5 Key Ways AI and ML Can Transform Retail Business Operations

AI and machine learning offer retailers a range of benefits, including automated presentation of product information and improved customer experience.

Jesse Creange, Head of Supplier Data Onboarding, Akeneo, Co-Founder and CEO of Unifai

June 10, 2024

5 Min Read
robot hand holding a shopping cart
AndriyPopov via Alamy Stock

Odds are you’ve heard more about artificial intelligence and machine learning in the last two years than you had in the previous 20. That’s because advances in the technology have been exponential, and many of the world’s largest brands, from Walmart and Amazon to eBay and Alibaba, are leveraging AI to generate content, power recommendation engines, and much more. 

Investment in this technology is substantial, with exponential growth projected -- the AI in retail market was valued at $7.14 billion in 2023, with the potential to reach $85 billion by 2032.  

Brands of all sizes are eyeing this technology to see how it fits into their retail strategies. Let’s take a look at some of the impactful ways AI and ML can be leveraged to drive business growth. 

How AI Can Transform Product Description Generation 

One of the major hurdles for retailers -- particularly those with large numbers of SKUs -- is creating compelling, accurate product descriptions for every new product added to their assortment. When you factor in the ever-increasing number of platforms on which a product can be sold, from third-party vendors like Amazon to social selling sites to a brand’s own website, populating that amount of content can be unsustainable. 

One of the areas in which generative AI excels is creating compelling product copy at scale. Natural language generation (NLG) algorithms can analyze vast amounts of product data and create compelling, tailored descriptions automatically. This copy can also be adapted to each channel, fitting specific parameters and messaging towards focused audiences. For example, generative AI engines understand the word count restrictions for a particular social channel. They can focus copy to those specifications, tailored to the demographic data of the person who will encounter that message. This level of personalization at scale is astonishing. 

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This use of AI has the potential to help brands achieve business objectives through product discoverability and conversion by creating compelling content optimized for search. 

Leveraging AI for Information Cataloging 

Another area in which AI and ML excel is in the cataloging and organizing of data. Again, when brands deal with product catalogs with hundreds of thousands of SKUs spread across many channels, it is increasingly difficult to maintain consistency and clarity of information. Product, inventory, and eCommerce managers spend countless hours attempting to keep all product information straight and up-to-date, and they still make mistakes. 

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Brands can leverage AI to automate tasks such as product categorization, attribute extraction, and metadata tagging, ensuring accuracy and scalability in data management across all channels. This use of AI takes the guesswork and labor out of meticulous tasks and can have wide-ranging business implications. More accurate product information means a reduction in returns and improved product searchability and discoverability through intuitive data architecture. 

Creating a More Personalized Customer Experience  

As online shopping has evolved over the past decade, consumer expectations have shifted. Customers rarely go to company websites and browse endless product pages to discover the product they’re looking for. Rather, customers expect a curated and personalized experience, regardless of the channel through which they’re encountering the brand. A report from McKinsey showed that 71% of customers expect personalization from a brand, and 76% get frustrated when they don’t encounter it. 

Brands have been offering personalized experiences for decades, but AI and ML unlock entirely new avenues for personalization. Once again, AI enables an unprecedented level of scale and nuance in personalized customer interactions. By analyzing vast amounts of customer data, AI algorithms can connect the dots between customer order history, preferences, location and other identifying user data and create tailored product recommendations, marketing messages, shopping experiences, and more. 

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This focus on personalization is key for business strategy and hitting benchmarks. Personalization efforts lead to increases in conversion, higher customer engagement and satisfaction, and better brand experiences, which can lead to long-term loyalty and customer advocacy. 

Search functionalities are in a constant state of evolution, and the integration of AI and ML is that next leap. AI-powered search algorithms are better able to process natural language, enabling a brand to understand user intent and context, which improves search accuracy and relevance. 

What’s more, AI-driven search can provide valuable insights into customer behavior and preferences, enabling brands to optimize product offerings and marketing strategies. By analyzing search patterns and user interactions, brands can identify emerging trends, optimize product placement, and tailor promotions to specific customer segments. Ultimately, this enhanced search experience improves customer engagement while driving sales growth and fostering long-term customer relationships. 

Supporting Customers With AI-Driven Tools 

At its core, the main benefit of AI and ML tools is that they’re always working and never burn out. This fact is felt strongest when applied to customer support. Tools like chatbots and virtual assistants enable brands to provide instant, personalized assistance around the clock and around the world. This automation reduces wait times, improves response efficiency, and frees staff to focus on higher-level tasks. 

Much like personalization engines used in sales, AI-powered customer support tools can process vast amounts of customer data to tailor responses based on a customer’s order history and preferences. Also, like personalization, these tools can be deployed to radically reduce the amount of time customer support teams spend on low-level inquiries like checking order status or processing returns. Leveraging AI in support allows a brand to allocate resources in more impactful ways without sacrificing customer satisfaction. 

Brands are just scratching the surface of the capabilities of AI and ML. Still, early indicators show that this technology can have a profound impact on driving business growth. Embracing AI can put brands in a position to transform operational efficiency while maintaining customer satisfaction. 

About the Author(s)

Jesse Creange

Head of Supplier Data Onboarding, Akeneo, Co-Founder and CEO of Unifai

Jesse Creange is pivotal at Akeneo as the Head of Supplier Data Onboarding. In this capacity, he oversees the processes that allow for the efficient collection, cleansing, and enrichment of supplier data, streamlining its integration into Akeneo's Product Information Management (PIM) system. Before joining Akeneo, Creange was the CEO and co-founder of Unifai, an AI company focused on automating data onboarding for PIM systems through innovative data collection, cleansing, and enrichment solutions.  

The acquisition of Unifai by Akeneo marked a significant milestone, bringing together Creange's AI and data management expertise with Akeneo's comprehensive product experience solutions 

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