Some time ago, the term machine learning (ML) referred to applications that were still in the works and had a vague aftertaste of science fiction. Over the past decade, however, there has been steady progress in developing practical applications based on machine learning for the benefit of businesses. Large and small companies can use ML to enhance the value of the shopping experience for users of their e-commerce sites.
The possibilities that Artificial Intelligence (AI) offers for increasing ecommerce profits are truly exciting. Over time, the cost of integration and development has become increasingly affordable, making ML-based applications ideal for predictive analytics, intelligent monitoring, and optimization of ecommerce resources.
Initially, improving ecommerce infrastructure required predicting all aspects of the problem being analyzed. Instead, ML is about automatically uncovering the paths (patterns) that connect the data and using them for subsequent analysis. The process is based on algorithms that apply different methods to study data input and its evolution.
Machine learning-powered applications can optimize business performance and increase the conversion rate of e-commerce users. From a site visitor's perspective, they can improve the site's ability to deliver results that are relevant to their searches (especially when they do not know the name of the product, but only have some of its features in mind). This can help speed up the purchase process and personalize the browsing experience.
ML self-learning systems can analyze information from interactions on e-commerce sites to draw accurate conclusions about user preferences.
For example, we can use ML to analyze the optimal price to charge based on:
In this way, we can analyze user behavior more comprehensively in order to present our offer more accurately. The self-learning mechanism can facilitate the placement of e-commerce products in more appropriate categories, taking into account the expectations of visitors (failure to properly classify products can make them difficult to track).
Machine learning can help manage the infrastructure behind e-commerce more efficiently. For example, predictive scaling can estimate the expected load on the website over the next few days, reducing wasted resources.
One must also consider the security needs that AI itself compromises, as in the case of virtual images that are difficult to distinguish from real ones with the naked eye, for which machine learning is precisely what is needed to detect them.
Machine learning has many applications in ecommerce, from inventory management to customer experience. Using natural language analysis, software can understand searches performed by users. It can extrapolate elements that may influence common searches (such as product title and description) based on previously entered searches. Behavioral analysis allows returning users to be offered items that are compatible with previously manifested browsing habits (see Amazon and eBay). This can increase the accuracy of segmentation of such users, identify which products may be valued based on shopping habits, and increase the effectiveness of retargeting campaigns.
More machine learning applications are likely to be added in the coming years as these technologies become more feasible and cost-effective.
