Ecommerce entrepreneurs are always looking for an edge; a way to outshine competitors, streamline their operations, boost ROI and provide an optimized user experience to shoppers. This explains why the online retail industry is embracing artificial intelligence technology.

In particular, we’re witnessing new uses for machine learning in ecommerce emerge as AI becomes more advanced and accessible. Machine learning, by definition, is “AI software that modifies its own algorithms in order to improve future results.”

So, each successive operation becomes more effective based on how the previous one went. In other words, machine learning alleviates the need for humans to fine-tune software to get results. The algorithms refine themselves over time, ensuring better business outcomes.

But how will this phenomenon look in action? Here are just three uses for machine learning is poised to revolutionize ecommerce.

A Smarter Search Bar

It’s non-negotiable for websites to provide a responsive search bar, preferably near the top corner of landing pages because customers have learned to look there first. Traditionally, these search results depended on ecommerce teams entering manual synonyms and phrases. The obvious pitfall here is that sometimes-relevant search results may not turn up simply because it’s difficult for humans to anticipate every single search customers might make.

Machine learning can help improve site search in several ways. Examples include:

  • Automatically broadening this set of synonyms
  • Prioritizing products with better ratings
  • Restricting results to include products currently in stock
  • Recommending products based on a customer’s past behavior

Essentially, machine learning reduces the potential for human error in setting up site search. More relevant search results can boost conversion rates and customer satisfaction with a given website. After all, if customers don’t see what they need—or want, they’ll try a competitor. If your site search returns relevant results, they’ll stay in your sales funnel until they complete a transaction—and perhaps even return again in the future for more.

Live Chat Customer Service

Top-notch customer service is a must-have for online retailers. But it’s challenging to have humans accessible 24/7. Many customer questions crop up outside of typical business hours. This explains why so many online stores are integrating live chat bots with their cloud-based ecommerce platform.

These intelligent response systems can answer basic customer queries even when human customer service representatives are not available. And machine learning enables these chat bots to learn from previous interactions in order to improve future ones. According to Practical Ecommerce, possible applications include on-site customer chats and social media posting.

Intuitive Product Recommendations

Upselling and cross-selling are staples in ecommerce, two tactics aimed at boosting Average Order Value (AOV). But the success of these sales methods depends on the relevance of your website’s additional product recommendations.

Let’s say a customer currently has a chair and reading lamp in their shopping cart. Suggesting they pick up a bedframe is a less-than-intuitive choice. It’s also not an ‘impulse buy’ someone would typically feel comfortable adding in at the last minute. However, recommending a small rug, a lap blanket, bookshelf or small side table makes sense.

Machine learning has the potential to make smarter recommendations to drive sales based on a host of factors, including customer behavior, shopping cart contents, time of year, product popularity and conversion rates for certain products. Based upon the results of each interaction, the algorithms can continually hone their approach to cross-selling and upselling over time.

These examples are just the tip of the iceberg when it comes to uses for machine learning in ecommerce. One thing is clear: Artificial intelligence has the potential to automate certain processes for better business outcomes.

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