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Product Recommendation Engine: How Does It Work?

AI-driven Product Recommendations

User needs are complex and understanding them becomes crucial in providing relevant products. The human mind may be complex, but nothing will ever compare to data-based decisions that gather accurate information over long periods of time. Allowing your eCommerce business to rely on insightful big data technologies alongside machine learning, your B2C business will see drastic increases in conversions and marketing ROI. How does a recommendation engine solve this?
McKinsey estimates that 35 % of what consumers purchase on Amazon comes from product recommendations.

Personalized product recommendations are relevant

A personalized recommendation engine provides users with individual item recommendations, allowing them to focus on content that matters to them instead of browsing through unrelated offers. Thus, relevant products that might otherwise be overlooked get to users in a quick and clean manner.

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.

Is your website ready to use an AI-driven product recommendation engine? Calculate your personalization readiness score →


Useful resources:

Read the case study for Imobiliare.ro to see how a recommendation engine helped their real estate portal

AI in Ecommerce (InsightOut Analytics)

How retailers can keep up with consumers (McKinsey)

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