Powering the Retail Business with Smart Automation 

In the modern era of the business landscape, companies across industries are progressively leveraging Artificial Intelligence and predictive analytics to get real outcomes. Akin to other industries, the retail sector also requires a combination of automation and predictive analytics.

Making a difference for the retail business, automation should go hand in hand with AI and predictive analytics. And leveraging these technologies together retailers can have access to gauge their business to vie with the major retail giants while boosting profit margins and maximizing revenue.

So, how to utilize automation with AI and predictive analytics to the retail business.

Inventory Management

Effective inventory management requires a retailer to precisely envisage customer demand, vendor performance, and future inventory levels when new orders arrive. Additionally, retailers are often challenged to determine an optimal size distribution, while distinct products and styles will experience distinct consumer demand across various locations. So, there is a place where predictive analytics comes in.

A smart predictive analytics system will create suggested order quantities to bring the right products to the right store at the right time. That process will enable retailers to generate open-to-buys based on merchandise and assortment plans while accounting for several business rules, vendor constraints, and future demand. It can also assist in solving some common challenges among retailers.

Social Media Management

Social media management is one of the biggest challenges for retailers as engaging with actionable posts and evading non-actionable posts is a time-consuming process. So, addressing this challenge, brands now can develop an AI model to extract spam, news articles, retweets, international posts, and other non-actionable content, without wasting agents time to review posts. Furthermore, it can be leveraged in prioritizing posts in the order of importance.

Price Management

Using predictive analytics and AI, retailers will get access to optimize prices throughout the entire lifecycle of a product, from initial introductory prices to regular, promotional and finally, markdown prices. Most retailers just arbitrarily mark down their products in the last effort to chuck out the merchandise. In many cases, they are also making no margins on markdowns, and just trying to clear their shelves and recoup costs. Thus, predictive analytics can be helpful here, working proactively on an optimal markdown strategy. It accounts for which products retailers have an excess supply of, how quickly they want to remove them and the best pricing strategy to get them there, along with increasing gross margin.