Relevant recommendations

Relevant product recommendations make shoppers more likely to create a shopping cart and complete their purchase.

Product recommendations in online shop

Real-time recommendations

Real-time recommendations suggest products based on the collective interest of all your site visitors as well as the unique visitor’s history.

Personal recommendations are based on what the current visitor has clicked or bought, on what other customers have bought, and on items currently in the cart or that were previously abandoned.

Boosting cross-sell and up-sell

To increase the average order value, the recommendations feed the e-commerce site with both alternatives as well as supplementary product recommendations. The recommendations capability can be used omnichannel, for example in Email Recommendations.


Working in synergy

Recommendations work in synergy with Site Search, Site Navigation and Banner Ads to avoid cannibalising results from the others. This is a strength compared to tools with a singular focus.

Apptus Recommendations based on:

Inspire with the best-selling products

These recommendations display real-time popular and trending products. They are suitable on the start page to welcome visitors, on category pages to show popular products for the specific category and on the No Results page.

Recommendations based on top sellers
Say 'Welcome back!'

Recommendations based on customer are site-wide personalised recommendations based on individual behaviour such as products the visitor has seen or bought. You can use them on the start page to make the visitor feel more “at home” and on the No Results page.

Recommendations those who viewed also bought
Give abandoned products another chance

These are customer-based recommendations of products from the customer’s abandoned cart. The system remembers the visitor's previous intentions and gives these products a new chance.

Recommendations based on abandoned cart
Remind about viewed products

Recently viewed products are another type of customer-based recommendations that work as a reminder of previously viewed items and allow the customer to return to products after viewing similar ones.

Recommendations based on customer viewed
Promote supplementary items 

Inspiration from people with similar tastes is one of the main drivers for purchases. Recommendations based on those who bought also bought can be used on product detail pages.

Recommendations those who bought also bought
Inspire with alternative and supplementary products

Recommendations based on product is a more advanced variant that uses visitors’ behaviour from the whole site to select product recommendations. Both alternative and supplementary product recommendations are provided.

Recommendations based on product
Increase the cart value

There is a golden opportunity to recommend products based on the items in a visitor’s cart.

These recommendations are used for both cross-sell and up-sell, and to push the order value above a certain limit by recommending products that will pass the threshold value.

Recommendations based on cart
Benefit from visitors with similar tastes

Shoppers often search for items that go well together. Inspire them by offering products that other people bought after looking at the same product.

Recommendations those who viewed also bought
Let visitors benefit from crowd wisdom

You can inspire and benefit visitors looking at a specific product by using recommendations based on products that other people found after viewing the same product.

Recommendations those who viewed also viewed
Hearts Don’t Lie

A customer adding an item to their favourite list says a lot! Take advantage of this information and give your customers personalised product recommendations based on their favourites.

Recommendations based on favourites

We’re looking forward to doing even more testing and use of automation in the future, especially now that we’ve seen how well it can work!

Jenny Vesterlund
eCommerce Manager, KICKS

Apptus eSales has given us the ability to use real-time customer data as the fuel for behavioural search, faceted navigation and trend sensitive contextual recommendations.

CEO and Owner, Lyko

Further reading