Natural Language Processing (NLP)
On this page
What percentage of your catalog is actually compared to the competition?
Schedule a meetingLearn about our MPS pricing solutionNatural language processing (NLP) is a branch of artificial intelligence that enables machines to understand, analyze, and generate text. In pricing, it is used to match products with different descriptions across catalogs, extract product features, analyze customer reviews, and detect prices in unstructured sources.
The Essentials in 6 Questions
AI that understands text: labels, descriptions, reviews.
Pricing, product data, and e-commerce teams.
Every time a catalog match is made and a record is enriched.
Competitors' catalogs, marketplaces, customer reviews.
Streamline the matching process and organize product data.
Vectorization, similarity, classification, sentiment analysis.
Why NLP Is Useful in Pricing
Because it automatically matches products that are described differently from one website to another.
- Standardizing product matching: “iPhone 15 Pro 256 GB Blue” and “Apple iPhone 15 Pro - 256 GB - Blue Titanium” are recognized as the same product.
- Enriching data: Extract thebrand, size, or color from raw text.
- Analyzing sentiment: understanding how customers perceive a price (expensive, fair, cheap).
Real-world example: from 38% to 94% matching
A cosmetics retailer has increased the percentage of closely matched SKUs from 38% to 94% using an NLP model and now monitors 11,280 products instead of 4,560.
94% automatic matching, compared to 38% manual matching
Cosmetics retailer · Matching 12,000 SKUs using NLP
automatic matching rate achieved using an NLP model trained on 50,000 pairs of labels, with an accuracy of over 99%.
Coverage of the former manual matching process
products now being monitored (up from 4,560), without additional hiring
The model, trained on 50,000 pairs of validated labels, matches 12,000 items from the catalogs of six competitors with an accuracy of over 99 percent, without the need for additional data.
How does NLP work when applied to pricing?
Four techniques, combined in models pre-trained on retail data.
| Technique | Usage in pricing |
|---|---|
| Vectorization (embeddings) | Convert the descriptions into comparable representations. |
| Similarity Calculation | Match equivalent products across catalogs. |
| Classification | Automatically categorize labels. |
| Sentiment Analysis | Read about price perceptions in customer reviews. |
Our product matching solution is based on these techniques, combined with EAN codes and attributes; our MPS pricing solution then leverages the structured data. See also agent-based pricing.
The 3 Common Mistakes with NLP
Too little labeled data, a general-purpose model, or unmeasured accuracy.
- Underestimating the need for labeled data: several thousand expert-validated pairs are required.
- Applying a general-purpose model: a specialized retail model performs significantly better.
- Don't focus on accuracy: 80% accuracy means 20% of comparisons are incorrect.
Frequently Asked Questions
Short answers to the most frequently asked questions about NLP in pricing.
What is natural language processing (NLP)?
Natural Language Processing (NLP) is a branch of artificial intelligence that enables machines to understand, analyze, and generate text. In pricing, it is used to match products with different descriptions, analyze customer reviews, extract product features, and detect prices in unstructured sources.
How accurate is a good NLP matching model?
A model that has been thoroughly trained on retail data achieves a 92% to 97% match rate with an accuracy of over 99% on standard catalogs.
Does NLP work in all industries?
Yes, with specific requirements: sizes and colors for fashion, capacities for food products, and technical specifications for home improvement. A specialized model for each sector is generally required.
Should we combine NLP and image recognition?
Yes, for visual categories (fashion, home decor, household appliances), where the image complements the description.
Key Takeaways
- NLP analyzes the text of labels, descriptions, and reviews.
- In pricing, it automates the process of matching catalogs.
- Its value depends on the labeled data and a measured accuracy.
Would you like to use reviews and text-based data to inform your pricing decisions?
Booper uses NLP to enrich your product data and refine your pricing recommendations.
Let's talk about your product match →Learn about our MPS pricing solutionYou might also be interested in these articles
Artificial intelligence must never drive pricing strategy. Its deployment requires the establishment of rigorous safeguards, such as price corridors and human validation, to protect financial margins. This alliance between computing power and expert oversight transforms raw data into sustainable profitability without the risk of algorithmic drift.
These reflexes can't be improvised: that's what the BOOPER Pricing Training is for—to give your teams the right guidelines before implementing AI in your pricing.
Key takeaway: AI-powered pricing overcomes Excel’s limitations by incorporating complex variables such as inventory and competition to model price elasticity accurately.
This robust management approach safeguards margins and volumes while remaining transparent to managers. Key point: An elasticity exceeding 3.5 often indicates a data anomaly rather than actual customer behavior.
Not all AI systems are created equal when it comes to pricing. Statistical rules, predictive machine learning, and generative AI: these three technologies are often lumped together, even though they address different needs and inform different decisions.