Conversion rate predictions are all about buying intentions. Predicting buying intentions for recommendations in an online environment is complex. The efforts required for the customer to visit an online shop are negligible, and therefore, customers are more likely to visit an online store without any buying intentions. Often, people are just browsing through a webshop for the sole purpose of entertainment or exploration.
In contrast, a sales representative within a brick-and-mortar store has an intuitive feeling about which customers are likely going to buy based on the behavior of the visitors in-store. Therefore, the sales representative is able to allocate his resources as effectively as possible by recommending the most relevant products leading to higher conversion rates. In a webshop we aim to mimic this by inferring buying intentions from implicit feedback based on the website clicks and transactions.
Relatively low buying intentions within a large e-commerce webshop results in millions of website clicks/views while conversions are relatively low in comparison. Because of the low level of conversions, it can be hard to find patterns in the data on when, why, and on which products website visitors will convert. Accordingly, determining which recommendations to show becomes a complex task. However not impossible.