Essays about: "Customer Clustering"

Showing result 1 - 5 of 59 essays containing the words Customer Clustering.

  1. 1. Exploring Automated Early Problem Identification Based on Diagnostic Trouble Codes

    University essay from Institutionen för tillämpad informationsteknologi

    Author : Mathias Forsman; Yihan Yang; [2024-03-05]
    Keywords : Automotive Industry; Early Problem Identification; Diagnostic Trouble Code; Case Study; Laboratory Experiment ; Machine Learning; Linear Regression; K-means Clustering;

    Abstract : In the current automotive industry, problem identification is a reactive process. It starts when the customer experiences a vehicle problem and goes to the workshop. Subsequently, all the problem-related data will be collected from the workshop and forwarded to the vehicle manufacturer. READ MORE

  3. 2. 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

  4. 3. Unsupervised Clustering of Behavior Data From a Parking Application : A Heuristic and Deep Learning Approach

    University essay from Umeå universitet/Institutionen för matematik och matematisk statistik

    Author : Edvard Magnell; Joakim Nordling; [2023]
    Keywords : ML; Machine learning; clustering; unsupervised learning; deep learning; autoencoder; AI; artificial intelligence;

    Abstract : This report aims to present a project in the field of unsupervised clustering on human behavior in a parking application. With increasing opportunities to collect and store data, the demands to utilize the data in meaningful ways also increase. READ MORE

  5. 4. Do estate-level characteristics generate unsafety? : Examining neighborhood and estate characteristics influence on perceived residential safety in Gothenburg

    University essay from Stockholms universitet/Statsvetenskapliga institutionen

    Author : Madeleine Frisk Garcia; [2023]
    Keywords : Perceived safety; fixed effects; estate level effects; customer satisfaction surveys;

    Abstract : Do estate and neighborhood characteristics influence perceptions of safety? Using data from a survey of residents living in municipal housing in Gothenburg, this paper argues that the spatial and social characteristics of a neighborhood vastly outpace the role of its socioeconomic and demographic composition, when it comes to accounting for the perceived safety of its residents. The dataset consists of survey data on residents’ perception of safety from 2013-2014 and 2016-2021 in Gothenburg linked with sociodemographic data at an estate level. READ MORE

  6. 5. Evaluation of Machine Learning techniques for Master Data Management

    University essay from Högskolan i Skövde/Institutionen för informationsteknologi

    Author : Fatime Toçi; [2023]
    Keywords : Master Data Management; Machine Learning; data quality; data duplicates;

    Abstract : In organisations, duplicate customer master data present a recurring problem. Duplicate records can result in errors, complication, and inefficiency since they frequently result from dissimilar systems or inadequate data integration. READ MORE