The Impact Of Large Language Models On Ecommerce

AI models hold significant benefits, especially when GPT collaborates with a search engine on an online shopping platform, it is likely to improve the shopping experience for customers. The Harvard Business Review states, “For companies that manage to leverage GenAI to achieve their business objectives effectively, the benefits are likely to be substantial and rewarding.”

Supporting this view, McKinsey predicts the possible economic effect of generative AI could be between $2.6 to $4.4 trillion worldwide. This growth is anticipated to be fueled by applications like efficient content creation, optimised use of data, improved product finding, AI-enhanced search features, and more efficient personalised shopping experiences.

Large Language Models (and a specific use case, generative AI chatbots) are currently the hottest trend in the tech industry, with numerous ecommerce leaders hopping on the generative AI bandwagon aiming for website enhancement. By leveraging vectors, semantic comprehension, and customization capabilities, language models are spearheading a conversational AI transformation happening globally. Although there are some bugs to be fixed for practical success in the real world, businesses of every scale are diving in to find out if they can achieve significant success with this technology.

So what benefits does AI offer ecommerce?

The main hurdle for every online shopping business is consistency: boosting sales by expanding the range of products and reducing expenses. Achieving success with artificial intelligence in online shopping demands a smooth blend with the shopping experience (UX). Therefore, are big language models suitable and potentially excellent, unsuitable and a risk to profits, or a bit of both?

The brief response: LLMs excel at some tasks but fail at others, and their capabilities could make them valuable contributors to online sales. Just like with other technological advancements, numerous business leaders are exploring how to use generative AI. However, if you’re among some entrepreneurs, discussing generative AI and online shopping might seem confusing. After all, online shoppers have certain must-haves, such as the ability to view various buying options with 100% accurate, contextually relevant details.

In the realm of search, LLMs are not yet at that level. To date, they have a reputation for doing some unexpected things: there’s that hard-to-ignore risk that they might start hallucinating. They’re also likely to share any data they’ve been trained on, which could lead to the leakage of confidential information if the company has provided access. For a website focused on providing accurate information about shopping choices, even a minor error could cause significant issues.

Large Language Models that work well in ecommerce

As usual, we can turn to Amazon for the newest trials with artificial intelligence tools. The company has introduced a chatbot, Rufus, equipped with an LLM trained on the product catalogue and customer feedback, to respond to user questions at different points in the shopping process. So far, feedback on Rufus has been quite negative.

However, this doesn’t imply that LLMs are completely ineffective for online shopping. The issue with many existing generative AI tools is that businesses are using them to substitute current, functional technology rather than just enhancing the online shopping experience. In the realm of ecommerce, in addition to established solutions like vector database technology, they can be quite beneficial.

They are gaining traction in the following areas, where consumers are finding them more user-friendly:

Customer support

“When can we expect this item to be back in stock? If not, what would you suggest as an alternative?” Customers frequently face questions about purchasing before they hit the Buy button. As a seller on the internet, it’s beneficial to provide top-notch, tailored responses instantly. However, what if your customer service team is short-staffed or not available?

If a language model trained with natural language processing (NLP) methods is equipped with the necessary details for answering questions, it can fulfil the needs of customers efficiently. Take, for instance, a customer who has previously reached out to Support. When they return to the website and pose some additional questions to the chatbot, it can utilize the previous conversation’s context to understand the situation better and respond logically.

Content creation

Similar to addressing various extensive marketing requirements, LLMs can efficiently generate text for product descriptions, blog articles, email marketing, and social media posts. They can even tackle all these tasks simultaneously, tailored to the platform and the desired tone. An area particularly suited for LLMs to automate is the creation of compelling purchase confirmation emails that also feature upsell suggestions.

GenAI can manage this task effortlessly when combined with a specialized recommendations model for selecting complementary products. Generative AI can also be leveraged to produce product descriptions in advance, which can then be shown alongside the products. An ecommerce platform can expand on this concept by utilising customer data to generate product descriptions and personalised marketing content in real-time. This approach would significantly enhance the shopping experience for potential customers, making the website appear more like a personal shopping assistant rather than a mere collection of products.

Hyper-personalisation relationships with each consumer

As more businesses have embraced personalization throughout the customer journey, from designing products to reaching out to customers, and from the shopping experience to adjusting prices on the fly, it has set a certain standard for personalised customer interactions.

The era of mass media, where broad advertisements to all potential customers could engage a broad spectrum of clients, is over. Nowadays, technology enables every interaction to be unique and personal, and customers anticipate a personal connection with the brands they deal with. A study by the University of Texas reveals that the desire for personalisation stems from the need to manage and simplify the decision-making process.

When products and interactions are personalised, it creates an environment where customers are at the heart of all business decisions, offering them more control over their interactions. This, in turn, affects customers’ decision-making as the information provided is customized to their individual needs, making it more relevant to their requirements.

This customised information also simplifies the decision-making process for customers when choosing products and brands. The mix of convenience, understanding of customers, and emotional connection leads to customer loyalty and increased sales for companies. Customers who are emotionally connected and loyal spend twice as much as those who are not engaged, and 80% of them will recommend the brand to their friends and family.

See our study on the distinction between the top 30 hyper-personalisation vendors

Originally published at https://swifterm.com on October 28, 2024.