Alie’s “Most Purchased” recommendations rely on other user's purchases. Alie uses item-based filtering to understand other user’s purchase behavior for a particular category of items(& attributes). Once the system understands the behavior, a link is established between user & item attributes, and recommendations are made accordingly. For Ex. User A is looking for a 55 inch TV, then Alie will recommend a list of 55 inch TVs bought mostly by other users.
“Most Purchased” recommendations are best suited for new visitors on your website or app. New visitors are an obvious challenge since you are yet to collect any behavioral data from them. In this case, the best product recommendations practice would involve highlighting items with the best conversion ratios (i.e. Most Purchased or Top Sellers).
Alie’s “Most Purchased” recommendation uses a concept called ‘social proof’, where people follow the actions of the masses. This is particularly important for an Ecommerce platform, because online shopping doesn’t provide the liberty to try on, touch, and see products in person. In this case, customers are more likely to be swayed by other shoppers' opinions. Also, you can try adding popularity messaging to increase urgency.
Alie’s “Most Purchased” recommendations are ideal for product detail pages. Recommendations displayed on product detail pages are generally the items that are similar or they complement the original product. You can display the “Most Purchased” Recommendations on product detail pages which can easily catch shoppers’ attention and are most likely to be purchased.
Alie’s “Most Purchased” algorithm is easy to implement. All you need to do is just login into Alie CMS, create a new project, add data using APIs or JS Plugins, select the “Most Purchased” algorithm in the next tab, and generate the final output (Recommendations). Subsequently, you can hook these recommendations on the appropriate pages of your website or app using APIs provided by Alie.