Our proprietary recommendation engine powers all our products, seamlessly adapting to each store's unique shopping patterns. With GDPR-free, intelligent recommendations, WRE is first of its kind to ensure smart suggestions for physical shopping.
WRE is our pride and joy, and here are some insights into how we generate recommendations.
The recommendations are generated in two stages, and we use machine learning and AI-models to train our engine. Our commercially available model of the recommendation engine also runs completely GDPR-free.
For stage one of the recommendation generation process, the engine establishes a foundational understanding of the product data, creating a structured base that stage two will later refine. Here’s how each component of stage one contributes to building this foundation:
Receipt Data: By analyzing transaction histories and receipt information, the engine gains insights into purchasing patterns and popular products. This data allows the engine to identify commonly bought items and seasonal trends, forming a basis for initial recommendations.
Descriptions: Product descriptions provide essential information about each item, such as specifications, attributes, and category relevance. This helps the engine understand product characteristics, making it easier to match items with similar or complementary features.
Producer: Knowing the producer or brand behind each product adds an additional layer to the recommendation logic. It enables brand-driven suggestions for users with brand loyalty, creating recommendations that align with user preferences for certain manufacturers.
Metadata and More: Additional metadata, including tags, product categories, and other classification details, further enriches the engine’s understanding of each item. This allows the engine to organize and group products, enhancing the relevance of recommendations by ensuring they fit within related categories or themes.
In stage one, the recommendation engine establishes a comprehensive product profile by combining these essential data sources. This foundation enables the engine to produce initial recommendations based on core product similarities and transaction patterns, setting the stage for the more nuanced analysis in stage two.
For stage two of the recommendation generation process, the engine shifts from basic data processing to a more refined analysis that considers several dynamic, external, and behavioral factors. This stage builds on the initial data and broadens the context around the product to enhance recommendation relevance. Here’s how each component of stage two contributes:
Customer Behavior: By analyzing user interactions and preferences, the engine tailors recommendations based on individual and collective user behaviors, such as products frequently bought together, purchase history, and a range of insights which gives a personalized touch to the suggestions.
New Products & Stock Info: The engine keeps recommendations fresh and up-to-date by integrating previous data onto new products and inventory levels. This helps promote newly available alongside your bestsellers.
Asymmetrical Product Combinations: Unique and non-traditional product pairings are explored here to suggest combinations that users may not expect but are often purchased together, thus enhancing cross-selling opportunities.
External Data Sources: Information from outside data feeds enriches the recommendations, incorporating trends, seasonal demands, or external factors like regional preferences, which aligns suggestions more closely with current market conditions and user needs.
Supplier Preferences: By considering supplier-defined priorities, the engine can boost visibility for preferred products, ensuring alignment with business strategies while maintaining relevance to the user.
This stage enables the recommendation engine to provide nuanced, adaptable, and highly targeted product suggestions, creating a sophisticated match between user interests and product offerings.
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The Woopz software integrates effortlessly with your existing systems, including POS, ERP, and inventory management. Everything is automatically updated, ensuring smooth and efficient operations across all types of retail stores.
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Woopz is a comprehensive, one-platform solution of digital assistants tailored for retail stores. Our mission is simple: to streamline operations, enhance customer experiences, and drive sales with innovative, adaptable technology. From automating pricing and providing real-time inventory updates to delivering smart product recommendations and endless aisles, Woopz helps retail businesses thrive.
Since 2019, we've been dedicated to research and development, collaborating with Norwegian retailers and Innovation Norway to create solutions that meet the evolving needs of the retail industry. In the spring of 2024, we took the leap into the commercial market and are now focusing on continuous development and scaling within Norway.
At Woopz, we are risk-takers who love to grow alongside our partners and clients. We believe in the power of collaboration and are proud to work with a range of agencies and customers today, building success together.
