Essays about: "customer e commerce"

Showing result 1 - 5 of 291 essays containing the words customer e commerce.

  1. 1. Orchestrating the service encounter in a digital era: How the presence of a greeting online service agent affects customers' experience of visiting a home electronics e-store

    University essay from Handelshögskolan i Stockholm/Institutionen för marknadsföring och strategi

    Author : Oskar Eskilsson; Elizabeth Lopez Alushkina; [2023]
    Keywords : Service encounter; Social presence; Mere presence; E-commerce;

    Abstract : The service encounter is to an increasing extent being transported to an online context. Companies are more and more often trying to create an initial point of contact when a customer enters a website. READ MORE

  3. 2. "Cracking the GenZ Code: Unraveling the Needs, Pain Points, and Desires for an Improved Omnichannel Fashion Experience" : A qualitative study by understanding the consumer decision-making process

    University essay from Linnéuniversitetet/Institutionen för management (MAN)

    Author : Ebba Uhlin; Moa Lundberg; [2023]
    Keywords : Omnichannel; E-commerce; Physical Stores; Pain Points; Touchpoints; Customer Journey; Consumer Decision-Making Process; Retail Technologies; Generation Z;

    Abstract : In recent years there has been a remarkable distribution of the omnichannel environment, which has emerged as a significant factor in the fashion industry. Today's society is characterized by customers who possess extensive knowledge and easy access to information online, with the added convenience of constantly available e-commerce. READ MORE

  4. 3. Customer churn prediction in a slow fashion e-commerce context : An analysis of the effect of static data in customer churn prediction

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Luca Colasanti; [2023]
    Keywords : Survival Analysis; Time To Event prediction; Churn retention; Machine Learning; Deep Learning; Customer Clustering; E-commerce; Analisi di sopravvivenza; Previsione del tempo a evento; Ritenzione dall’abbandono dei clienti; Apprendimento automatico; Apprendimento profondo; Segmentazione della clientela; Commercio elettronico; Överlevnadsanalys; Tid till händelseförutsägelse; Churn Prediction; Maskininlärning; Djuplärning; Kundkluster; E-handel;

    Abstract : Survival analysis is a subfield of statistics where the goal is to analyse and model the data where the outcome is the time until the occurrence of an event of interest. Because of the intrinsic temporal nature of the analysis, the employment of more recently developed sequential models (Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)) has been paired with the use of dynamic temporal features, in contrast with the past reliance on static ones. READ MORE

  5. 4. Exploring Environmentally Sustainable Last-Mile Deliveries in Sweden

    University essay from Lunds universitet/Teknisk logistik

    Author : Emelie Ehrensvärd; Clara Wilhelmsson; [2023]
    Keywords : Last-mile delivery; E-retailer; Sustainable last-mile delivery; Environmentally sustainable last-mile delivery; Technology and Engineering;

    Abstract : Last-mile delivery is the part of the e-commerce supply chain that is the least efficient in terms of cost, time, and environmental impact. As e-commerce is expected to keep growing, this puts pressure on last-mile deliveries to reduce their environmental impact if Sweden is to reach its environmental goals for 2045. READ MORE

  6. 5. Context-Aware Fashion Recommender Systems to Provide Intent-based Recommendations to Customers

    University essay from Stockholms universitet/Institutionen för data- och systemvetenskap

    Author : Edda Waciira; Marah Thomas; [2023]
    Keywords : e-commerce; Fashion Recommendation Systems; Machine Learning; Recommendation Systems;

    Abstract : In recent years, Recommendation Systems have revolutionized how social media and ecommerce are used. Fashion Recommendation Systems have made it easier for customers to do shopping, by recommending items to them based on various factors, such as their previous orders, and their similarities to other users. READ MORE