E-Commerce Site Search and Merchandising

Where Do Large Language Models Fit Into the Future of E-Commerce Search?

By Paul Edwards


E-Commerce Site Search and Merchandising

Where Do Large Language Models Fit Into the Future of E-Commerce Search?

All of us have used a search box. Its purpose is well defined — you type something in, and you get results to what you were looking for. If you are on an e-commerce site, those results are products or services. Well, that’s what should happen, anyway, but not every search vendor is capable of serving up results that are relevant, personalized, and optimized for important e-commerce metrics, like conversions, revenue, and add-to-cart rate. 

Despite the holes already in the market, an evolution is happening underneath the current landscape of e-commerce site search, and it’s made a big splash in the court of public opinion over the past six months. That’s right — generative AI has entered the chat, and it’s not leaving anytime soon. And, there is increasing curiosity around the tech behind it, known as “large language models” or LLMs, which use massive amounts of data to learn and generate language that appears human-like in response to different queries. Some call it the future of product search; others say it could never replace it. 

Yet, we should understand that LLMs are a two-sided coin — on one side, there are all the possibilities for innovation, and on the other, there is a fair amount of risk. While LLMs show a lot of potential, they also have a long way to go in terms of development (currently, valid questions are being raised about the ethics and potential risks associated with the global AI race). It’s especially important to remember that LLMs hold a lot of powerful (and dangerous) capabilities, like “Do Anything Now,” where people can subvert the AI’s purpose for their own amusement or gain.

All in all, it isn’t a perfect technology — LLMs require ongoing refinements and adjustments to ensure consistent accuracy and relevance. Even then, a big question on the minds of commerce professionals remains: Where do LLMs fit into a shopping scenario? Is it possible now? If it isn’t, will it eventually be possible?  

Chatbot with LLM Communicating with a Customer

Since artificial intelligence has never been (and will never be) a finite line drawn in the sand, this question is more complex and multi-faceted than it seems. 

AI Should Already Be Embedded in E-Commerce Site Search 

AI in e-commerce search today drives revenue by using recall and ranking algorithms to deliver the most relevant results that match your shopper’s intent. This happens through a combination of natural language processing (NLP) and machine learning (ML). NLP breaks down the context behind shoppers’ queries, and ML learns about your business from analyzing these behaviors. This self-learning AI becomes smarter daily and can rapidly adapt to your customer’s ever-changing needs as you continue to scale. 

But, your business will need to see your customers as more than just “customers” to succeed in the current competitive landscape. The winners in e-commerce find the “seekers”. They look beyond a person’s singular identity as “the consumer” to uncover their true motivation behind wanting a certain product or service and build digital experiences for them. And this is where AI can help tremendously. 

Consumers want AI in e-commerce to feel like a real personal shopper, and that’s why it needs the most extensive data set (based on product data and individual customer behavioral data) to inform and personalize the customer experience. When implemented properly, the AI underneath your conversational interface should usher shoppers down their desired purchase path — and beyond. 

Data Point Visualization - Customer to Product Data

As they are today, LLMs are too dependent on prompts we give to them to truly assist the shopper on the other side of the screen. Even though LLMs have been trained on a vast amount of information, they haven’t been trained on your specific product catalog — or the product catalogs of others in your industry. And while they have a knack for returning the right class of product, LLMs ultimately know nothing about your company’s proprietary information or performance metrics, making its ingenuity a bit trivial for the time being. 

As we look towards the future, we must recognize that the potential for LLMs to revolutionize e-commerce search lies in their ability to provide shoppers with the information and results they need without prompting. 

Then, Why Should E-Commerce Care About LLMs and Generative AI?

As we all probably know, ChatGPT took the conversation about generative AI to the mainstream and provided an environment where anyone could interact with it. Not only has it caused a lot of discussion around the current uses of AI within the e-commerce industry, but it also has a lot of professionals wondering what LLMs look like when we picture the future of e-commerce

LLMs have a lot of potential use cases within the e-commerce industry, especially when it comes to brands and retailers assisting their customers. But how do you keep it focused on e-commerce, and is there a certain way to leverage it successfully? 

It all comes back to the seeker and unlocking the full potential of their customer journey. Take, for instance, when somebody is looking to decorate their living room with neutral, earthy tones, but they don’t know how to verbalize this “want” into a search query. Although the search terms might be unknown to the seeker, an LLM can bring context to their particular need through a simple conversation. 

Again, there is no concrete answer here. We just have to think of AI as the digital version of the industrial revolution — it will absolutely revolutionize our world and how we live, and there is no denying its potential. Nonetheless, we must strike a balance between that innovation and creativity inherent in LLMs with the proven AI tools already available on the market to commerce businesses, like site search and merchandising capabilities. 

Since the e–commerce industry is laser-focused on building better shopping experiences, we must lean into what works today — but never be afraid to remain open-minded to future possibilities. While LLMs indicate a smarter and more intuitive future in e-commerce, they aren’t quite there…yet.

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Paul Edwards

Technical Product Strategist at Bloomreach

Paul is a Technical Product Strategist versed in fields as diverse as e-commerce, machine learning, telecommunications, and aviation. He spends his time focussing on the application of cutting-edge technologies and hybrid algorithms in the e-commerce space. His spare time is consumed with the renovation of a cottage in the Surrey hills.

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