Machine Learning Ecommerce

The tech industry is full of jargon that can be difficult to parse and understand — from cloud computing to marketing automation. You’ve probably heard of machine learning at some point, but if you’re not a data scientist, computer scientist, or someone with a deep interest in artificial intelligence, you probably don’t know how it works or exactly how it could be used in your business.

In this article, we will try to demystify machine learning, show a tangible example of how it works, and demonstrate how it can help eCommerce businesses improve their conversions.

What is Machine Learning?

Machine learning uses statistical techniques to give computers the ability to “learn” how to perform tasks without the exact steps being explicitly programmed. For example, a machine learning model might predict what products a customer is likely to purchase by looking at his or her purchasing history or similarity to other customers without being given a specific algorithm that defines how to make those connections.

While the term was first coined back in 1959, machine learning technologies didn’t become widely popular until the early 2000s, with the rise of internet giants such as Google, Facebook, and Amazon. The technology is now used across a wide range of industries where there’s a lot of data available to do everything from predicting consumer behavior to recognizing faces in photos.

Some examples of machine learning in the wild include:

  • Finance – Financial institutions, such as banks, insurance companies, and investment firms, use machine learning algorithms to analyze credit scores, spending patterns, and financial data to assess risk in insurance underwriting. In addition, banks may use machine learning in payment processing platforms to recognize and avoid potential fraudulent transactions.
  • Retail – Amazon uses machine learning algorithms to analyze the past purchasing history of its customers and make personalized recommendations. Netflix similarly uses machine learning to make movie recommendations.
  • Travel – Google Maps uses machine learning algorithms to analyze anonymous location data from smartphones and recommend the fastest routes to drivers. Uber also uses machine learning to calculate pickup times and find the closest drivers.
  • Social Media – Facebook uses machine learning algorithms to recognize familiar faces from your contact list and automatically tag them in photos.

There are many different types of machine learning algorithms, and they are quickly becoming ubiquitous in software development and a part of everyday life for consumers.

How Machine Learning Applies to eCommerce

There’s no doubt that machine learning can be a powerful force in eCommerce. After all, Amazon owes a lot of its success to its ability to predict what consumers want to purchase, and then deliver those products in a very short period of time. The massive amount of data that it collects allows it to improve at both over time.

Machine learning is becoming increasingly necessary to predict what consumers want to purchase and remain competitive.

There are many ways that machine learning can help an eCommerce retailer:

  • Search – Machine learning can be used to optimize search results pages to show products that are the most likely to result in a sale. For example, the algorithm may look at what products customers purchase the most for a given search term and rank those higher in the search results. Machine learning algorithms can also be used to populate autocomplete search fields.
  • Categories – Category pages can be optimized in the same way as search results pages — by prioritizing the products that consumers tend to purchase the most within the category.
  • Personalized Recommendations – Machine learning can be used to make specific recommendations for a user based on their purchase history or their similarities to other users.
  • Pricing – Machine learning can help optimize prices based on competitors’ pricing, time of day, demand, and the type of customer visiting the website.

Of course, there is a lot more to data analysis than simply looking at what products were purchased the most. Cheaper products may be purchased more often than expensive products, so price biases must be accounted for to avoid only showing cheap items in search results or category pages. The top items will also tend to be purchased more, so the machine learning models must account for the position in the search results or category pages. For more information on this topic, see our article, How Behavioral Data Can Improve Search.

Computer scientists spend a lot of time thinking about these issues when building machine learning algorithms.

When used properly, machine learning offers many benefits to eCommerce businesses:

  • Improved Conversion Rates – Optimized search results and category pages can greatly improve conversion rates compared to standard non-optimized pages. Personalized marketing campaigns can also increase conversion rates on advertising spends for email or display ads.
  • Enhanced Brand Loyalty – Customers that receive personalized recommendations may have greater loyalty to a store than other stores that don’t offer such recommendations.
  • Improved User Experience – Optimized search results help visitors quickly and efficiently find the products that they want to purchase, which improves the user experience and reduces the time spent on the site.
  • Fewer Fraudulent Transactions – Machine learning can help eCommerce stores avoid fraudulent transactions by recognizing risk factors before shipping a product.

Adding Machine Learning to Your eCommerce Store

Machine learning may sound complicated and beyond the reach of small businesses, but this isn’t the case. A number of businesses and products have sprouted up in recent years to help eCommerce retailers compete with the Amazons and Walmarts of world.

That said, many eCommerce companies are quick to throw around the term “machine learning,” despite using overly simplistic implementations. It’s important to find a balance between affordability, ease-of-use, and a truly useful product that tangibly improves your business.

At Avatria Convert, we harness the power of machine learning to help small to mid-sized eCommerce companies show customers products that they’re most likely to purchase.

We use customer shopping data from your existing Google Analytics account and provide significant value with minimal implementation effort. You can even select specific categories or search terms to optimize for granular control.

These capabilities can help you improve your conversion rates, improve the user experience for shoppers, and ultimately generate more revenue for your business.

Getting Started

If you’re interested in harnessing the power of machine learning for your eCommerce website, sign up for free today!