Generative AI Ushers in Retail’s New Era

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For many, 2023 was the year of AI. Talk of the revolutionary new technology was everywhere, dominating media headlines, boardroom discussions, and pop culture. After all, the technology has essentially had its “iPhone moment”—the point in time when the technology suddenly became useful and accessible to the masses.

Thanks to the public adoption of readily accessible generative AI tools, everyone can see the technology in action and use it themselves. AI is quickly advancing far beyond simple customer service chatbots and image generators, and generative AI tools are already demonstrating their impact across industries. In fact, generative AI is expected to become a $1.2 trillion market by 2032, and rising demand for AI products could add $280 billion in new software revenue.

For retail, generative AI opens a world of new opportunities and use cases, and retailers and brands are only beginning to understand the short- and long-term implications of the technology. Building on the current use cases for AI, generative AI could soon help retailers streamline their data management and ultimately automate customer engagement practices.

Before diving in further, it’s important to grasp the distinction between the ways that AI traditionally has been used and what makes generative AI particularly promising.

Understanding AI’s Current Implications

While AI may seem like a brand-new technology that is taking the world by storm, artificial intelligence has been used for decades to help manage data and accomplish specific tasks with predefined rules.

AI technology can analyze and make predictions from data, excelling at pattern recognition, but it lacks the ability to independently create new material or understand concepts outside of what the model was built and trained specifically to do. That’s where generative AI comes in.

The fundamental technology behind generative AI and new large language models deals with language generation. In other words, this technology can now consume text or data, interpret it, and output generated responses and information to the user. While it is a significant step forward, generative AI can only work off the material it is given, limiting its current output.

For instance, a retailer could provide a given AI tool with a spreadsheet that has three columns labeled “store name,” “store location,” and “store sales.” Assuming the data within each column makes sense, traditional AI can learn what each column represents based on its name and form connections between the columns to understand how they relate to each other.

Using generative AI, the retailer can then ask the tool to output which store had the highest sales for this quarter of the year. Right now, this is about where the extent of the technology lies, but as generative AI advances, the technology could prove incredibly useful in the following areas:

Cleaning Datasets with Generative AI

Retailers sit on mountains of data, but that data isn’t always perfectly packaged or consistent. Whether it’s a user input error or missing information, data gaps present a challenge that generative AI could help overcome.

Combined with traditional AI’s ability to analyze and interpret large datasets, generative AI can step in to fill the gaps. For example, if a store code is formatted wrong, includes a typo, or is missing altogether, generative AI can potentially fix issues within the data itself, ensuring that a data set is clean and useful for future applications.

AI is going to keep developing, and as it grasps increasingly complex relationships within provided data, generative AI can work in tandem to take retail capabilities to the next level. This is where the technology’s ultimate promise lies.

Revolutionizing Customer Engagement With AI-driven Insights

Think about a marketing executive at a retailer with a small team at their disposal. It might take months to manually clean a customer dataset, run it through traditional AI to delineate patterns, and then formulate customer engagement strategies based on the results.

Yet as traditional AI becomes increasingly skillful at drilling down into data and generating deep insights, a retailer can connect various generative AI tools across language and image generation to implement consumer engagement strategies in a fraction of the time it currently takes.

First, AI will analyze shopper behavior, preferences, and purchase history to create individual customer profiles. Then, the platform can pull strategic insights that will empower brands and retailers to connect with customers in all new ways via instant, smarter engagement strategies.

For example, targeted marketing plans that previously took hours of manpower to generate could be formulated in seconds, and hyper-personalized campaigns could be sent out to customers instantly.

Say an unexpected item increases in popularity over the previous week. Traditional AI can identify trends like this almost instantaneously and, in the not-so-distant future, feed that to a generative AI model that then drafts personalized email copy to offer the product on promotion to customers. If connected to an automated distribution mechanism, that offer can be quickly sent out, creating instant engagement.

Nothing helps foster customer relationships more than savings on the items that matter most to them, and AI can help retailers understand their shoppers better than a team of humans ever could through customer segmentation and personalized communications. By delivering relevant offers and promotions to their customers, brands will see heightened customer satisfaction and loyalty.

Retail’s Future, Bolstered by Generative AI

While generative AI still often needs human oversight, AI and data analysis can provide strategic personalization for retail now. As the technology develops further, retailers will be able to combine generative AI with today’s AI-driven capabilities to create unmatched customer engagement.

From there, the possibilities for automated customer engagement become endless, and it’s why the retail industry is only beginning to understand the technology’s future implications. Brands that take the time to invest in present AI capabilities and future generative AI tools will be one step ahead at realizing the ultimate potential of revolutionary personalization.

Shekar Raman is CEO and cofounder of Birdzi, a grocery retail AI solutions company that was inspired by an idea his 11-year-old daughter had about locating products in the supermarket. He is passionate about building data-driven technologies leveraging AI and machine learning to help retailers and brands elevate the customer experience.

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