AI merchandising revolutionizes the way products are promoted and sold online by using artificial intelligence to enhance visibility and sales. You can score the following departments either by prioritizing them or not. In Local Express the primary areas of focus in AI merchandising include "Brand," "Manufacturer," "Tags," and "E-commerce." Each section has a specific role in optimizing product listings and positioning to attract more customers. Access the feature via Partner Web -> Operation -> Merchandising.
Brand Section
The "Brand" section is crucial for retailers who want to emphasize specific brands in their inventory. Here, you can select which brands should be featured more prominently or even add new brands to your list. This customization ensures that the brands that align with your business goals or current marketing strategies get the spotlight.
Manufacturer Section
The "Manufacturer" section allows the addition of products from various manufacturers. By managing these two sections effectively, businesses can tailor their online presence to better reflect their strategic partnerships and inventory strengths.
Tags Section
Existing product tags include “sale items, featured, breakfast, seasonal, vegan” or others depending on the store. You can score any of the products by using these existing tags.
E-commerce Section
In the eCommerce section, the calculation of sales focuses on identifying the most popular product sold within a given period for a specific store. This helps you understand which items are most popular so you can boost sales by promoting these top-selling products or exploring strategies to increase sales of other items sold less.
Considerations and Challenges
While AI merchandising offers numerous advantages, it also presents some challenges. One significant drawback is the potential for unpredictable results due to the AI algorithms. These algorithms, while sophisticated, can sometimes produce unexpected outcomes that may only sometimes align with the retailer's strategic goals. Users need to monitor these systems closely and adjust settings as needed to ensure that the AI's actions are enhancing rather than complicating sales efforts. The reason for the drawbacks is that dynamic scoring gives dynamic results when new products appear in stock or when old ones are out of stock.