Machine learning is a subset of AI that uses algorithms that mimic human learning by providing machines with datasets. From these datasets, machines can learn various tasks ranging from forecasting to data analysis.
Businesses use machine learning to improve their decision-making processes, making data-driven decisions that impact the entire company. With the help of machine learning, businesses can start understanding their data and making predictions about what might happen if they change some aspects of their business. For instance, what would happen to a business if it stopped selling one of its products? Machine learning can tell them based on previous sales data.
Data is at the heart of any machine learning process, so you might wonder why more companies aren't using it. Machine learning and AI require massive amounts of training data to learn, and with enough data, they become more accurate.
Nevertheless, AI in marketing and business can help businesses learn how to improve sales, enhance the customer experience, and plan for the future.
Let's take a look at a few of the ways machine learning improves decision-making.
Predictive analytics is another subset of AI that's based on statics. It makes predictions based on the data it has, providing you with better business intelligence insights. For instance, if you give it sales data, it can make sales projections for the next month, quarter, or year. Predictive analytics uses predictive modeling to use historical data to predict something that may happen in the future.
It can be used to predict anything as long as you have historical data. For instance, financial companies might use it to determine when to sell a stock based on past market behavior.
ML can also predict the performance of a marketing campaign and how likely it is to convert customers based on past purchases and behavior, ultimately measuring the performance of a campaign that hasn't happened yet.
Machine learning can also be used to segment customers based on various data points. For instance, AI can group customers with similar characteristics together based on demographics and attitudes. However, when you add in customer data you collect from your online store, it can be used to segment customers based on past purchase behavior.
For instance, Mailchimp uses predictive analytics to analyze past purchase behavior and predict contacts with a high, moderate, or low customer lifetime value (CLV). Then, those predictions are used to segment your customers automatically.
AI technology can automatically find patterns in customer data the human brain can't, allowing you to segment them based on the information you didn't even know existed and create more personalized marketing campaigns.
Using machine learning for customer segmentation increases efficiency and is highly scalable. Manual methods of combing through customer data to find similarities might work for small businesses, but it's not efficient enough when you have tens of thousands of customers.
Fraud detection refers to IT processes that prevent fraudulent payments. Unfortunately, many fraud protection tools have high accounts of false positives, which prevents real customers from being able to do business with you.
For instance, large orders were considered more likely to be fraudulent, blocking transactions over a certain amount. If your fraud detection system blocks customers automatically based on order quantity or sales amount, you can't determine whether any of those orders were from genuine customers.
AI technology solves some of the problems related to outdated fraud detection programs. Additionally, it works faster than most of those programs, giving you results immediately after receiving an order. ML fraud detection is also more scalable, allowing you to increase transaction volume by providing it with more data. But that's just the beginning.
Using machine learning for fraud detection is more accurate, which means you're not potentially blocking genuine customers. These technologies learn from patterns and can adapt to changes faster than human intelligence. Therefore, it can identify suspicious or fraudulent transactions even faster to protect your business.