The term “Machine Learning” sounds like the premise of a futuristic sci-fi movie where computers take control of the world. Fortunately, the future of humanity is much brighter. In this blog we will see how machine learning fuels product recommendation tools which can translate into increased sales and loyal customers.

Ok, What Exactly is Machine Learning?

Machine Learning is a part of modern search engine technology that gives computer systems the capability to “learn” from site visitor input and make predictions about what people would like to purchase. The goal of machine learning is to continuously boost search quality without having to be specifically programmed for such a task.

Product recommendation tools, which are found in many search engines are largely impacted by machine learning. Creating recommendations for site visitors unleashes the power of suggestive selling by showing customers products that would most likely appeal to them.

Recommendation tools use machine learning to analyze what customers have recently purchased, which products are placed in carts and just as importantly, what other customers have viewed or bought. This data greatly improves the ability to predict customer behavior. Utilizing this information is also a powerful method to achieve search personalization, as only products which would most likely appeal to visitors are displayed.

Product Recommendations – Just How Well Do They Work?

Let’s look at the numbers:

  • 100% of the top 10 websites in the Internet Retailer 500 engage visitors with recommendations.
  • 70% of Amazon’s home landing page is populated by product recommendations and 35% of their revenue is generated from this source.
  • Netflix reports that 75% of viewer choices come from product recommendations.

Data also shows that a recommendations tool generates additional business from both new and existing customers. Average revenue per order is positively affected as well.

Getting It To The People – Recommendation Strategies

There are various ways to incorporate product recommendations into your site search. While the end aim of these strategies are the same – to increase sales, having flexibility in the approach can be incredibly helpful.  Here are some effective ways to categorize recommendations for site visitors.

  • Also Added To The Cart
  • Also Bought
  • Also Viewed
  • Best Sellers
  • Featured Items
  • Hot Now
  • More Like This
  • Most Popular
  • Recently Searched
  • Recently Viewed
  • Trending Items
  • View Then Bought

Filtering Out The Noise – Keeping The Customer Focused

Filters within the search engine can be used to make the most out of each specific recommendation option. For example, an online sports retailer wants to utilize the “more like this” approach when displaying hockey sticks. The site’s marketing team can use onboard filters to only display additional hockey sticks (not related products) as recommendations. Filters can be used to suggest hockey sticks by any facet such as brand, price, size etc.

The “also bought” option however has a different strategy. In this case when an online seller displays hockey sticks, it would be beneficial to set the internal filters such that hockey tape or gloves also come up as recommendations. These products complement the purchase of hockey sticks and could be easily bought at the same time. Filters are a powerful way to control what product recommendations site visitors see and they can influence buying decisions.

What Do You Recommend?

Having a recommendation tool as an integral part of your search functionality is vitally important today. Some of the most successful and largest ecommerce retailers use these suggestive selling methods and it accounts for a significant part of their revenue. At the heart of every effective recommendations tool is machine learning. The more data that is fed into a search engine, the better it becomes at predicting shopper preferences and behavior. All of this effort leads to a better search experience for site visitors. Providing a relevant and personalized search experience through recommendations will translate into sales, additional revenue and repeat customers.

We highly recommend that you let machine learning teach you how to sell more!

For more information on how Hawksearch’s Relevant Recommendations tool can help you succeed, click here to schedule a demo!