Find out whether your eCommerce shop needs AI-driven product recommendations, manual/rule-based product recommendations, or no recommendations...
Product Recommendation Engine: How Does It Work?
AI-driven Product Recommendations
McKinsey estimates that 35 % of what consumers purchase on Amazon comes from product recommendations.
Personalized product recommendations are relevant
AI Product recommendations can be used on multiple channels
A tailor-made recommendation engine can reach its audience through multiple channels: widgets on the home page, category lists in combination with user-defined filters, product detail pages, ads, and e-mail campaigns.
Keeps your recommendations up-to-date automatically
The Machine Learning model works by considering product attributes (brand, price, profit margin, stock availability), shopper behavior (pages visited, products bought, add to cart, favorites lists), as well as the behavior of other clients (to incorporate “hot” listings on the market). Moreover, the model can update daily or even in real-time (if the data allows it), to account for changes in products and stock levels, and to learn from new consumer interactions.
Analyses your users' behavior and website content
There are different types of recommendation engine algorithms that deliver successful conversion. One type is the collaborative filtering algorithm which is based on collecting and interpreting large volumes of customer behavior data. It compares similar actions of different potential customers and predicts what a particular user might be interested in.
Another type of machine learning algorithm is the content-based recommendation engine. It takes into account customers’ profiles as well as attributes of the products that users interact with. This algorithm is strongly focused on item properties and the similarity between them.
Helps you increase the most important online KPIs
Overall, the benefits of implementing a recommendation engine through big data technologies are countless, from increased conversion rate, average order value and no. of page visits to continuously decreasing bounce rate averages and cart abandonment rate.