Most retailers are accustomed to using algorithms for e-commerce personalization, particularly for product recommendations. And the best product recommendations leverage the individual preferences and in-the-moment intent of each shopper to help each person discover the products that are most relevant to them in the moment.
Incorporating each person’s affinities and intent as part of your recommendations doesn’t just improve the experience for the shopper, it also makes good business sense. Gartner found that by 2020, smart personalization engines used to recognize customer intent will enable digital businesses to increase their profits by up to 15%.
But a personalized experience does not need to stop at product recommendations in select locations on your website. There are many other areas of your site that you can personalize to the individual. With that in mind, here are five ideas for leveraging machine-learning e-commerce personalization in non-traditional ways.
1. Site Navigation
Making sure that visitors can navigate your site efficiently should already be a key goal for your team. One way to help visitors more easily find what they’re looking for is to surface relevant links in the navigation. Leverage machine-learning algorithms to present navigation options that are relevant to each person’s current and past session behavior, interests and intent, purchase history, content consumption and more so they spend more time shopping and less time digging around the site.
For example, in the image below, one visitor could see “Politics,” “Sports,” and “Finance” listed in the top navigation, while another could see “International,” “Entertainment,” and “Business” in the top navigation instead.
2. Category Recommendations
You may think of products first when you think of recommendations, but many times when your shoppers visit your site, they’re not looking for anything in particular. They may be looking to browse particular categories instead — just like they do in stores when they gravitate toward specific departments to begin their in-store journey.
In various places on your site, such as the homepage, you can recommend categories that are most likely to appeal to each person rather than display a static list of categories you would like to promote (or rather than displaying no categories at all!).
3. Featured Brands
Taking a different approach from categories, you could also personalize the brands that you promote across your site for each shopper. If you feature brands on your homepage, for example, consider highlighting those brands that shoppers have shown an affinity for, rather than show the same set of brands for all shoppers. When shoppers see a brand they like, they may be willing to explore further.
4. Search Bar
Visitors who use your on-site search functionality are telling you exactly what they’re looking for, and it’s beneficial for you to help them find it quickly before they move on. Direct them to the products that they are looking for right in the search bar itself by using algorithms to surface the most relevant items for each person based on the brands, categories, price ranges, etc. they have already shown affinities for on your site. Search results that consider each person’s preferences and intent will show products that are relevant to the individual, not just to the search term, for maximum relevancy.
5. List Sorting
Any list you have on your site, such as a list of products on a category page or a search results page, can be sorted in a way that is relevant to each individual person. There is no reason to show every person an arbitrary order of products that requires them to scroll through several pages to find something they are interested in. Instead, machine-learning algorithms can be used to sort lists to prioritize products that are most relevant to each individual.
Product recommendations are a hugely valuable tool to facilitate product discovery on your site. They can be placed on your PDPs, your homepage, your checkout page, or anywhere you can imagine. But don’t forget to think about the other ways you can leverage machine-learning algorithms to provide a better shopping experience for your visitors. Site navigation, category or brand recommendations, search results, and list sorting can all be dynamically modified to be more relevant to each individual. And you don’t have to stop there — any experience on your site can be driven algorithmically. Of course, you should think beyond your website, too. The same principles apply to using algorithms to drive your mobile experiences, email content, and more.
To accomplish all of this, you need the right data to help you understand each person’s preferences and in-the-moment intent and the right solution to allow you to deliver the most relevant experience to each person – in real time.
To discover how Evergage can help you create individualized e-commerce shopping experiences, request a demo today.