Personalize and Recommend:
A new rule of digital marketing that works

Online shopping environment is becoming more and more overloaded with data. At the same time, an online shopper perceives the direct retailers' efforts making him or her buy as the white noise. Nowadays more than ever a customer awaits a personalized approach. And the Amazon success with recommendations engine and its Item-to-Item Collaborative Filtering is the clear evidence of this strategy efficiency. Recommendations extension to an online shop is an intelligent algorithm to make a personalized suggestion and get more purchases from a customer. The eCommerce recommendation engine is an effective upselling and cross-selling tool to increase conversion rates, average order value, revenue and make a customer return again.

Searchanise embedded the best practices in building the logic of their Recommendations Block tool. In this article, the insiders are going to share the secret on how to invest in your online shop passive income by smartly using eCommerce recommendations.
What a product recommendation block is
Recommendations are the blocks of items' widgets grouped based on your client browsing history, recent orders, and other relevant data and located on a targetted page or pages to prompt the customer to buy more. Moreover, the recommendation block makes it easier for customers to navigate through the site. There are several types of eCommerce recommendations, each tailored depending on its logic.
Recommendation blocks in cross-selling and upselling
Each of these blocks may be considered as a very effective upselling or cross-selling tool. Let us discuss every block in detail.

  • The 'New Products' block may be effectively used for upselling, you can compare higher-end new products with the older and cheaper ones, making customers consider purchasing a higher-end product than the one in question.
  • The 'Featured Products' block is also good for upselling, you can use it as the 'New Products' block, letting your clients decide for a more expensive product than the cheaper one they were looking for to purchase.
  • The 'Similar Products' block is applied both for upselling and cross-selling, it depends only on the decisions you make. Our Smart Search & Instant Search application allows adjusting the block (as well as any other block) manually.
  • The 'Most Popular Products' block works fine for upselling as the 'New Products' does prompting customers to think over a higher-end product, however, the 'Most Popular Products' block additionally shows, that the higher-end product is not only more expensive but also more popular.
  • The 'Products by Attribute' block allows you to use it for both upselling and cross-selling. It shows other more expensive products and those products meet additional, complementary needs that the original item fails to fulfill.
  • The 'Customers Who Bought This Product Also Bought' block is a perfect tool for cross-selling, and the Amazon proved it perfectly. It is a product of a technique called affinity analysis. The algorithm identifies the association of people who purchase, for example, a certain book and also purchase another book. Then, with enough data, it suggests customers, who decided to buy only one book, to add an extra book to the cart as well.
The best practices and tips on using recommendations
The position of product recommendations influences how effective they are. You can consider having multiple widgets, one for each of your top categories. As a customer engages with your website, your product eCommerce recommendation engine begins to understand what types of products this customer is interested in and deliver more personalized suggestions.

  • The experience of Searchanise shows that the 'Customers Who Bought This Product Also Bought' and 'Similar Products' widgets perform better when placed on the product page as the customer already decided about the purchase, is loyal and ready to buy, and is more prone to add more to the basket.
  • Use the 'Most Popular Products' (Bestsellers) recommendations for new visitors. The best practice is to supply the bestsellers of your store on the most prominent pages of your shop: homepage, collections, and checkout. When a new visitor comes to your store, you don't know what products to recommend. Showing other people social proof on the main pages is the first touch point with the lead for further engagement.
  • Personalize the 'Products by Attribute or Featured Products' recommendations based on web behavior. You can customize 'Products by Attribute or Featured Products' by picking up the items of interest, grouping them in a block and naming it based on shopping behavior. You can place such a block composed of the items of the customer favorite brand on a checkout page and call it as 'Other Items of This Vendor…', for example.
  • If your website has Blog, Search or Error pages you can use them to lead your customers back to your shop by showing intelligent recommendations on them. Think about placing 'New Products' above the fold on the blog page.
The Searchanise extension allows you to tweak the code to edit the external appearance like fonts, color or size to attract more attention to your recommendation. This option is called CSS.

Adding a label such as On Sale or View Product on the widget with a recommendation will land your client faster on the checkout page.
Use analytics to track the eCommerce recommendations' performance.

Look at the following examples with Featured Products on the Collections page
Bestsellers and Customers also bought on the Cart page
Similar products with promoting labels on the Product page
Customers also bought… with promoting labels on the Product page
New Products with promoting labels on the Home page
This is how you can apply the recommendations, manually tweaked or left without changes, on the right pages to upsell and cross-sell your products.

Product recommendations serve as the foundation for your eCommerce personalization strategy. The next step to increase conversions is to build out more advanced personalization tactics.

And what is your story of personalizing the smart suggestion? Share your success in comments or drop a line to get the advice of Searchanise!
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