Artificial intelligence is gradually influencing the daily lives of common consumers.

Siri, Amazon Echo, Google Home, and other AI products have made it mainstream, but what if we could make AI-powered retail buying mainstream as well? For example, imagine stepping into your favorite store and having it not only recognize you but also route you to specific styles, brands, and products based on your unique user profile. Does this sound too good to be true? The moment has come for artificial intelligence in ecommerce.

According to IDC, global AI spending will reach $46 billion in 2020, a 768 percent increase from 2016. There is fierce competition in the retail market, so chances are that a brand’s competitors are already implementing an AI strategy for their business. This means that CMOs, CTOs, and chief digital officers must become aware with various techniques to leveraging AI in order to reap the benefits and develop an organized, comprehensive, and more predictive approach to customer knowledge.

AI’s potential in ecommerce is enormous. As more people browse, study, and buy online, retail firms learn far more about their customers than they ever could from brick-and-mortar locations. So, how should brands use all this consumer data? One approach is to employ AI to make better merchandising judgments. Gartner claimed in 2013 that less than 5% of ecommerce organizations used big data or predictive analytics technologies, but this figure is increasing. Brands must capitalize on the customer and purchase data accessible to them.

Retailers today must embrace new technology in order to future-proof their businesses, and AI should be at the top of the list. Ad hoc machine learning applications in ecommerce include up-sell and cross-sell recommendations, as well as dynamic creative optimization. However, ecommerce lacks true AI capable of aggregating and analyzing thousands of attributes linked with millions of users, with probability updated in real time.

Ecommerce requires AI to read consumer behavior and transfer it to structured data with predictive variables such as size, fit, color, brand preferences, price range, and so on in order to construct a true and actionable customer relationship management (CRM) system. This comprehensive approach to knowing consumer preferences offers up a plethora of new and exciting applications for retailers, like predictive merchandising, dynamic re-ranking of product listing pages, in-store personal digital concierges, and so on.

Here are the top three advantages of adopting AI in e-commerce:

1. Enable smarter merchandising and inventory planning.

Decisions must be made quantitatively across millions of data points and millions of users. Merchandising teams must have access to data as well as tools for aggregating and computing it. It cannot just be a log or a historical summary of customer behavior; rather, it must anticipate what will happen in the future based on millions of unique qualities for each consumer.

2. Create a personalized concierge experience.

Consumers can adjust their preferences on an hourly basis. A woman may want pink pants today but a blue dress tomorrow. However, some choices are inflexible, such as a woman’s size, brand preferences, or her desire for three-quarter sleeves. Retailers require AI to process all of this consumer behavior, map it to predictive features, and compute the data in real time. When a business offers an optimal and distinctive consumer experience, the likelihood of customer involvement increases. When consumers form a more personal connection with a brand, the likelihood that they will become loyal customers improves.

3.Use AI-generated data to increase revenue.

If you compute aggregate demand data across all channels and develop unique attribute data for each consumer, you can unlock new revenue streams by enabling each channel with this data. For example, your search can grow smarter by understanding aggregate demand for various products as well as specific consumer preferences at the attribute level, such as size, fit, color, brand preferences, and so on. Investment in AI leads to exponential revenue growth, with a single investment having a knock-on effect across numerous channels.

Using AI technology is the only way a company can differentiate itself and win in today’s hyper-competitive retail environment.

As today’s consumers become more knowledgeable and have higher expectations of how brands target them, retailers will need to rely on structured data with predictive qualities. Some retailers, such as Nordstrom Rack, have already begun leveraging structured data based on consumers’ predicted traits to better target customers.

Moving forward, we expect to see more adoption among other retailers. Artificial intelligence-powered forecasts can improve the shopping experience for your customers, provide fresh insights into overall demand and trends, and, ultimately, generate new income opportunities for the company. It would be crazy to pass it up.