The eCommerce industry has witnessed tremendous growth in recent years, transforming the way people shop and changing the eCommerce landscape. With the rise of online shopping, eCommerce businesses have flourished, providing convenience and accessibility to customers worldwide.
However, to stay ahead in this competitive market, it's crucial for online retailers to keep abreast of current technological developments and embrace the future of online retail. In this article, we will explore the impacts of incorporating artificial intelligence (AI) tools to enhance the customer experience, boost sales, and streamline operations.
Let's break down some of the ways this technology is revolutionizing eCommerce.
1. Improving eCommerce Search and Product Discovery
Product discovery is difficult. Serving the exact right products to customers exactly when they want to see them is tricky, and is still limited by technology. Legacy search and product discovery technology runs on keyword-matching, which blindly interprets search queries and struggles with complex searches and long tail keywords.
Unfortunately, customer behavior has changed to the point where omnichannel experiences dominate and long tail searches are becoming more common; legacy technology struggles to keep up. AI-first technology, however, is far more versatile.
Trained on far more data than legacy solutions, its dynamic, self-learning nature allows it to go beyond relevancy and deliver search results that are individually personalized, buyable and optimized for revenue. This advanced technology improves customer retention by aligning search results to individual customer preferences -- without requiring manual intervention the way legacy engines do.
What is manual search intervention? Well, that gets into our second point.
2. Reshaping the role of the Merchandiser
Legacy search engines require hundreds, if not thousands of manually curated and maintained search rules in order to deliver relevant search results. While AI-first engines still allow merchandisers to input manual search rules, they typically don't need them. (Edge cases like promotional agreements, and other factors that are external to the AI, however, can still require search rules.)
The AI learns dynamically and shifts search results to account for factors like inventory status. With manual search rules, a pinned product may stay at the top of search results long after it's sold out. With AI, that item will be buried as soon as the inventory counter drops to zero. This almost completely removes the need for search rules and is dramatically reshaping the role of the merchandiser.
Merchandisers could spend up to 60% of their time curating and managing vast amounts of search rules. Now, they can spend that time on revenue generating and strategic activities, like investigating new technologies such as conversational commerce and generative AI, fixing data problems that impact the performance of eCommerce solutions, and curating innovative new campaigns that set trends across the retail landscape.
3. Facilitating Hyper-Personalization
One of the key factors that sets successful eCommerce businesses apart is their ability to provide a personalized shopping experience. In fact, 80% of customers are more likely to make a purchase if their shopping experience is personalized.
As mentioned above, AI-first commerce search solutions support something called hyper-personalization. Personalization is the act of customizing eCommerce shopping journeys, and typically has relied on segmentation, using large, demographically sectioned portions of the target audience as the basis for this.
Hyper-personalization takes it one step further, providing individually personalized experiences based on a customer's clickstream data, preferences, and purchase and transaction history. By leveraging advanced algorithms , machine learning and data analysis, AI-powered systems can generate personalized product recommendations, personalized email marketing campaigns, and even dynamically adjust website interfaces based on individual preferences to increase customer satisfaction and drive conversions.
4. Optimizing Inventory Visibility
AI-first search and product discovery technology plays a crucial part in optimizing inventory visibility. Near-real-time data uploads allow retail chains to make their inventory visible online. By dynamically displaying current inventory levels and out of stock notifications, as well as information like in-store product location, AI-first product discovery platforms facilitate the omnichannel retail experiences that consumers demand. This intuitive and state-of-the-art AI search technology ensures that the most relevant search results are displayed that are not only intent and context driven but also take into account inventory status.
This technological innovation also plays into the rise of mobile shopping. A whopping 56% of customers now use their phones when shopping in-store to perform tasks such as helping them locate items, making inventory visibility a crucial part of the customer experience.
5. The Power Of Large Language Models and Generative AI in Product Discovery
The rise of Large Language Models (LLMs), the technology that powers Generative AI, have revolutionized the way we interact with technology. These sophisticated AI systems are capable of outputting human-like text, making them invaluable assets for eCommerce businesses.
Recent use cases of LLMs have been introduced in the eCommerce search and product discovery space. Understanding natural language and consumer intent is crucial for returning relevant search results. These models enable semantic search that is superior to keyword-based systems and rule-based approaches producing high-quality, hyper-personalized customer search results.
This new and rapidly evolving technology has the potential to transform every customer touchpoint and the overall digital experience. Check out our recent eBook and discover how Generative AI is transforming the eCommerce customer journey. Read eBook now
