Product Matching and Linking
Product Matching and Linking
Product Matching and Chaining: improve reliability Your product comparisons using AI
BOOPER MPS automates product chaining between your assortments and those of your competitors using AI. You have a reliable, governed repository to confidently drive your pricing strategies, competitive analyses, and business decisions.
Building a reliable view of the market and competition
In the retail industry, comparing prices and analyzing the market requires accurately identifying equivalent products across different retailers. The Product Matching and Chaining module automates this critical step using advanced recognition algorithms.
Automatic Identification of Equivalences
Compare your products to those of your competitors, even when the product names are different.
A reliable and well-organized product catalog
A common foundation for all your competitive analyses.
Analysis of Competitive Price Differences
Compare only what is truly comparable.
Real-time monitoring of market trends
Detect changes in product assortment or placement as soon as they occur.
Matching based on NLP, not on rigid rules
The text matching process is not based on a simple comparison of text, but on a genuine understanding of the wording.
NLP Recognition
The matching process relies on natural language processing to match different product descriptions across brands.
Product Cloning Management
Automatically link variants of the same product to a common SKU.
Vertical and Horizontal Tie Bars
Organize the relationships between private-label brands, national brands, and comparable products according to your own rules.
A starting point for all your competitive analysis
Reliable matching that then ensures the security of all your other pricing modules.
Customer testimonials
Discover how our customers leverage BOOPER's artificial intelligence to structure their pricing decisions, secure their margins, and accelerate their commercial performance.
We have made our entire pricing decision-making process more reliable thanks to BOOPER.
The teams now have a clear and shared view of price performance by category and by store, with data-driven recommendations.
The platform allows us to anticipate the impact of our choices on the margin and to justify our decisions to management with concrete and measurable indicators.
The predictive scenarios offered by BOOPER have transformed the way we prepare promotional campaigns.
We can compare several pricing scenarios before launch, measure their effects on volumes and profitability, and secure our business decisions.
This has allowed us to become more responsive while improving the consistency between supply strategy, price image and economic performance.
BOOPER has enabled us to industrialize our pricing approach without losing strategic control.
The teams have common tools to analyze the competition, simulate decisions and align field actions with business objectives.
We have structured a cross-functional governance that improves coordination between sales, marketing and finance while generating tangible results on the margin.
Product matching, or matching in certain situations, consists of automatically identifying equivalent products between different brands or ranges in order to compare their prices, performance, and competitive positioning on a consistent basis.
AI simultaneously analyzes labels, attributes, formats, brands, and price behaviors to detect similarities invisible to human analysis. It continuously learns from user validations to improve its accuracy. When available, AI also takes image recognition into account.
When there is no direct association by EAN, cloning takes over and links the retailer's products with those of its competitors; this applies to both private labels and national brands. The other type of complementary relationship is chaining: the retailer's products are grouped together to establish links in terms of hierarchy, coefficients, and historical models.
BOOPER identifies substitutable products based on their functional attributes and price positioning in order to structure MN/MDD/PPx chains and analyze the effects of cannibalization and upmarket positioning.
No, although recommendations are becoming increasingly intelligent and automatic, the association requires human validation in order to train the algorithms.
MPS uses product databases, descriptions, technical attributes, price histories, and competitive data. The richer the data, the more accurate the association proposal.
The gains come from reduced manual time, reliable price comparisons, improved business decisions, and overall competitive performance. The first benefits are usually seen in less than three months.
