How AI Can Help Retailers Regain A Profit

Following the pandemic, there has been a lot of talk about retail theft causing profit losses. Many retailers have closed stores across the nation due to organized retail crime and shoplifting. However, while retail theft is still a prominent issue in the U.S., recent news suggests this narrative might not be as accurate as sources originally claimed it to be. Regardless, the bottom line is retailers have been increasingly losing revenue across the board. Even if we weed out retail theft, there are still many other reasons for profit losses, from inflation to escalating costs to retailers’ inability to accurately predict consumers’ demands and manage inventory.

Retailers have an opportunity to recoup a substantial portion of their revenue through the use of tools like AI assistants. These modern technologies can analyze retailers’ performance drivers, helping to unveil new revenue opportunities. There are a few key areas to which retailers can deploy AI to unveil instant insights into performance and regain some of their lost margins.

Optimize Your Product Mix And Inventory Levels

Product portfolio analysis, or SKU rationalization, involves analyzing sales data, profitability, and demand to determine which specific products retailers should keep, improve, or discontinue. This strategy is critically important to streamline stock management and avoid carrying excess inventory into the next year – a problem that plagues the majority of supply chain managers. A recent survey revealed that only a third of supply chain executives anticipated their warehouse inventories returning to normal before the end of the year.

SKU rationalization is often a complex and error-prone process for retailers. It’s subject to biases and requires significant human effort. Analysts dedicate extensive hours to cleaning and consolidating data sources before manually conducting tests, while executives are left waiting for answers that could take a month to arrive. The time lag between gathering the data and delivering the report renders the information outdated and irrelevant. This lack of timely and accurate insights leaves retailers scrambling to determine which products they should maintain and which they should discontinue, impacting inventory management and profitability.

Through a combination of generative AI and machine learning, AI copilots can help retail organizations automate the SKU rationalization process, from collecting and integrating the data to analyzing it. This tool can serve as a sort of “AI assistant” for retail executives, providing an interactive and conversational way for them to obtain full access to their data and truly understand it. They can drill down into sales trends and consumer behavior by asking specific questions and receiving immediate, easy-to-understand responses. By eliminating products that drain resources, retailers can channel investments towards high-performing lines that contribute significantly to revenue and profit. 

Forecasting Consumer Demands Ahead Of Seasons

Retailers must strategically predict consumer demand to plan stock levels and pricing. This can be especially high pressure around brief holiday seasons with a limited window for sales. For instance, Thanksgiving and Christmas are huge revenue drivers for retailers. The planning process commences several months prior, and during the peak of the season, teams are constantly modifying plans, rearranging inventory, meeting orders, and responding to unexpected challenges.

AI assistants offer retail executives detailed, data-driven answers to abrupt performance changes. They can dive deep into data, within seconds, to identify the root cause of sudden demand spikes, as well as dips in promotional effectiveness, providing actionable insights and suggestions to optimize performance. To maximize sales during the holiday season, retailers must be adaptable, act before opportunities pass, and respond quickly to changing situations.

Imagine a retail data science team is seeking to boost the sales of perishable goods during the winter holiday period. They could leverage an AI analyst to receive pricing recommendations, classified by their projected revenue contribution. Furthermore, the AI assistant could suggest transferring inventory between stories to maximize their sell-through and minimize spoilage. From account type to competitor products to sales activity, these recommendations encompass all the relevant factors. 

Find New Ways To Make A Profit With AI

While certain factors impacting revenue loss can’t be changed, modern AI tools present an avenue for retailers to offset these losses and thrive in other areas. Optimizing product portfolios and forecasting consumer demands are just two of the countless benefits AI assistants bring to the table. AI assistants can be a retailer’s secret weapon, giving them a competitive edge and enabling them to make a revenue rebound.

Contributed by Pete Reilly, COO at AnswerRocket.