August 18, 2022

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Advertisement The innovation and new technologies granted new opportunities for businesses around the world. As...


The innovation and new technologies granted new opportunities for businesses around the world. As a downside, firms received new possibilities for gaining a competitive advantage. The more technological the market and industry become, the fiercer the competition. As one of the CTOs at Accenture indicates, “The playing field is poised to become a lot more competitive, and businesses that don’t deploy AI and data to help them innovate in everything they do will be at a disadvantage.”

Following the aspects mentioned above, the power of innovation can be coupled with conventional methods of product matching. As a result, retailers receive ML-based product matching as a new tool for creating value for a business. Here, we’d explore various types of ML-based product matching and find out how different models operate.  

Differentiation with ML-based Product Matching 

It is no secret that the value of a product is determined based on the price in competitive markets. The price is often compared to the one presented by a company’s rivals, considering the provision of a product or service of the same quality. The approach is often known as competition-based pricing. In addition to the phenomenon, its effectiveness is usually determined by proper product matching. It ensures the adequate application of the pricing strategy results across one’s stock.

While retailers broadly use product matching, there are still several types to differentiate from:

  1. Manual product matching. The type of product matching requires a person manually search and identify each product pair.
  2. Barcode product matching. The kind of product matching is dependent on automated matching via product URLs.
  3. ML-based product matching. The product matching method uses the power of advanced algorithms to analyze datasets and match products based on the analysis.
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These are core methods of product matching. Depending on your business needs and capabilities, one can employ either one particular approach or all at once. After knowing the main strategies in product matching, it is important to focus on the value it can bring to a business.  

The Core Value within ML-based Product Matching

For numerous companies, broadening their portfolios and assortment is crucial for staying competitive and ensuring a growing outreach to different groups of customers. Respectively, having more products in a store requires a more accurate product matching. Considering that each product has various elements, such as size, color, and shape, proper product matching becomes increasingly challenging. 


The greater the number of correctly marched products ensures better data quality and makes it easier to apply pricing to all the selected products. In contrast, incorrect product matching makes it extremely hard to know what assortment a company has, which translates into more obstacles in pricing. 

Luckily for retailers, product matching became easier and more accurate with the integration of advanced algorithms. Approaches like product matching software help companies match their assortment in minutes. It benefits the business and assists in avoiding costly manual labor. 

Central Models in ML-Based Product Matching

ML-based product matching is a complicated process stemming from multiple inputs and data points. Proper application of the approach requires relying on different product characteristics. Going further, the adaptation of ML-product matching can be delivered in two stages through two given models:

  1. Item screening in ML-based Product Matching. It is considered a lightweight method that explores the assortment among competitors to find the similarities with a client’s store. The item screening model is used along with text preprocessing for various products, including converting text into a lower case and removing extra characters. The given method is based on 4-grams. It uses 4-character sequences of letters and characters in each given title. Later, the results are compared with titles in competitors’ online stores. As a next step, the selected candidates for each given product can be used in training datasets for the reranking process delivered by the item ranking model.
  2. Items ranking in ML-based Product Matching. When the appropriate candidates used in training datasets are chosen, the item ranking model comes into play. Respectively, the reranking presented by the second model determines whether given pairs of products come forward as matches or not. Returning to the utilization of advanced technologies, there are instances when multilingual neural networks are employed to get more stable and accurate results. Such an approach is way better than transformer-based methods. Later, one should use tools like the F1 score to engage in a local validation. 
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These two stages and models represent the functionality of ML-based product matching. Through particular phases of item screening and item ranking, companies can employ tools for accurate and precise product matching. As an outcome, it ensures timely examination of a given assortment to adopt good pricing strategies. 

Application of ML-based Product Matching in a Practical Setting

When thinking about hands-on experience of using ML-based product matching, it is crucial to compare DIY retail and retail electronics segments. The practical insights show that higher precisions of ML-based product matching are delivered in electronics rather than DIY. 

Why? Because in the latter segment, there is more unstructured information in titles. The electronics retail industry often used barcode matching as a prerequisite for ML-based product matching. As a result, it leads to more thorough descriptions, granting more accurate data to be processed by ML algorithms. The usage of identifiers and codes proves important for ML product matching to function correctly. 

The insights mentioned above illustrate that despite all the benefits of ML-based product matching, particular challenges are considered. Respectively, the algorithms work best in a properly established environment. 

Concluding Remarks

In a highly competitive market, product matching proved to be vital. Yet, the rising competitiveness leads to the necessity to adopt new technologies and complex algorithms. ML-based product matching proved to be an important tool for getting the right vision of a company’s assortment and what it can offer in the light of competitors. With item screening and item ranking, it is possible to secure pair matching. However, don’t forget about the obstacles in the approach, especially considering the differences between DIY and electronics retail as presented above. 

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