Product Recommendations and Cross-Selling. Recommendation Forms, Product Categories and Product Familiarity (PDF)

Seminar paper from the year 2018 in the subject Business economics - Offline Marketing and Online Marketing, grade: 1,3, Nanjing University, language: English, abstract: In this study, we aim to investigate how two different types of online recommender systems affect the cross-selling of a retailer on a website using the online recommender systems. Furthermore, this will give us the chance to study the direct effect of targeting on cross-selling. Different from the previous studies, this study will mainly focus on two aspects.

On the one hand, the online recommender systems will be divided into two types, the targeting recommender systems, and popular recommender systems. The first one is the personalized form through which we can give the targeted consumers the specific products recommenders based on the consumers' purchase history. This type has been fully discussed in previous studies. The second type of a recommender system is the public form giving the recommendations based on the hot products, which is a common phenomenon in e-commerce platforms. In other words, recommended products or services come from common preferences of all consumers. Research on the relationship between cross-selling and this type of recommender system is still relatively lacking.

On the other hand, we will discuss the different moderating effects between two different types of the recommended products - search-type and experience-type products - as well as the influence caused by product familiarity on the relationship between the online recommender systems and cross-selling. These two types of products have been fully discussed in the area about the helpfulness of online reviews. In addition, familiar and unfamiliar products differ in terms of the knowledge regarding the products that a consumer has stored in memory. This will affect how consumers search and process the online recommender systems and co-purchase information. Therefore, we will consider these in the process of data collecting.

Through examining those previously described causal effects, we can make the following two contributions. Firstly, we can make further suggestions about how the choice for an online recommender system can influence cross-selling and thereby further contribute to the discussion about recommender systems in the e-commerce ecosystem. Secondly, we can classify the cross-influence from the product types and product familiarity in the above-stated relationship between online recommender systems and cross-selling.