Assortment Optimization is a powerful tool for retailers to increase profit and improve customer experience and loyalty. It combines cutting-edge machine learning techniques, an agile continuous improvement methodology, and flexible customization to accommodate unique business requirements. Like other related solutions, assortment optimization utilizes machine learning techniques and AI to find the optimal assortment for every SKU in each store location. However, assortment optimization offers other unique benefits.
AI-based systems do not always make better decisions than humans. Find out why, and how to make sure your system is getting the best of both worlds.
Man vs. Machine
When it comes to Assortment Optimization, AI (artificial intelligence) and machine learning play a key part behind the scenes. While a human perspective helps, there are two important factors that make AI very important: scale and automation. For example, a large retailer with 1,000 physical stores and 10,000 products has as many as 10 million assortment decisions to determine which stores should carry which SKUs, what quantities, and when. Even though a significant portion of product offering is chainwide, that still leaves a formidable number of micro-decisions to be made. This amount of decision-making for humans is inefficient and not very profitable.
Assortment optimization is a major focus area for retailers at the moment. Retailers need more accurate SKU rationalization, a better understanding of what to buy in the future, and localized assortments that drive sales. Getting this right is crucial in the market today. There are a few traditional ways that retailers plan out their assortments. They either 1) think through their assortment from a business process standpoint, 2) look at loyalty and shopper data to determine assortment, or 3) think of a brand-new method to determine assortment.