Four Ways AI is already making ecommerce smarter
There’s no doubt that artificial intelligence (AI) will fundamentally change the world over the next few decades. What many do not realize however, is that in some fields, it has already become part of the status quo. One such example is e-commerce (EC).
Below are a few examples of how we are leveraging various applications of AI, specifically machine learning, to advance our EC business.
Predicting sales –Supervised machine learning is a form of AI in which “the machine,” or algorithm, is given sample data from the past that helps train it to process the data of the future. With 200 million products being traded, supervised machine learning algorithms allow us to use historical sales data to forecast the sales volume of products to a high degree of accuracy – and make surprising discoveries in a far more efficient way than a team of humans ever could.
Marketing to the right groups – We make use of so-called “unsupervised” learning algorithms when segmenting customer groups for marketing campaigns. Traditionally, marketers have defined market segments in ways that appeared to make sense to them – by age or gender, for example. But AI is demonstrating that those are not always the most effective approaches. An unsupervised learning algorithm, working from raw real-time data only, might identify alternative means of segmentation, such as online behavior or preferences, that can serve as a more accurate predictor of interests or tastes.
Classifying products – This can make categorizing a challenge. To solve the problem, we utilize a “semi-supervised” learning algorithm, which repeatedly resamples data until the algorithm learns how to process it in the most efficient way.
Analyzing ratings and reviews – Understanding user ratings and reviews is important, but it is also time-consuming. Applying “structural” machine learning algorithms, a method commonly used in the study of the structure and formation of words (morphology), we can efficiently collect and analyze product review text, both positive or negative. In addition, structural machine learning can help us mine valuable information data from page explanations and reviews.