In eCommerce, staying competitive on price means surviving to see another day. Most companies already use price monitoring tools and competitive pricing engines. At this point, those are just table stakes.
Something new is now changing the game: large language models (LLMs) like GPT-4, and the broader wave of AI tools that can actually understand context.
The real question now is: How do we combine raw pricing data with LLMs and conversational AI for better eCommerce outcomes? How do you get more traffic, customers, sales, and revenue by combining these tools?
In this article, we will tackle this exact topic. We will go through five ways to bring together repricing engines, LLMs, and AI chatbots. This way, your pricing strategy moves from reactive to predictive, and you take the first step towards outsmarting your competition.
Advanced Repricing with LLM-Driven Context
Repricing engines can react to competitor price changes, but they often lack context. Should you match a competitor who dropped prices 15%? Even if their delivery time is five days and yours is two?
Maybe highlighting your faster delivery times without changing your price is the better option.
These situations are where LLMs come in. Instead of just reacting to price deltas, you can feed pricing data into an LLM that also considers product attributes, delivery promises, brand strength, and other contexts. The point is, with a repricing tool and LLM combined you can get both a repricing suggestion (if there is one) and the reasoning behind it.
The solution is to feed real-time pricing data into an LLM pipeline that considers product attributes, customer promise, and market context before recommending price adjustments.
Here’s a workflow example:
- Price monitoring tool → Data pipeline → Product metadata → LLM-generated recommendations with justifications.
- Output: “Maintain price; differentiate with fast shipping badge instead of cutting price.”
AI Chatbots that Negotiate and Explain Prices
Most AI chatbots today are still stuck in support roles. They assist customers with tracking shipments, handling returns, or escalating tickets. But in pricing, they can play a far more strategic role: negotiating and explaining price logic in real time.
This is particularly valuable in wholesale or enterprise settings, where pricing isn’t static. Discounts vary by volume, contract terms, customer tier, inventory levels, and other factors. Traditionally, negotiations require a sales rep and a spreadsheet.
But with the right backend logic and LLM integration, a chatbot can do the job faster and more consistently.
Here’s a plausible scenario:
Customer: “What’s the best deal if I order 100 units?”
Chatbot: “Given your order history, current inventory, and our volume discount rules, I can offer 8% off. Would you like me to reserve that price for the next 24 hours?”
Do note, the LLM isn’t setting the price on its own. It’s surfacing logic based on the rules you set: inventory thresholds, deal history, and pre-defined discount tiers. It knows what can flex, and what’s non-negotiable. And crucially, it can explain its reasoning, which builds trust and helps you nurture profitable customer relationships.
LLM-Powered Pricing Dashboards with Business Narratives
On one hand, the traditional pricing dashboards are powerful. On the other, if you are not trained, interpreting them will require time and a strong sense of context.
With LLMs there’s now a way to bridge this gap. By integrating LLMs into your dashboard analysis workflow you can get answers to natural-language questions such as: “Why did margins drop in Category Y last week?”
The power of a data-fed LLM lies in the fact that you now won’t have to dig through filters and pivot tables to get an answer along the lines of: “Margin fell 9% due to auto-repricing in response to aggressive markdowns by Competitor A. Your floor price settings prevented a match, maintaining margin but reducing sales velocity.”
These language models likely won’t completely remove the need for dashboards. However, they will be able to augment the insights you get out of them. This is especially true for small and medium-sized eCommerce companies, who don’t have enough resources to hire teams of data analysts.
Combining Customer Sentiment with Pricing Strategy
A common mistake many pricing managers make is that they ignore qualitative signals from their customers. Here we primarily refer to reviews, social media posts & comments, and forum discussions.
You may already anticipate what we are going to say but: LLMs excel at analyzing and extracting insights from large amounts of qualitative data. By combining these insights with pricing models, you can uncover perception gaps that pure conversion data might miss.
For example, suppose a product has hundreds of glowing reviews and high customer satisfaction, but sales are underwhelming. A traditional pricing tool might suggest a discount to boost conversions. But an LLM could detect that customers actually love the product and are surprised it doesn’t cost more. That opens the door to a smarter set of pricing moves: a modest price increase, a premium bundle, or a “best value” badge to reinforce its positioning.
Inversely, you could be faced with a mixed customer sentiment. Typically this is a situation in which customers frequently mention quality concerns or poor unboxing experience. Lowering the price might align expectations better and prevent churn or returns in this case.
Automating AB Pricing Strategy Generation
One of the most underrated roles LLMs can play in pricing is that of a strategy co-pilot—helping you rapidly generate, refine, and test pricing ideas based on real-world constraints.
Instead of starting from a blank slate, you can ask the model for targeted recommendations.
Here’s a sample prompt: “Given 5,000 units in overstock and a 20% price gap versus a key competitor, propose three pricing tactics to move inventory without fully matching their price.”
As an answer you could potentially get this:
- Tactic A: Undercut by 5%, add an urgency tag (“Selling Fast”) to trigger demand without racing to the bottom.
- Tactic B: Bundle with a high-velocity SKU to increase perceived value and smooth out margin impact.
- Tactic C: Apply a geo-targeted discount in low-traffic regions to test localized price sensitivity.
Once generated, these tactics can be tested through your existing pricing or promotion engine in controlled AB setups.
Over time, the model can also be trained to prioritize strategies that align with your business goals. These can be, for example, margin preservation, inventory velocity, or new customer acquisition.
Conclusion
Combining price tools with LLMs gives eCommerce teams a sharper edge. You can stop reacting to competitor moves and start acting with context. Smart pricing isn’t just about matching numbers. It’s about knowing when and why to hold, adjust, or explain a price.
LLMs help make sense of complexity. They turn dashboards into answers, reviews into insights, and pricing rules into conversations. This makes decisions faster and more confident. It also frees up your team to focus on strategy, not spreadsheets.
Think of LLMs as hyper-capable consultants, and think of price monitoring and repricing tools as the data engines they rely on. On their own, each is definitely useful. However, when paired together, they become a real-time decision system. One gathers signals, the other interprets and acts. Solutions like Price2Spy combine price tools, LLMs and AI, giving your pricing strategy the speed, clarity, and precision it needs to win.
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