Transformation of Retail Industry with ML Product Matching Method
Implementation of Machine Learning (ML) technologies grants many benefits in retail. However, getting the most out of the approach requires understanding several key principles. Here, one should explore different ML product matching processes and indicate their value to any given business.
The introduction of ML and Artificial Intelligence (AI) has opened new horizons for many businesses and industries. It has revolutionized how firms deal with data and the insights it brings. Yet, product matching via ML proved to be a major component in seeking competitive advantage. ML integration into pricing focuses on offering good revenue, and profits retailers intend to get.
Using the power of ML algorithms, companies got an opportunity to automate product matching capabilities. Further, one should discuss how automated product matching has become a crucial part of any competitor-based pricing strategy.
Various Kinds of ML Product Matching
When it comes to one of the basic principles of competitive markets, it is all about consumers assessing the product and its value based on the prices the company’s rivals offer for the same products and services. In such a case, competition-based pricing ensures proper positioning, price perception, and competitiveness.
With this approach, retailers can engage in smart pricing to stand out from competitors. With the rise of modern technologies, smart pricing has become even more functional. For instance, companies can employ product matching software to analyze and process massive amounts of data that can later be translated into comprehensive pricing strategies.
Keeping that in mind, along with sophisticated pricing, firms can use various kinds of product matching, namely the following:
- Barcode matching. It is a fully automated process of searching for product URLs through ASIN, GTIN, and UPC.
- Manual matching. It is a manual process of identifying each given match.
- ML product matching. The fastest and most effective approach is based on algorithms capable of processing massive volumes of data at great speeds.
Companies can use either one or all the strategies mentioned above. It all depends on on the business model used and the industry in which the methods are applied.
The Key Role of ML Product Matching
ML product matching is a comprehensive process. Besides, with the rising competition and market shifts. The experts indicate that the playing field is poised to become more competitive. Businesses that don’t deploy AI and data to help them innovate in everything they do will be at a disadvantage. Essentially, ML product matching can become a new page in a retailer’s history and strategic pricing.
Yet, there are a couple of aspects to understand about ML product matching. First, the bigger the share of properly matched products, the better the data. Second, the lower the share of correctly matched products, the higher the likelihood of issues in the price calculation process, which all result in adverse consumer experiences.
The insights show that any type of product matching plays an important role in gaining a competitive advantage. Respectively, a properly used approach, boosted with ML, can be a game-changer in retail.
Central Elements of ML Product Matching
There are two core elements or models within the realm of ML product matching. They are complex processes requiring the incorporation of prices, titles, images, and product characteristics. Following are the primary models used in ML product matching.
The Model of Item Screening
It is considered to be a lightweight model. It often serves to find the most similar items in a client’s and competitor’s stores. The item screening model is usually employed with preprocessing for product titles. The method includes converting text to lower case and removing extra spaces and characters with an exception for numbers and hyphens.
The first iteration of the item screening model includes the utilization of TF-IDF, which stands for Term Frequency – Inverse Document Frequency. Importantly, if companies using the model require a more scalable tool, it is always better to use Local Sensitive Hashing (LSH). Notably, before utilizing the model, it can be helpful to have a trial run. Select candidates for each particular product and use them in the training dataset.
The Model of Item Ranking
After running the selected candidates presumed by the item screening model, it is time to start the ranking process. The item ranking model helps one define the particular pair of products will be presented as a match or not. There are available networks, for instance, XLM-RoBERTa, which allows a company to receive more stable results compared to models like BPE.
In the context of the model application, always use the option of downsampling along with setting custom class_weight for the sake of handling an unbalanced dataset. When it comes to local validation of the item ranking model, one can employ the F1 score and ROC-PR. With the presented elements considered, it is possible to use both models of ML product matching to find whether a selected set of items is a match or not.
Practical Insights into ML Product Matching
Considering all the insights in ML product matching, it is apparent that the process is challenging to adopt and requires certain expertise. However, nothing speaks better about the power of the phenomenon than particular examples obtained from the retail industry. The evidence dictates that companies working in retail managed to get a higher precision of product matching by using item screening and item ranking.
Yet, in almost all instances when the models were adopted, the key challenge correlated to the lack of structured data on goods in titles. When retailers used codes and identify, it offered a much more thorough description. Respectively, the practical applications of item screening and item ranking work best with codes rather than titles used in DIY products. Utilizing tools like pricing gun labels can contribute to a more organized and efficient system, further streamlining and enhancing product identification.
Wrapping Up
It can be challenging for retailers to use ML product matching. However, companies can tackle such issues when using relevant data and sophisticated algorithms. Access to high-quality datasets can be translated into healthy competitive pricing, which will maximize value for business and be appealing to consumers.
