Machine Learning in eCommerce Site Search

Predicting the future may be a tricky exercise, but that doesn't mean businesses can't identify and prepare for changing trends. Spotting these and capitalizing on them can help your company improve your products and increase your customer base. With machine learning, you can analyze current data and extract insights about the future.

Machine Learning in eCommerce Site Search

Machine learning is a phrase that refers to a computer or program's ability to teach itself based on experience instead of being programmed by someone. It uses artificial intelligence to recognize patterns in data and enhance its own abilities through trial and error.

Artificial intelligence isn't necessarily the same thing as machine learning, though. Artificial intelligence is any type of automated behavior that mimics human behavior in some way. Motion-activated cameras are a type of artificial intelligence because they can sense movement. Machine learning is a subset of artificial intelligence that specifically looks at how machines process data and make predictions about the future based on their experiences.

The best part about machine learning is that it doesn't require the intervention of humans. Your company can save countless hours and overhead costs by relying on machine learning instead of needing programmers to make every change manually. Machine learning works great within the eCommerce space by leveraging data from customers like their shopping history and habits.

From these numerous data points, machine learning can make predictions about what they'll need and when they'll need it. However, this can only take your website so far; once customers get to your website, they'll need to be able to find their products without spending time unraveling complex site navigation.

Driving traffic to your website means nothing if people can't find what they're looking for in a timely manner. Your conversion rate depends on having an accessible website that makes it easy for customers to peruse your products and find what they need. This is where the second part of machine learning comes into play. With machine learning, your website can modify the messaging and internal search results based on user intent in real-time.

Improving Relevance

One of the ways that machine learning is used for site searches is to improve the relevance of the results. Typically, customers who start searching for products on your website are already prepared to buy them. Their goal is to see if you offer the products, included features, and your prices.

If customers are faced with result pages that give them zero results, or hundreds of results that have little to do with what they searched for, they're very likely to leave your website unsatisfied and take their business elsewhere. Machine learning helps your search return relevant results for customers who don't have any time to waste.

Related Search Results

Machine learning doesn't just help customers by filling in product names or accounting or misspellings either; it can also show similar or related products to entice customers to buy more than they'd originally planned. Showing accessories for products can be a great way to upsell customers during the purchasing process.

Machine learning incorporates previous shopping history, browsing history, and relates products in new ways based on other customers' experiences. It can also take into account what other customers have clicked on or added to their carts after searching for a term or set of keywords, learning from their connections.

For example, if you have an account with Amazon, you'll likely notice that every time you sign in, even before you click the search bar Amazon is presenting you with similar items to whatever you bought last time or the time before. Amazon often presents items based on the past two or three items you purchased.

Error Tolerance

Your site search needs to tolerate errors in spelling. Focusing solely on keywords won't help when plenty of customers are in a hurry and spell words incorrectly or don't know how your clever brand names are spelled. If you use machine learning to account for these errors and provide autocomplete names, you can make it easier for your customers to find what they're looking for.

This is a good thing since most customers will leave your website after a few minutes if they can't find what they need.

Natural Language Processing

When customers set out to find things, they're more likely to type their inquiries in the way they would tell their friends, using natural language. Guessing at the right combination of keywords to yield the right results is a tedious waste of everyone's time, and most customers don't have the patience to play that game.

Site search tools like Yext Answers can help you enable your site search to process natural language and return relevant results, regardless of how your customers ask their questions. This tool also catalogs all of the search queries, so you can see what people are searching for and if you have any gaps in your content.

Predictive Analytics

Machine learning can also extract insights about customer behaviors that allow your site to predict future behavior from other customers by knowing what is happening in their lives and what is motivating their purchasing choices. Depending on what customers choose to buy, machine learning can extrapolate major life events that might make other items appeal to them in the future.

For example, if a customer buys cat food, this should trigger your website to predict that the customer owns a cat and will need other cat supplies. They might see ads for cat litter, cat toys, harnesses, or costumes. If they are registered with your website, you should enable coupons or discount codes for cat-related items to incentivize them to return to your site.

However, these predictive analytics can also function on a much larger scale too. By analyzing the buying trends, you stay apprised of the market and what people will want in the near future. This can influence your marketing strategies, as well as how you stock your items so you won't run out of something when people start buying.

Understanding the context of customer searches is key, both for predicting what they need now and what they'll need in the future. Machine learning can also learn to incorporate time between purchases of consumable items. Using the example from above, your site search may learn that after purchasing a bag of cat food, consumers typically purchase a second bag three weeks later.

It learns this fact by seeing repeat customers and finding patterns or overlaps, and comparing the time between purchases for an average. Your website can then send out personalized notifications or discount codes gently reminding customers that they may be running out of cat food soon and should buy some more, so their kitty doesn't go hungry.

Improve Ranking

Another way machine learning can benefit site searches is through ranking. When a customer searches for cat food, and you sell multiple products with that label, your search engine needs some way to determine the order in which the cat food products are listed on the search results page.

Some eCommerce websites like Amazon allow customers to choose filters that automatically sort their results by price or average review star rating. You, too, can enable Yext to learn what items the customer is looking for based on previous search histories, shopping behaviors, what they entered into the text box, and what other people like them bought previously.

With machine learning, your site search can parse all of that data and sort the items into a reasonable ranking system that lists the items the customer is most likely to buy at the top, with the likelihood of purchase decreasing as the results page continues down.

Chat Bots

Customers aren't always looking for products on your website. Plenty of customers are looking for information about what you offer, tutorials for how to set up or use products, or more information about the product's capabilities. For questions like these, having an automated chatbot is a great way to provide answers without involving customer service representatives.

Plenty of customers would rather avoid waiting for an email response. Instead, a chatbot provides instant access to a knowledgeable expert who is always online. Plus, it saves your business time and money since the bot is answering questions and decreasing the number of customers who need human support.

In Conclusion

The great thing about machine learning is that your site search will become smarter and more informative with every shopper, whether they purchase your products or not. Your website analyzes the data from your customers and users to see how they organize products and which ones relate to each other to provide better, more relevant results.

Contact us for more information about incorporating machine learning into your company's site search and boosting your conversion rate.

Sources:

Machine Learning Improves E-Commerce Site Search Read More | Eventige

How machine learning is changing eCommerce site search for the better | Loop 54

Machine Learning For Ecommerce: How Does it Work? | Big Commerce