You've probably heard about artificial intelligence and machine learning more in the last two years than you did in the previous 20. That's because advances in technology have accelerated exponentially, with many of the world's largest brands, from Walmart and Amazon to eBay and Alibaba, using AI to generate content, power recommendation engines, and more.
Investment in this technology is significant and predicted to grow exponentially: the value of AI in retail will reach $7.14 billion in 2023, $85 billion by 2032.
As brands of all sizes look to this technology to see how it can fit into their retail strategies, let's take a look at some of the ways AI and ML can be effectively used to drive business growth.
How AI is transforming product description generation
One of the biggest hurdles for retailers, especially those with a large number of SKUs, is creating compelling, accurate product descriptions for every new product added to their assortment. Embedding that amount of content can become impossible when you consider the ever-increasing number of platforms on which products can be sold, from third-party vendors like Amazon to social selling sites to a brand's own website.
One area where generative AI excels is in creating compelling product copy at scale. Natural language generation (NLG) algorithms can analyze vast amounts of product data and automatically create compelling, customized descriptions. This copy can also be adapted to each channel, tailoring specific parameters and messaging to specific audiences. For example, a generative AI engine understands the character limits of a particular social channel. It can tailor the copy to those specifications, aligning it with the demographic data of the people who will encounter that message. The level of personalization at scale is staggering.
Leveraging AI in this way has the potential to help brands achieve their business goals by creating compelling, search-optimized content that increases product discoverability and conversions.
Using AI to catalog information
Another area where AI and ML excel is in cataloging and organizing data. Again, when brands deal with product catalogs with hundreds of thousands of SKUs across numerous channels, it becomes increasingly difficult to keep information consistent and clear. Product, inventory, and e-commerce managers spend countless hours keeping all product information accurate and up-to-date, but still make mistakes.
Brands can use AI to automate tasks like product categorization, attribute extraction, and metadata tagging, ensuring accuracy and scalability of data management across all channels. AI takes the guesswork and effort out of tedious tasks, enabling broad business impact: accurate product information reduces returns, and intuitive data architecture improves product searchability and discoverability.
Creating a more personalized customer experience
As online shopping has evolved over the past decade, consumer expectations have changed too. Customers rarely visit a company's website and browse countless product pages to find the product they're looking for. Instead, they expect a curated, personalized experience regardless of the channel through which they encounter a brand. A report from McKinsey Research shows that 71% of customers expect personalization from brands, and 76% are frustrated when they don't get it.
While brands have been delivering personalized experiences for decades, AI and ML have opened up entirely new avenues for personalization. Again, AI enables unprecedented levels of scale and nuance in personalized customer interactions. By analyzing vast amounts of customer data, AI algorithms can connect customer order history, preferences, location, and other identifying user data to create tailored product recommendations, marketing messages, shopping experiences, and more.
A focus on personalization is key to achieving business strategies and benchmarks. Personalization efforts lead to higher conversion rates, increased customer engagement and satisfaction, and improved brand experience, which translates into long-term loyalty and customer advocacy.
Building better search with AI and ML
Search capabilities are constantly evolving, with the integration of AI and ML being the next leap. AI-powered search algorithms can process natural language better, helping brands understand user intent and context, making searches more accurate and relevant.
Additionally, AI-powered search provides valuable insights into customer behavior and preferences, allowing brands to optimize their product offerings and marketing strategies. By analyzing search patterns and user interactions, brands can identify emerging trends, optimize product placements, and tailor promotions to specific customer segments. Ultimately, this enhanced search experience increases customer engagement, driving sales growth and fostering long-term customer relationships.
Supporting customers with AI-powered tools
The inherent benefit of AI and ML tools is that they always work and never wear out. This fact is most felt when applied to customer support, where tools like chatbots and virtual assistants enable brands to provide instant, personalized support around the clock, worldwide. This automation reduces wait times, improves response efficiency, and allows staff to focus on higher-level tasks.
Similar to personalization engines used in sales, AI-powered customer support tools can process vast amounts of customer data to customize responses based on a customer's order history and preferences. And like personalization, these tools can dramatically reduce the time customer support teams spend on low-level inquiries like checking order status or processing returns. Leveraging AI in support allows brands to allocate resources in more effective ways without sacrificing customer satisfaction.
Brands are still only scratching the surface about the capabilities of AI and ML. But early indicators show that the technology can have a significant impact in driving business growth. Embracing AI will enable brands to transform operational efficiency while keeping customers satisfied.
