions like rain push up the number of people seeking rides all at once or the sky-high prices for airplane tickets during the vacation season.

Conducting Better Market Research And Customer Segmentation 

AI and machine learning technologies don’t only help organizations set their pricing options better, but they can also help them deliver the right products and services at the right time to the right place through predictive inventory planning and customer segmentation. For example, retailers utilize AI and ML to accurately predict what inventory will sell best in their stores based on multiple factors impacting a particular shop, the demographics of the region, and other data points.

Likewise, machine learning applications are utilized by companies to understand particular segments within their customer base better. For instance, retailers use the technologies to gain valuable insights into the purchasing patterns of specific buyers, whether a group based on similar incomes, ages, or education level, thinks alike. This way, companies can better target their needs, like stocking stores with the merchandise that the identified segment is most likely to buy.

Improving Fraud Detection 

Machine learning’s potential to comprehend patterns and promptly discern anomalies that fall outside them makes it a valuable tool for discovering fraudulent activity. As a matter of fact, banks and other financial institutions have been utilizing ML in this area successfully for years.

Data scientists utilize machine learning to learn the individual customer’s typical behavior, like where and when customers use their credit cards to purchase things. Then, machine learning takes this information and uses it to determine in mere milliseconds which transactions fall within the normal range and are legitimate versus which transactions are outside the expected norms and might be fraudulent.

Image Recognition And Image Classification 

Organizations are also turning to AI and machine learning technologies like neural networks and deep learning to help them make more sense out of images. These technologies that fall under the AI’s umbrella have broad applications, from Facebook’s and Instagram’s desire to tag photos posted on their platforms, security teams’ will to identify criminal behavior in real-time, and automated cars’ need to see the road ahead.

Lately, retailers also make the most out of image recognition and classification for equipping robots with computer vision and ML to scan shelves and determine what goods are low or out of stock, ensure that all items are removed from shopping carts and forbidden for purchase, and detect workplace safety violations.

Strengthening Operational Efficiencies 

Despite the fact that most machine learning applications are highly specialized, many organizations incorporate the technology to better handle routine business operations and processes, like software development or financial transactions. Therefore, AI and machine learning technologies are widely used to drive unmatched efficiency in many business departments, including: 

• Operational departments that use ML-based solutions to monitor equipment and identify in advance when repairs or maintenance are needed, thus diminishing problems and work disruptions; 
• Finance departments that use ML to speed up work and reduce human mistakes; 
• Information technology teams that utilize ML as part of their automation or software testing to improve and speed up their processes, resulting in better software at lower costs.

Outstanding Information Extraction 

When combined with natural language processing, machine learning can automatically determine critical pieces of structured data from docs even when the needed information is kept in semistructured or unstructured formats. These days, using machine learning technologies to understand documents is a massive opportunity across nearly all industries. As a result, companies make the most out of this technology to process everything from invoices to legal contracts to tax forms, bringing enhanced efficiency and better accuracy to such complex processes and freeing the human workforce from dull, repetitive, mundane work.

Final Thoughts On How AI And Machine Learning Technologies Are Bringing New Opportunities For Businesses 

Artificial intelligence’s and its subcategories' continued rise is inevitable, so they are advancing into the workplace at a dizzying speed. The question is no longer whether you should investigate adopting AI and machine learning technologies in your company’s scope of work but how fast you can do so to stay competitive in your industry. At the same time, you should always remain thoughtful about how you’re going to apply these groundbreaking technologies to your organization, with a complete understanding of the benefits and disadvantages that come along with these technologies.