7 Innovative Product Recommendation Technique Examples For Modern Retail

A hybrid recommendation system is a type of recommender that combines two or more recommendation strategies to leverage their complementary strengths and overcome individual weaknesses, like the cold-start problem. Commonly, a hybrid approach merges collaborative filtering (which suggests items based on similar users' behaviors) and content-based filtering (which suggests items similar to what a user liked in the past) to deliver more accurate, robust, and diverse recommendations than a single-component system could achieve.

A product recommendation technique is a strategy or algorithm used to generate suggestions of items a shopper might be interested in purchasing, based on various data points. The primary techniques include collaborative filtering, which looks at the behavior of similar users; content-based filtering, which focuses on product attributes and the user's past preferences; and knowledge-based or demographic approaches, all of which aim to guide customers toward relevant products and increase metrics like average order value.

Recommendations can be personalized for anonymous users by tracking their real-time, in-session behavior using a session ID, even without a customer profile or personal identifiers. The system collects data on categories browsed, products viewed, price ranges checked, and items added to the cart, then uses predictive AI or session-based filtering methods to dynamically adjust content, banners, and product recommendations in real-time based on the current context, enabling effective personalization from the very first interaction.

Recommendations handle seasonality and campaigns by integrating temporal factors, like holidays and specific events, and campaign-related rules or boosting logic into the recommendation algorithms. This allows the system to prioritize timely and relevant products, such as suggesting holiday-themed items or products on sale, ensuring that the recommendations align with current trends, marketing promotions, and shifting consumer purchasing habits, thereby maximizing the impact of limited-time offers and seasonal demand fluctuations.

You can prove the incremental impact of a technique, such as a new recommendation algorithm, by using incrementality testing, which is primarily done through controlled experiments like A/B testing or a holdout group. This involves creating a test group that receives the new technique and a control group that gets the old technique or no recommendation at all, and then comparing the difference in key metrics like conversion rate or revenue to isolate only the additional conversions that were directly caused by the new recommendation, ensuring you measure true uplift as opposed to just correlation.