Forward-thinking retailers are embracing AI to transform the customer experience and generate more cash flow. They realize that it is imperative to understand the customers' preferences. The modern data landscape has a staggering amount of data from various sources, and data-driven insights enable retailers to predict customer behavior and offer a superior CX.
Retailers, at the core, must maintain an optimal assortment of products to sell in stores or risk losing customers. The number of products is increasing at an astonishing rate; shelf space is not. As a result, retailers need a scalable solution. Algorithmic merchandising optimization allows retailers to forecast demand, maintain the right merchandise assortment at each location, and balance inventory across store networks.
If you're looking at relieving some of your inventory bottlenecks, we at ElectrifAi can give it to you in 6-8 weeks. We are working with an apparel and footwear retailer with 1000+ locations and sales of over $500m. Their window of opportunity is limited to fashion trends every season, sometimes even weeks in the case of fast fashion. This makes the merchandise a highly perishable product. The solution we offer balances inventory across stores to maximize the opportunity to sell and minimize the amount of stock needed. We helped them with their Merchandise Flow Optimization. It addressed their warehouse to store replenishments and store-to-store transfers, allowing them to operate efficiently while generating free cash flow.
Our solution recommends an optimized assortment of merchandise likely to attract customers. It considers socio-demographic factors like wealth, income levels, neighborhood, education, and product attributes like color, style, and size. In addition, factors such as store size, store traffic, shelf space, and current weather conditions are applied to give it a local context.
The replenishment model considers the predicted sales, store and warehouse on-hand quantities, and delivery lead time to automate replenishment orders. This saves time for store personnel who can focus on providing a better customer experience.
The store transfer model leverages the sorting and searching algorithms to figure out the fast- and slow-moving products so products can flow smoothly through the store's network to meet any additional demand. In addition, it identifies sluggish inventory to avoid its accumulation. Also, AI and ML models can help optimize routes by analyzing a range of data points such as traffic patterns, weather conditions, delivery schedules, and even driver behavior. This data can be used to create a model that predicts the most efficient route for each delivery, taking into account factors such as delivery time, fuel consumption, and vehicle capacity.
The solution works alongside your current platform and applications and does not require complex re-platforming or data migration. This ease of implementation makes it possible to start showing outcomes in 6-8 weeks.
The right product mix leads to more sales, higher margins, efficient operations, and a loyal customer base. Reducing inventory carrying costs is an effective way to improve productivity and generate free cash flow.