Machine learning and AI are quickly becoming essential tools for optimizing and automating business processes that previously took up valuable financial resources and large amounts of time. The power of machine learning can be used for a wide range of purposes and transform how computers both handle and leverage data. Knowing how to apply machine learning to your apps will ultimately boost your business.
Machine Learning and AI can help you grow and streamline your business in a diverse number of ways. Without realizing it, your business has access to enormous amounts of data that can be easily leveraged by machine learning to boost your business.
Companies find that machine learning can help them to personalize their interaction with their consumers and target market. Through technology predicting patterns and behavior, machine learning ultimately boosts sales, suggests relevant marketing content and results in a more seamless customer relationship.
Machine learning can also help your business eliminate data security risks, prevent fraud and detect any hazards before they become a problem.
Machine learning can help your business overcome one of the biggest challenges all companies face - customer value prediction.
While all businesses have huge access to different consumer-related data, few businesses actually know what to do with this data and how to use it to their advantage to generate revenue.
Machine learning can be effectively used to help businesses predict customer behavior purchasing patterns, and send customers relevant and targeted offers that are based on their browser and purchase history.
With the huge volumes of quantitative data, financial industries are easily able to utilize machine learning. AI is already well established in finance.
Algorithmic trading, portfolio management, fraud detection, and loan underwriting are a few features that are already available to financial industries. An example of where AI can truly excel is in stock market forecasting. With the ability to analyze millions of stock data units simultaneously, AI can come up with an accurate prediction quickly.
Machine learning can help you improve that all-important customer loyalty as well as secure a better customer experience. If your business relies on customer service executives to deliver help to customers, machine learning can help assign the correct individual to solve the problem.
Analyzing previous call records, customer behavior, and the request, this data is used to match the right customer service executive to the customer’s problem. In this way, machine learning will speed up the response time to customer service requests and reduce the time and money spent on a successful customer relationship.
Machine learning can also be implemented in the form of sending a query to tech support or used as a chatbot to help direct customers to the right place.
Machine learning approaches are usually split into three categories and generally fall under supervised or unsupervised methods. Categories are divided based on the type of ‘signal’ or ‘feedback’ available to the learning system. Choosing the right approach can make a huge difference in the end result of your application.
Using known data and input examples, the computer can apply what has been learned in the past to learn a general rule to make predictions about future output values. This type of approach results in shorter development schedules and provides a “ready-to-go” app, but is only suggested for simpler applications.
The learning algorithm is left alone to train itself. Information is left unlabeled and the learning algorithm can discover hidden patterns in data and structure its own input. This type of approach is better for more complex applications when making the right labels would take longer than it would for the system to figure it out.
A learning method that interacts with a dynamic environment, discovering both errors and rewards. The learning method must perform a specific task like playing a game. Using trial and error, the learning system works out patterns that will help it to receive the maximum reward. This type of approach is only recommended when there is a longer release schedule and internal beta testing is viable.
Outsourcing machine learning software development projects to Latin America is often a cost and time-effective strategy without sacrificing high-quality work. Although some businesses might think they prefer to have an in-house software and application development team, this is often an expensive and impractical strategy. Nearshore outsourcing to Latin America, to a country like Argentina, known for its hub of strong IT talent, will bring your business the results it needs.
Interested in learning more about nearshore outsourcing? Download our infographic, detailing the main characteristics of Latin American software engineers by clicking here, or schedule a call with one of our executives.