author	 = {Amina Voloder},
	title	 = {{Understanding Customer Behaviour to Optimize Product Sorting for E-Commerce Websites}},
	advisors	 = {Julian Kunkel and Antonia Wrobel},
	year	 = {2018},
	month	 = {08},
	school	 = {Universität Hamburg},
	howpublished	 = {{Online \url{{{:research:theses:amina_voloder_understanding_customer_behaviour_to_optimize_product_sorting_for_e_commerce_websites.pdf|Thesis}}}}},
	type	 = {Master's Thesis},
	abstract	 = {This thesis investigates product-specific and user-specific characteristics that influence the sales in order to develop a novel sorting algorithm for application in the field of e-commerce, through the analysis of customer preferences and the nature of a given store’s products, to improve the personalisation of online shopping systems. The algorithm optimises the order of displayed products by their purchase probability, determining which products users are most likely to purchase. This is determined by investigating the correlation between product sales and: product seasons, the time of day, and the devices users own. It was found that products could be classified as being sold well in particular months or regardless of the month. Similarly, products could be sold well at particular times of the day or regardless of the time. It was also determined that users of Apple devices should have more expensive products promoted to them, as they typically purchase greater quantities of expensive products. The algorithm is evaluated by means of visual and quantitative comparison against the standard sorting algorithm used within an e-commerce system by novomind AG. Test results indicate a discernible variation in the sorting order of products, as well as an increase in the variety of the eight highest sorted products. The full contribution of the algorithm to the sorting optimisation is verifiable through real-world A/B testing.},