Creating the best first impression: Designing online product photos to increase sales Abstract   Effectively displaying goods in search results is valuable for B2C merchants to earn clicks from consumers and even increase sales. Taking down jackets and trousers sold on Tmallxe2x80x94Chinau0027s largest B2C ecommerce platformxe2x80x94as the example, this paper collects data about three factors that influence consumersu0027 first impression on their search results: the price of a product, the quantity of historical reviews, and a photo of the product. Among these factors, previous research shows that a product photo contains four attributes: brand logo, promotional information, street scenes, and model display. Focusing on these attributes, we apply a decision tree to explore customer purchasing patterns, which allows us to further investigate the influence of product photo attributes on sales volume by using a hierarchical regression model. Our research finds that among the products from the list of their search results, customers prefer those with many good historical reviews and low prices. In addition, gender that differentiates menu0027s from womenu0027s clothing has a moderating effect on the relationship between photo attributes and product sales. The purchase decision of consumers shopping for menu0027s clothing is susceptible to the influence of the product photo. Furthermore, different from the traditional view which shows that brands can reduce perceived risks and increase sales, this study finds that menu0027s clothing sales are negatively affected by brand logo attribute in product photos. Finally, using models cannot significantly increase product sales among consumers shopping for either menu0027s or womenu0027s clothing.

Observational Learning in the Product Configuration Process: The Effect of Information Presentation Format With the booming of ecommerce, online product configurators have been applied in various industries to elicit customer needs and transform them into product variants. To facilitate customersu0027 choice navigation during configuration process, some configurators provide extra information about product, such as best seller options. Previous study shows that these kinds of information significantly affect consumer decision making process, a phenomenon often referred to as observational learning. This paper attempt to study the format of the information provided in configurators affects observational learning effect. Specifically, two kinds of information are investigated, namely, the best seller choice and customersu0027 choice distribution among all the attribute alternatives. We empirically investigate how different kinds of factors affect customers choices when the best seller and choices distribution information are provided. Our experiment shows that motivation to customize the product is significant in customersu0027 choices. While purchasing intention is significant only when best seller information is provided. We further notice that there is no much difference between peopleu0027s choices when best seller and the choice distribution information are provided.