Essays about: "methods of evaluating customer service"

Showing result 1 - 5 of 12 essays containing the words methods of evaluating customer service.

  1. 1. Diffusion-based Vocoding for Real-Time Text-To-Speech

    University essay from Lunds universitet/Matematisk statistik

    Author : Lukas Gardberg; [2023]
    Keywords : Diffusion; Vocoding; Text-to-Speech; Machine Learning; Mathematics and Statistics;

    Abstract : The emergence of machine learning based text-to-speech systems have made fully automated customer service voice calls, spoken personal assistants, and the creation of synthetic voices seem well within reach. However, there are still many technical challenges with creating such a system which can generate audio quickly and of high enough quality. READ MORE

  3. 2. IT’S IN THE DATA 2 : A study on how effective design of a digital product’s user onboarding experience can increase user retention

    University essay from Linköpings universitet/Institutionen för datavetenskap

    Author : Gustav Fridell; [2021]
    Keywords : saas; software as a service; software-as-a-service; data-driven; data driven; analytics; A B testing; A B-testing; AB testing; AB-testing; bucket testing; split-run testing; web; web development; application; design; design pattern; programming; software; Fisher s exact test; Fisher exact test; growth; onboarding; onboarding experience; retention; user retention; churn; digital product; digital; statistics; user onboarding; customer; profitability;

    Abstract : User retention is a key factor for Software as a Service (SaaS) companies to ensure long-term growth and profitability. One area which can have a lasting impact on a digital product’s user retention is its user onboarding experience, that is, the methods and elements that guide new users to become familiar with the product and activate them to become fully registered users. READ MORE

  4. 3. Analysis of uncoordinated versus coordinated inventory control

    University essay from Lunds universitet/Produktionsekonomi

    Author : Mirja Björning Engström; Johanna Rådemar; [2020]
    Keywords : Inventory management; uncoordinated versus coordinated inventory control; single-echelon versus multi-echelon inventory control; Technology and Engineering;

    Abstract : This master thesis has been completed at the Faculty of Engineering, Production Management in collaboration with the company Duni Group. The overall objective has been to provide guidance to Duni and analyze how they should control their inventory at each node in their new supply chain set up. READ MORE

  5. 4. Using Machine Learning to Detect Customer Acquisition Opportunities and Evaluating the Required Organizational Prerequisites

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

    Author : Olle Malmberg; Bobby Zhou; [2019]
    Keywords : Consumer decision journey; Customer acquisition Customer relationship management; Machine Learning; Organizational prerequisites; User activity; Random forest; XGBoost; Användaraktivitet; Consumer decision journey; Customer relationship management; Kundförvärv; Maskininlärning; Organisatoriska förutsättningar; Random forest; XGBoost;

    Abstract : This paper aims to investigate whether or not it is possible to identify users who are about change provider of service with machine learning. It is believed that the Consumer Decision Journey is a better model than traditional funnel models when it comes to depicting the processes which consumers go through, leading up to a purchase. READ MORE

  6. 5. Evaluating Random Forest and a Long Short-Term Memory in Classifying a Given Sentence as a Question or Non-Question

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

    Author : Fredrik Ankaräng; Fabian Waldner; [2019]
    Keywords : Bag-of-Words; Chatbot; Classification; LSTM; Machine Learning; Natural Language Processing; Random Forest; Word2Vec;

    Abstract : Natural language processing and text classification are topics of much discussion among researchers of machine learning. Contributions in the form of new methods and models are presented on a yearly basis. However, less focus is aimed at comparing models, especially comparing models that are less complex to state-of-the-art models. READ MORE