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