Frequently Asked Questions

A personalized recommendation system leverages various customer data like purchase history, browsing history, etc., to offer relevant recommendations to the customers. For example, Netflix and Amazon are two popular names that use personalized recommendations to deliver unique content for each user.

Recommendation engines are data filtering tools that use powerful machine learning algorithms to offer relevant recommendations to each user. A personalized recommendation engine leverages consumer behavior data to provide personalized recommendations.

The number of eCommerce stores is increasing at a rapid rate, and the competition is huge. The only way to stand out is through customer-centric, personalized recommendations. A personalized recommendation engine ensures that appropriate predictions are made using customers’ online activities. Hence, the possibilities of conversion increase further.

Not really! All recommendation engines are not AI-enabled. Non-AI based recommendation engines have limited, rule-based segmentation capabilities. On the contrary, personalized recommendation engines Argoid are are AI-enabled, and that’s the reason why they make prompt, state-of-the-art recommendations unique for each user.

Argoid’s AI-enabled recommendation engine can help you with different types of personalization - home page recommendations, in-cart recommendations, product page recommendations, and so on. 

Argoid’s powerful recommendation engine leverages in-app customer data like purchase history, browsing history, etc. to predict the most appropriate products to customers. We use 1 billion ethical data points to create an outstanding shopping experience for your customers. 

With Argoid’s personalized recommendation engine, you can experience up to 1200% ROI, a CTR boost of up to 92%, and as much as 18% growth in Average Order Value (AOV).