Machine learning is a subset of artificial intelligence (AI), featuring algorithms that improve with exposure to data. People are understandably curious about how machine learning e-commerce applications could help online retailers increase their business. Here are five possibilities of note:
1. Offering Product Recommendations
Amazon was one of the first major e-commerce retailers to utilize machine learning to give people product suggestions. Some estimates indicate that 50-60% of its page elements come from machine learning algorithms that show products and ads based on what individual shoppers should like.
Product recommendations driven by machine learning could feature items similar to products people bought before or show merchandise that individuals in certain demographic groups are likely to buy. In either case, shoppers are more likely to stay on a site longer, and potentially increase the sizes of their purchases, if they see recommended products that seem relevant.
2. Reducing Cart Abandonment Instances
Cart abandonment happens when people fail to follow through with their intended purchases. If online retailers don’t figure out how to cut down on the total consumers who don’t complete their transactions, they’ll miss opportunities to make sales.
Machine learning algorithms could help. For example, they could provide people with targeted information on the site to keep them interested. If a person searched for electric toothbrushes and filtered the search results to show the least expensive options first, the algorithm might cause a discount coupon to appear on the screen.
Metrical is one company that takes such an approach with machine learning. It utilizes predictive analytics to determine the content most likely to make people stay on a site instead of leaving. Other enterprises do something similar by providing material based on how a person navigates through a site or which sections seemingly interest them the most,
3. Giving People the Best Prices
Machine learning e-commerce success could also occur when internet retailers use the technology for price optimization. That technique relies on data to assess the best prices for a particular product at any given time. Some people refer to this approach as dynamic pricing since the rates shown on a site could change by the minute.
A machine learning algorithm could assess dozens of factors, ranging from the weather to a retailer’s on-hand stock numbers, before setting prices. The goal is to create a price that helps the retailer profit but does not seem too high to the consumer. If a shopper continually perceives a retailer to have the best prices for the products they want, they have a better chance of becoming a loyal customer and even recommending the store to their friends.
4. Personalizing Ads for Optimal Conversions
Retailers are constantly challenged to come up with ads that entice the members of their target audience. In 2019, a research team discovered a way to target ads to people based on their personality types. The group used computer algorithms to find out what kinds of colors and images extroverted people preferred to see, for example.
The scientists also realized that people perceive images to have “personalities,” and that they responded more favorably to pictures that matched their own personalities. These conclusions give a glimpse into how e-commerce retailers might apply machine learning when designing their email marketing messages or on-site ads.
5. Providing Improved Customer Support
Chatbots combine several advanced technologies, including machine learning and natural language processing (NLP), to provide people with real-time customer support. They’re becoming a particularly advantageous machine learning e-commerce option since they streamline interactions and take some of the burdens off of human representatives.
The kinds of machine learning algorithms used for chatbots can also get smarter based on feedback. For example, many shoppers who request help from an e-commerce website in any format get asked to rate the quality of the assistance and whether the interactions resolved their issues
Chatbots can also operate 24 hours a day and give customers answers outside of business hours. Then, the chances are higher that people will get the responses they need to encourage them to finalize their purchases.
If customers feel doubtful about buying things because they believe the company is not available to help them, they may give up and shop elsewhere.
Only the Beginning of Machine Learning E-Commerce Possibilities
Retailers’ efforts to tap into machine learning to grow their businesses are still in the relatively early stages, so it’s anyone’s guess what the future may hold. However, the five examples above should be enough to convince brands that they should investigate machine learning without delay.
Depending on it could cause sales increases, improved customer satisfaction and other perks that collectively solidify a company’s presence in a continually challenging online landscape.
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