Increase Average Order Value
Whether you want to sell a complete outfit set, or let customers build the perfect sound system, “complete the look” recommendations are a proven way to increase average order value.
Whether you want to sell a complete outfit set, or let customers build the perfect sound system, “complete the look” recommendations are a proven way to increase average order value.
Shoppers experience tremendous frustration when they fall in love with an item, only to find out it doesn’t come in their size. Kibo makes it easy to avoid annoying your shoppers by taking into consideration past shopping history and geo-targeting parameters.
Not only does shopper preference play a role in product recommendations ROI, but so too does selecting those offers with the highest profit margin in mind. Kibo allows for the ultimate combination of individual insights and filtering based on unique business metrics.
Infuse recommendation algorithms with everything you know about the individual—including historical data, third-party insights, and real-time visitor behavior.
Use one of our many algorithms such as:
Or, have the flexibility to upload one of your own.
Automatically avoid confusing or frustrating shoppers by prioritizing rules to ensure shoppers only see prioritized experiences—for example, only show products that are in their size and are in stock.
Slotting enables slot-level control within product feeds, enabling you to mix, match, and sequence individual algorithms for ultimate control, or let Kibo AI sequence them for you. Further fine-tune with dynamic visitor history filters by feeding in suggested products based on behavioral data.
Empower the entire team with a powerful marketer-friendly interface. Remove the burden on IT and give your team the power to forge ahead unencumbered by technical requirements.
Send highly relevant and scalable recommendations via email that are loaded and displayed upon open, use dynamic run-time filters to pass through parameters to your ESP to easily make changes to existing and new email recommendation strategies.