LLMs and E-commerce: How AI is Changing the Way We Shop Online‍

What is LLM?

Large language learning models (or LLMs) are basically the text-handling part of AI. On e-commerce sites, they are used to interpret customer languages, pick out important words, and identify the overall context of what’s being said. They are trained on massive amounts of data, such as customer transaction records, website analytics, and social media insights, to generate text that sounds human and natural.

Most LLM are built on a transformer architecture, which has several mechanisms—such as self-attention, positional encoding, multi-head attention, and so on—to process and understand the relationships between different parts of the text. 

For example, with the self-attention mechanism, the model looks at each word in a sentence against other words and calculates attention scores for each word pair. They then determine how much focus each word should have in a given sequence based on that score. Over time, it starts to pick up on common language patterns to generate material that is super relevant to customers.

LLMs can perform a wide range of language tasks, from simple text classification to text generation, with high accuracy, fluency, and style.

Top LLMs for E-commerce Businesses

GPT 4o

GPT4o is the latest and most advanced large language model created by OpenAI. As the successor to the original Generative Pre-trained Transformer 4, this model boxes all the text, image, video, and voice capabilities into one system.

One of the most upgraded features of this model is that it supports real-time voice exchanges. The system can listen to your customers in real-time and detect a wide range of emotional expressions in their voice. For example, if your customer is sounding frustrated, the system can identify that and create a response that is empathetic and understanding. 

Claude 3.5 Sonnet

Claude 3.5 Sonnet, the latest model from Anthropic, is the first release in its forthcoming Claude 3.5 series.  According to Anthropic's report, these models have bigger improvements than predecessor Claude 3 opus and GPT-4 by maintaining the 200,000 token context window at a cheaper API cost. This upgrade is 6 times more advanced than GPT-4's 32,000 token capabilities.

Claude 3.5 Sonnet has the most advanced vision model  which can transcribe text from imperfect images or generate insights from charts. This can be beneficial in e-commerce operation such as automated product categorization, visual search, and inventory management

It also introduced a new feature called Artifacts which lets users interact with generated text documents, in a separate dynamic panel. This is particularly helpful for collaborative content creation and product management within eCommerce teams.

Gemini 1.5

Gemini 1.5 is Google Deep Mind's latest and most advanced large language model. A significant upgrade has been made over its predecessor, Gemini 1.0, and other LLMs as it now provides one million-token context window—the longest context window to date compared. This means it can process and remember massive amounts of information in a single interaction.

With its ability to process huge amounts of data, Gemini 1.5 Pro can help e-commerce platforms analyse customer’s purchasing behaviour, identify top market trends, and track inventory movement.

Llama 3.1

Llama 3.1 is the largest open-source language model—developed by Meta. One of the most notable improvements in Llama 3.1 is the expansion of context length from 8,192 tokens in its predecessor, Llama 3, to a massive 128,000 tokens. This allows the model to handle much longer text inputs without losing context.

Another thing that sets Llama 3 apart is its enhanced training data. The model was trained on over 15 trillion tokens. Plus, Meta added eight additional languages for this model. Due to this, users will also notice major improvements in the performance.

Furthermore, Llama 3.1's multimodal capabilities allow it to process both text and images. This feature is particularly beneficial for understanding customer questions that include images, such as identifying a product from a photo or troubleshooting issues with visual guides.

Key Benefits of LLMs in E-commerce

Making Site Search Smarter

One of the main reasons customers abandon online stores is because they can’t find the products they are looking for easily. Online retailers often have large product catalogues, which makes it difficult for systems to find the specific products customers desire.

LLMs can ease this issue by confidently assessing what customers are trying to find and proactively presenting the search results they want. When a customer question comes in, the LLM tries to interpret the intent and semantics of the question by referencing past interactions (if available) or the general context of the site's content.

Additionally, advanced LLM can recognise synonyms and related keywords of the customer question. They then add the relevant content in the search results even if the exact keywords aren't used. For example, searching for "affordable laptops" could also return results for "cheap notebooks.”

Boosts Customer Loyalty

Knowing and understanding what your customer is looking for is one of the main components of an excellent e-commerce experience. Your customers are more likely to be happier when you acknowledge their' preferences and deliver exceptional support that is specifically made for them.

With LLMs, you can recommend specific products that solve customers problems or create a response with a tone that works better for the customers. This personalised strategy can dramatically increase customer satisfaction while also activating one of the loyalty drivers.

Pro tip: Mevrik offers AI assistance during conversations, which automatically understands the context of the previous message and response in a way that meets both customers and business needs.

Increase Production of Top-tier Support Content

LLMs are brilliant technology to write well-structured and precise FAQs. The FAQ answers generated by these models are often grammatically correct and offer flexibility for tone adjustments to match with your brand guidelines. Customers can easily find these FAQs on the product pages or through a chatbot.

In terms of chatbots, the NLP algorithms first identify the intent behind the question. Once it finds the most relevant FAQ that matches the intent, it uses LLM to generate concurrent responses. This not only saves time but also ensures that the responses accurately reflect customer needs and questions.

Pro Tip: Mevrik offers a wide range of FAQ creation tools. It can automatically analyse existing content and previous customer interactions to generate accurate FAQ answers. 

Final Thoughts

It goes without saying that AI in e-commerce platforms should be integrated in a way that is responsible, ethical, and beneficial to customers. An AI-powered platform like Mevrik can help you meet this standard by optimising your e-commerce operations while safeguarding your consumers' data.

Schedule a free demo today to see how Mevrik can help you drive growth while prioritising customer trust.