|Businesses are adopting AI and and machine learning to gain a competitive advantage / Credits: Vasin Lee via Shutterstock|
Today’s businesses work in a highly-competitive environment. They need to constantly prove that their products or services are more reliable than other companies. Thus, more and more businesses are now adopting artificial intelligence and machine learning to gain a competitive advantage. It is projected that the machine learning market alone will grow to $8.81 billion by 2022 from only $1.41 billion in 2017.
A 2017 study conducted by software company Salesforce showed that more than 60% of marketing leaders are planning to leverage AI to create dynamic landing pages, websites, programmatic advertising, and media buying. Over the next five years, they are expecting improvements in efficiency and advancements in personalization. In the future, experts are expecting that AI would become increasingly sophisticated, making it a powerful tool for social media marketing.
According to Supply Chain Game Changer, a world-leading blog dedicated to sharing experiences and expertise for the benefit of everyone, AI would greatly improve the process of developing relationships with customers. This can be done through personalized, real-time content targeting that produces 20% more sales opportunities. For instance, AI algorithms can suggest targeted posts for users just by observing their behavior online.
AI and machine learning would also impact the e-commerce industry. Have you noticed being flooded with products you are looking for after searching it online? That’s machine learning. It analyzes data based on a user’s purchase history or online shopping behavior. Currently, retail companies are also targeting you with specific content by tracking what ads or images you’re most likely to stop scrolling on. At the same time, it also tracks what time of day you are most active in several social media platforms such as Facebook, Instagram, Twitter, and more.
Also, machine learning can reduce the risk of credit fraud in small businesses. It learns from historical datasets that contain fraudulent transactions, helping it to identify patterns that represent a typical fraudulent transaction.