Explore the phenomenon of product matching from a different perspective. Here, you can read about integrating new technologies into the approach to make it even more advantageous.
Machine Learning (ML) redefined how people collect, analyze, and synthesize data. With the instrument at your fingertips, the process became as effective and seamless as never before. Product matching proves to be an important aspect of any business working with assortment. Combining ML and product matching brings ML product matching, an innovative approach to using data to boost sales volumes.
ML Product Matching and its Key Types
The revolution of ML and Artificial Intelligence (AI) has created new opportunities for many businesses. With more and more companies taking advantage of the technology, the market has seen a massive increase in competition. One of the leading experts from Accenture suggested that many industries have become a lot more competitive up to the point that not using ML and AI represents itself as a critical disadvantage.
Several key types of product matching emerged to answer the competition and make the most of the technology. Depending on the market situation and level of competition, companies can appeal to product matching in the following forms:
- Manual matching. The process often involves a person doing product matching manually. One should check all the pairs of items and determine whether they are a match. The process is accurate yet takes too much time.
- Barcode matching. The method includes using UPCs, GTINs, and ASINs. It is an automated process of checking pair of products electronically. However, because of the high degree of verification complexity, the quality of matches is not that good.
- Algorithmic or ML matching. The fastest and most accurate method of product matching. It employs the power of advanced software and algorithm to check through the massive data volumes to determine matches. The key issue is to have access to data.
These are primary types of product matching used across businesses. However, even if one has chosen both manual and barcode matching, it does not ensure the high accuracy of the given results. Besides, there is a human factor to consider. Many firms use product matching software to get the best outcomes possible.
The Role of ML Product Matching
Equipped with instruments like product matching software, companies can achieve massive advantages in their pricing approaches. However, it is always important to remember that using an advanced tool requires knowledge and understanding. Several aspects need to be considered to make product matching bring the most benefit in such a case.
First and foremost, the strategy plays a key role in business because the growing share of correctly matched products offers a higher quality of data and better decision-making. When it comes to product matching in retail, accurately matched data helps retailers keep up the pace of the business and achieve good results in various processes. At this point, one should mention the following:
- Data-driven assortment optimization.
- Product performance.
- Effective decision-making.
- Better consumer targeting.
- Price optimization.
These factors constitute the role of product matching in retail. As one can witness, various objectives can be achieved, and if they are reached effectively, any company can rip many benefits.
ML Product Matching in Small Details
ML product matching is a complicated process that requires some major inputs to understand. Regardless of the numerous aspects, there are only two key models encapsulating the essence of the approach.
- Item screening model. The initial approach relies on searching for product matches within one’s store compared to the same products in competitors. The primary method of delivering the model stems from following several steps. The first one is about converting text to lower case. The second phase is about removing extra characters and spaces. The model is often used with Term Frequency – Inverse Document Frequency (TF-IDF) iteration. However, when companies need more scalable instruments, they appeal to Local Sensitive Hashing (LSH). The final stage revolves around selecting candidating for each given product match employed in a training dataset for further reranking.
- Item ranking model. When the final stage of the screening model is complete, and training datasets are presented, it is time for the first stage of the ranking model to engage. Item ranking helps determine which particular pairs of products will match or not. When delivering the method, it is important to use downsampling to manage unbalanced datasets if favor of pairs constituting a non-match. The further phase of the item ranking model is presented through the F1 score, which is a harmonic average between precision and recall. To better understand the results from the phase, one should use a precision-recall curve that encapsulates the outcomes of reranking. When turning to the curve, the “precision” element indicates an accuracy of matches, while “recall” constitutes the number of matches possible to find.
Item screening and item ranking is the core of ML product matching. Two models come in a particular succession and create conditions and preconditions for each stage. In such a case, it is crucial to implement two models to pan out in real life.
How does ML Product Matching Show Itself in Real Life?
The functionality of ML product matching can be better understood when compared between implementation within different markets and industries. ML Matching for retailers and brands constitutes a particular approach and shows certain promises. It is important to explore ML product matching as presented through the electronics retail segment and DIY retail when narrowing down the scope.
The general insight from ML product matching in electronics retail suggests a higher degree of accuracy in product matching. It happens because of the availability of more structured information in electronics rather than DIY. The key outcome is that practical application shows the great dependence of ML product matching from structured data.
Conclusion
ML product matching can be a game-changer when adopted properly. Fueled by advanced software, the approach stands on the models of item screening and item ranking, showing whether given product pairs are a match or not. When having a higher product matching accuracy, the companies receive a greater understanding of their assortment and what to do with it.
