Product recommendations are an excellent way to optimize the customer experience and drive conversions. They allow you to create an in-store experience for your online customers all while boosting metrics like average order value and revenue. However, it’s important to remember that product recommendations aren’t one-size-fits all; what works for one online business might not work for another.
There are many factors that go into successful product recommendations, such as algorithm and placement, and it’s crucial to A/B test and optimize all of them along the way. Here are a few ways you can optimize your product recommendations and deliver a tailored experience to your customers.
Determine The Right Algorithm
The type of product recommendations you deliver can make or break your success. This is why you want to choose your algorithm carefully and even A/B test various algorithms and rules to see what works best for your site. Four common algorithms we see are:
- Popular: The most bought items within a given time frame
- Complementary: Items most often bought in the same transaction as the current item or set of items
- People Also Like: Items most often bought by users who viewed the current item.
- Personalized: Items tailored to the visitor’s preferences by analyzing their past behavior.
Each of these algorithms work a little differently, and they all have their own set of unique benefits. For new visitors, you may want to use Popular product recommendations to show them the most purchased items on your site. People Also Like and Complementary algorithms both offer add-on/upsell items based on cart and item similarities, respectively. Of course, Personalized product recommendations are based on individual user profiles and can deliver a truly tailored experience to your customers. We’ll talk more about personalization later on in this article.
Within each algorithm you should experiment with fine tuning the product recommendations even more by adding rules to boost or exclude certain product attributes, prioritizing companion accessories, for example, or giving your store-brand a bump.
Once you’ve determined the right algorithm(s) for your site, it’s time to A/B test the overall user experience of your product recommendations. The details in how your recommendations are presented can make a big difference and should all be A/B tested. Understanding how your users respond to each of these aspects is crucial to your success.
While it can feel overwhelming, A/B testing each of these items individually will ultimately help you identify what your customers want and deliver better product recommendations. Here are some aspects of your product recommendations that should be A/B tested and optimized:
- Which page(s) will your product recommendations appear on? (i.e. product detail pages, homepage, cart page)
- Will your product recommendations appear above or below the fold?
- Should you include more than one placement and algorithm type on the page?
- Does the location of your product recommendations differ depending on where the user came from?
- How do you incorporate special placements for unique situations, like searches with low results, or product detail pages for out-of-stock items?
- What is the layout of your product recommendations display? (i.e. row versus grid of products)
- How large will your product images be?
- How many products will you include in your display? Do you show all at once, or offer a scrolling carousel or “see more” option?
- What product information will you include in your display? Depending on your site, you may want to include or exclude details like product name, price, review rating, color options.
- If a customer clicks on an item, will a ‘Quick Shop’ window pop up or will they go directly to that item’s PDP?
- Will customers be able to add an item directly to their cart from the product recommendations section?
By optimizing the look and feel and the functionality of your product recommendations, more customers will engage with your product recommendations and convert.
Personalized product recommendations are perhaps the best way to create a one-to-one shopping experience for your customers. By basing your product recommendations on data like previous purchases and behavior patterns, you can recommend items specifically tailored to a customer’s preferences.
For example, you may recommend a product that the customer previously viewed or added to their cart but did not purchase, or a complementary item that pairs well with something they’ve previously purchased. With the right data and targeting, your personalized product recommendations can create an in-store experience digitally.
To learn more about SiteSpect, visit our website.
About Kate Orchard
Kate Orchard is a Manager of Customer Success at SiteSpect, where she consults SiteSpect users on their optimization and personalization road maps and projects. She is based in Boston.
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