The retail and consumer packaged goods (CPG) industry has undergone significant transformations due to advancements in technology. Technological innovations have reshaped various aspects of the industry, including customer engagement, inventory optimization, and supply chain management. These innovations have helped drive digital transformation, improve operational efficiency, enhance the customer experience, and promote sustainability.
In our previous blogs, we discussed the transformative capabilities of AI in demand forecasting and planning for the retail and CPG industries, as well as the data science and ethical considerations behind it. Now, in this final installment of our series, we'll explore how businesses can maximize their margins by integrating, adopting, and executing AI-powered solutions.
The retail industry is going through a period of major upheaval. AI is transforming the landscape at a rapid pace. Grand View Research evaluated the market value at USD 5.79 billion in 2021 and this is expected to grow at a 23.9% compound annual growth rate (CAGR) from 2022 to 2030. For retailers, this translates into a need to adapt to an entirely new paradigm of customer expectations.
Following our well-received presentations with Walgreens’ Andy Kettlewell at this year’s NRF and RILA LINK shows, we were fortunate to present another testimonial breakout session—this time from a CPG perspective—at Gartner’s annual Supply Chain Symposium/Xpo in Orlando. One of the things I’m especially proud of since the beginning of antuit.ai is the number of customers who’ve been eager to come forward and share their positive experiences with us.
Effective inventory management is an essential aspect of running a successful retail business. Whether you own a small boutique or a large chain of stores, managing your inventory properly can help you reduce costs, increase efficiency, and maximize profits. However, inventory management can be a complex and time-consuming task that requires a significant amount of planning and attention to detail.
Digital code behind a digitized face Welcome back to our series with Nicholas Wegman, Ph.D., Senior Director of Artificial Intelligence and Alex Barnes, Senior Director of Product Management, as they continue to discuss the science behind Artificial Intelligence (AI) and reveal how it can specifically increase retail/CPG margin. In the second part of our three-part series, we’ll delve deeper into the data science of AI, and why it’s not working for every business.
We know that for many retailers and CPG companies, AI/ML solutions represent a game-changing technology. Yet, this journey seldom comes without a few expectable “growing pains”—from adoption and scale through a fully-fledged data-driven transformation. For multiple internal stakeholders across an organization, the end-to-end process can seem quite daunting—especially without a well-defined plan.
Effective retail price optimization is essential for increasing profitability, driving revenue, and enhancing customer satisfaction. As we navigate through the economic aftermath of the pandemic—inflation, supply chain uncertainties, and other lingering disruptions, successful retailers are leveraging advanced, data-driven price optimization solutions.
Deciding what metrics to measure leads to the next big issue: How to Measure. Often the problem is not so much in the measuring but in the comparison. Measuring the revenue is only useful if you can compare it to a baseline of revenue to know whether the result is good or not. Summarized, there are four basic techniques, each with advantages and disadvantages: Test and Control: Typically execute new pricing in some locations while using the legacy process in others.
