Intelligent Recommendations

Intelligent recommendations use predictive analytics to recommend items and services to customers based on their past behaviours, preferences, and interests. They are used by businesses to increase customer engagement, provide a more personalised experience, and improve customer retention. Intelligent recommendations can be based on data from customer surveys, browsing behaviours, and purchase histories, as well as other sources of customer data. They are often implemented as part of a larger customer relationship management system, and have been shown to be effective in increasing customer satisfaction, loyalty, and revenue.

Product recommendations are a critical aspect of any retailer’s ecommerce strategy. Smart recommendations help customers find relevant products at lower costs. A product recommended at the right time can influence buying of a product over another. Product recommendations can help boost sales and, thereby, profits too.

In a traditional search system, the objective is to return results that are similar to a query object in one dimension or multiple dimensions based on product features. Recommendations added differentiate and personalise across queries based-on user behaviour analytics.

Ecommerce businesses must keep customers interested long enough for purchase decisions to be made. They must make suitable recommendations to keep customers interested. In fact, smart recommendations can stimulate impulse purchases too.

A study shows that product recommendations account for one-third of online revenue and can reduce cart abandonment by 4.35%. Leading ecommerce sites acknowledge that over 30% of their revenue is earned from purchase of recommended products.

This algorithm generates recommendations based on the behaviour and preferences of similar users. It finds other users who have similar purchasing patterns and recommends items that they have bought or shown interest in.
This algorithm generates recommendations based on the characteristics of the items. It analyses the attributes of the products and recommends similar items to the ones that the user has shown interest in.
This algorithm is used to factorise the user-item interaction matrix into two lower-dimensional matrices, one for users and one for items. It can be used to make personalised recommendations and to identify latent features underlying the interactions.
This algorithm uses deep neural networks to learn the user-item interactions and generate recommendations. It has been shown to be effective in handling large-scale and complex data and can generate more accurate recommendations than traditional methods.
This algorithm uses machine learning to sort and filter products based on customer search queries and browsing history to provide more relevant search results.

This algorithm tracks which products are viewed by a customer and uses that data to recommend similar products to the customer.

This algorithm tracks which products are purchased by a customer and uses that data to recommend similar or complementary products to the customer.
Based on multiple algorithms and combined with other factors such as context, demographic information, and time information, we provide more accurate recommendations in real-time, weighted according to their relevance.




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