In this post, we’ll explore various ways that large language models (LLMs) can be utilized for eCommerce beyond just product matching. If you haven’t already, check out our previous post on Using LLMs in eCommerce [insert link here].
Large Language Models have a wide range of applications in eCommerce, from analyzing customer reviews to enhancing product discovery. Beyond product matching, they can help with tasks such as product classification, sentiment analysis, customer support, and even identifying trends in consumer preferences. They can also assist in detecting pricing discrepancies, evaluating marketing content, and automating customer interactions—all key to staying competitive in today’s digital landscape.
Sentiment Analysis in eCommerce
Sentiment analysis, often called opinion mining, is a method used to determine the sentiment behind customer statements or reviews, classifying them as positive, negative, or neutral.
In simpler terms, sentiment analysis tools help businesses understand how customers feel about their products by analyzing what they say in reviews or comments. For example, if a customer writes, “I love how easy this product is to use,” sentiment analysis would classify that as positive. If another customer says, “The product works, but it’s overpriced,” the sentiment might be a mix of neutral and negative, depending on the context.
In the past, organizations relied on complex machine learning models, deep learning techniques, or lexicon-based methods to do this kind of analysis. However, these approaches required a lot of manual effort to train models, build specialized algorithms, or develop dictionaries of words associated with specific sentiments.
Historical methods of sentiment analysis are often time-consuming because they require extensive data labeling, tuning of algorithms, and constant updates to ensure accuracy. For example, deep learning requires large datasets and powerful computing resources to identify patterns, while lexicon-based methods require compiling a long list of words and their associated sentiments, which may not always capture the nuances of human language. LLMs, however, are pre-trained on vast amounts of text and are inherently capable of understanding context, making sentiment classification much easier and faster.
There are, of course, some limitations. LLMs, like humans, struggle with certain language subtleties such as sarcasm. For example, consider a review that says, “Oh great, another product that breaks after a week, just what I needed!” While a human might recognize this as sarcasm, an LLM could misinterpret it as positive due to phrases like “just what I needed.” Misinterpreting sarcasm could lead to incorrect sentiment classification, potentially skewing insights from customer reviews.
Using Large Language Models to Improve eCommerce
Now that LLMs can quickly and efficiently classify sentiment, how can we leverage this for eCommerce success?
1. Improving Customer Service
By sorting negative and neutral comments from your customer feedback, you can prioritize the most urgent issues and assign your best customer service agents to respond.
For example, if a flood of negative reviews about a delayed product shipment starts coming in, you can deploy your top support agents to address the issue before it escalates further. Alternatively, for neutral comments, like “The product is okay, but nothing special,” you could have agents reach out to convert indifferent customers into loyal ones by offering incentives or solutions to enhance their experience.
2. Support Insights
Sentiment analysis can also help you match the tone of customer reviews with their corresponding star ratings, offering deeper insights. For instance, you may find that a 5-star rating includes a minor complaint about packaging, which could give you an opportunity to make improvements. Conversely, a 3-star rating might have a glowing review except for one small drawback, providing an opening for targeted outreach.
Let’s say you have a product rated 4.5 stars, but after analyzing the comments, you notice several customers are expressing minor frustration with setup instructions. With this insight, you could refine your setup guides and improve user satisfaction. Similarly, if a 2-star review includes praise for product quality but criticizes shipping times, addressing the logistics issue could improve overall ratings without product changes.
3. Better Understanding Your Customer
LLMs can help identify common themes in customer feedback, making it easier to uncover pain points, preferences, or emerging trends in the market.
For example, if several customers mention a desire for eco-friendly packaging, this might signal a growing trend toward sustainability. Alternatively, if many reviews mention that customers find your product too complicated to use, this indicates a usability issue that could lead to product redesigns or enhanced support materials.
By identifying these patterns, you can take proactive steps to improve customer satisfaction and market perception.
4. Does It Solve the Problem?
After classifying customer feedback, the next step is determining whether your product is living up to its promise. If you sell a product that is meant to improve productivity, does customer feedback reflect that outcome?
For example, if you sell a productivity app and customers frequently mention how it’s making their lives easier, then the feedback aligns with your product’s value proposition. On the other hand, if negative reviews often complain about the app crashing or being difficult to navigate, that indicates a serious problem that requires immediate attention. Furthermore, if competitor analysis shows that other similar products are receiving more positive sentiment around ease of use, it could highlight areas where your product falls short.
5. Real-Time Customer Service
Using Large Language Models to monitor customer feedback in real time allows for proactive service. If the sentiment suddenly turns negative, you can assign customer service agents to resolve issues before they escalate.
Imagine running a flash sale, and during the event, you detect an uptick in negative comments about checkout issues. With real-time sentiment analysis, your support team could step in, offer guidance to struggling customers, and potentially prevent cart abandonment. Or, if several reviews mention a product defect, you could immediately halt further sales and address the issue before it spreads.
Conclusion
Large Language Models offer numerous ways to enhance eCommerce beyond product matching. By efficiently classifying customer sentiment, businesses can improve customer service, gain insights into product performance, and better understand their customers’ needs and preferences. Whether it’s identifying pain points, improving customer support, or being proactive with real-time customer feedback, LLMs help brands stay agile and responsive in the fast-moving world of eCommerce.
Want to learn more? eCommerce leaders are invited on October 16th, to join us for a 1-hour working session to explore practical applications of ChatGPT in product classification. Attendees will gain hands-on experience with classifying their own products and how to optimize prompts to test and refine their strategies.
Register below!
