Shopify Ecosystem

Machine Learning Product Matching And The Future Of Retail

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Machine Learning has been introduced in various fields. In this instance, we’d explore the value of the technology in retail, more specifically in the domain of product matching.

Product matching ensures a company has a properly aligned assortment with a sufficient degree of pairs of products that match. It helps understand what you have to offer to customers and gain a competitive advantage. With the rising degree of competition, firms use more innovative and sophisticated tools. Avoiding tools like Artificial Intelligence and Machine Learning will become something businesses cannot afford to avoid.

Here, we would like to explore the horizons of Machine Learning product matching and explore its key details, definitions, and models. The outcome of the reading is to grant insights that can help retailers translate them into a competitive advantage. 

Various Types of Product Matching to Consider

There are different types of product matching available. Respectively, knowing the types helps one to understand when to use them. Along with product matching, there are various fields where Machine Learning matters. When it comes to key product matching types, there are the following to consider:

  • Manual matching. The type of product matching involves a person doing matching manually. Such an approach is accurate but takes an extremely long time to complete, especially if the company has a broad assortment of products. Manual matching might also require additional spending for hiring a professional who would do it.
  • Electronic or barcode matching. This kind of product matching is fully automated. It is based on the never-stopping search of product URLs based on elements like ASIN and GTIN. The process is fast but not that effective. There is also a low degree of accuracy because of the verification complexity involved. 
  • Machine Learning matching. It is the type of product matching that involves the usage of advanced algorithms. Usually, the power of machine learning is delivered through software many companies offer. A product matching tool can help to fully automate the process of product matching and boost its accuracy.

These types of product matching proved to offer different outcomes. There are certain pros and cons to each one of them. Nonetheless, Machine Learning product matching seems to outperform manual and barcode matching by a lot. 

Machine Learning Product Matching as a Game-Changer

Getting product matching to work for your benefits is not an easy task to perform. What is more, product matching is extremely important when companies find themselves in a position of high competition. In such a case, product matching works best in market-based pricing. It can be applied to any business, meaning any size and industry.

The important question is to understand why product matching brings benefits to a business. First and foremost, with the rising number of accurate product matches, a company can better grasp its assortment and what to do with it. Second, good product matching grants insights into pricing to make proper calculations while creating prices most appealing to consumers. Third, product matching offers an option for data-driven assortment optimization, which is all about deeper insights into the process and a more reliable process as the outcome. Fourth, product matching enhances consumer targeting that translates into greater customer loyalty. Finally, product matching can be used for performance evaluation to ensure that given products bring the most value. 

These aspects ensure that product matching is a game-changer in any given industry. The crucial point is to use the phenomenon properly and correctly integrated tools.  

Machine Learning Product Matching Models

When it comes to Machine Learning product matching, there are essentially two models to consider. 

  1. Item screening approach. As a complex process, product matching depends on incorporating images, prices, and titles. Everything starts with an item screening process as the initial approach. It includes finding the most similar items in one’s assortment. The application of the model starts from several key phases. The first one is to convert text to a lower case while also removing all the extra spaces and characters.
  2. Item ranking model. When the item screening process is complete, it is time to proceed to the second approach. Item ranking depends on choosing appropriate candidates during the screening stage and further using various algorithms to determine whether candidates are a match or not. The item ranking model requires downsampling and local validation.

There are several particular moments when using specific tools within both models. For the item screening approach, one should engage in iteration with a deployed Term Frequency – Inverse Document Frequency. Besides, utilization of the item screening sometimes requires scalability. It is often advised to use Locality Sensitive Hashing (LSH). 

In terms of the item ranking, companies often appeal to a multilingual neural network, for instance, XLM-RoBERTa. It allows receiving stable results in comparison to other transformer-based approaches. What is more, the item ranking model requires a local validation process. Often, companies use the F1 score and ROC-PR. Within both models considered, one should follow the method of calculation, implementation, and validation carefully and precisely. 

ML Product Matching  

As mentioned above, product matching can be extremely important for many businesses, including retail. However, one should not forget that different retail options exist, and product matching can work for them differently. At this point, one can illustrate the difference between the application of product matching in DIY retail and electronics retail.

DIY retail often uses manual matching to engage in a more accurate matching of assortment. In turn, electronic matching usually requires automation and barcode matching. In such a case, both industries face the challenge of the lack of structured information. Yet, with electronic retail using codes and identifiers, the electronic retail has much broader options for access to structured data, which DIY does not have. AT this point, depending on the industry, the usage of ML product matching can be different. 

Concluding Remarks 

Product matching can be beneficial in many instances, and retail is no exception. The phenomenon grants insights that can be translated into a competitive advantage. However, product matching should be applied in a proper environment and with appropriate processes to work correctly. With item screening and item ranking in mind, product matching delivered through product matching software can do great things to retail. 

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