How Businesses Leverage Machine Learning
The speed of technological development can make it easy to take technology for granted. However, some of today’s most innovative machine learning applications were not possible a decade ago.
Take, for example, the AI tool Salesforce Einstein. It analyzes customer interactions and builds detailed profiles of individual customers. It can identify crucial moments in the sales process and improve lead scoring, customer service, and opportunities. And while we might take these developments for granted, they are practical and essential for businesses.
What is Machine Learning?
Machine learning (ML) is a powerful technology that can help predict business performance. This technology is already being used in companies across industries, from manufacturing to oil and gas. Most businesses depend on machinery to run, and downtime can quickly become expensive. ML can help detect any problems before they happen. These benefits are great for companies of all sizes. Let’s take a look at the ways in which ML can help your business.
Machine learning helps businesses understand customer behaviour better. For example, it can be used as a spam filter. The more emails a machine learning algorithm analyzes, the more accurate it will be at filtering spam messages. Another use of machine learning is for threat assessment. It can identify vulnerabilities, predict attack vectors, and even help with legal research. Businesses can use this technology to better target individual customers and enhance their products, services and client retention.
Case Studies
With the popularity of machine learning growing every day, it is only natural that companies are looking for ways to apply it to their businesses. Using a machine learning algorithm to analyze a large set of data is becoming increasingly important for many companies. This technology can improve many areas of business, including spam detection, data analytics, and customer service. Here are some examples of businesses harnessing this technology’s power. Let’s take a look at two recent examples. A labour-hire company is using machine learning to automate the candidate interview process. On the other hand, a software company has made machine learning its core technology and wants to use it to speed up its workflow.
Sky UK, a UK-based company, is transforming customer experiences with artificial intelligence and machine learning. The company’s customer base is very diverse—there are more than two million different types of customers. By analyzing customer data, the company can identify positive customer behaviour and high-converting ‘lookalikes’ — and improve the experience for their existing customers.
Applications
There are a variety of applications for machine learning in business. Machine learning algorithms can be applied in many industries, from developing an operating system for autonomous vehicles to forecasting customer behaviour. They can also help companies and enterprises to better understand their customers through data-driven algorithms that learn to associate actions over time. These algorithms can be extremely helpful for marketing teams, assisting them in tailoring their product development and customer service campaigns. Here are a few of the most common applications of machine learning in business.
One of the main goals of machine learning in banking is to detect fraudulent activities. Machine learning algorithms can scan vast amounts of transactional data and determine patterns that might indicate a fraudulent transaction. Once this process is complete, the fraud transaction is rejected or blocked immediately. In some cases, the machine-generated algorithm can even improve existing processes. However, the most important applications of machine learning in business will come in the future when AI is widely implemented and widely used.
Costs
There are a lot of costs associated with the use of machine learning in businesses. For one thing, hiring IT professionals with deep knowledge of machine learning is costly. Those who can develop these solutions are hard to find. As a result, big companies harvest those with the necessary skills and pay them fat salaries. The combination of this problem with high expectations of the earnings of the specialists leads to increased employment costs for businesses.
When determining the scope of a machine learning project, it’s crucial to consider the costs of short and long-term benefits. While presenting costs as Total Cost of Ownership (TCO) can be challenging, the costs should be delivered in terms of TCO per model and short-term savings. These costs will vary widely based on the benefits of machine learning, including improved customer experience, improved revenue streams, and more. It is also important to consider the opportunity cost when evaluating ML costs in businesses.
Impact on the Bottom Line
The most tangible impact of machine learning on a business’s bottom line will be seen in marketing and sales. Companies can personalize advertisements for each customer, generating incremental revenue increases. Other practical applications of machine learning are in the supply chain, identifying causal drivers of demand and reducing inventory costs. These technologies can also assist in fraud detection. Let’s take a look at a few examples of these applications.
For instance, MHS Global uses machine learning to improve shipping. Combined with Six Sigma baselining (a specific Machine Learning algorithmic model) it develops an order-fulfillment cell that builds over 40,000 orders a day using just two robots.
The underlying technology allows it to learn from past performance, improve pick strategies and make better predictions. Machine learning is making these applications more useful than ever. In fact, it will soon become the norm. And while the impact of machine learning is not yet fully understood, it is already transforming companies’ business practices.
Remaining Competitive in an Ever-Shifting Landscape
The modern business landscape is ever-shifting. To remain competitive businesses need to become more agile and implement effective staff augmentation to help enhance all digital assets and business processes. Machine Learning and Artificial Intelligence will help businesses learn about their customers and provide custom-tailored, high quality solutions; an excellent brand experience.
If you’re searching for qualified talent that can help you elevate your business and get ahead of the competition, MALTO Group can help. With over 20 years of experience working with business enterprises, we’ve helped many organizations digitally transform and become lean, agile operations. For Staff Augmentation or Executive Leadership Coaching, call us today at 1-833-625-8647 or visit us at www.maltogroup.com
Would you like to speak to our advisors over the phone? Please submit your information, and we will connect with you shortly.