E-commerce Models

These models and simulations have been tagged “E-commerce”.

The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three c
Potential Loss Throughput Performance Metrics Sales Revenues Revenue Throughput Buy-to-Visit Ratio E-commerce
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three classes of e-Customers: 'Spendthrifts', 'Average Spenders', and 'Tightwads', and an operational profile based on these three classes, which assumes 24% 'Tightwads', 61% 'Average Spenders', and 15% 'Spendthrifts'.
e-Commerce Revenue Estimator
Ilija Hristoski
8
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three c
E-commerce Revenue Throughput Sales Revenues Potential Loss Throughput Buy-to-Visit Ratio Performance Metrics
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three classes of e-Customers: 'Spendthrifts', 'Average Spenders', and 'Tightwads', and an operational profile based on these three classes, which assumes 24% 'Tightwads', 61% 'Average Spenders', and 15% 'Spendthrifts'.
Clone of e-Commerce Revenue Estimator
Denis Smagin
e-Commerce prediction rate for evaluation. This is DRAFT and non-functional
Prediction Rate E-commerce
e-Commerce prediction rate for evaluation.

This is DRAFT and non-functional

RNTLS Insight
Pantelis Nicolaou
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three c
Performance Metrics Sales Revenues Potential Loss Throughput Buy-to-Visit Ratio Revenue Throughput E-commerce
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three classes of e-Customers: 'Spendthrifts', 'Average Spenders', and 'Tightwads', and an operational profile based on these three classes, which assumes 24% 'Tightwads', 61% 'Average Spenders', and 15% 'Spendthrifts'.
Clone of Clone of e-Commerce Revenue Estimator
Denis Smagin
Insight diagram
E-commerce
Seller Aggregation On-Demand
Rohan George Cheriyan
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three c
Sales Revenues Performance Metrics E-commerce Buy-to-Visit Ratio Potential Loss Throughput Revenue Throughput
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three classes of e-Customers: 'Spendthrifts', 'Average Spenders', and 'Tightwads', and an operational profile based on these three classes, which assumes 24% 'Tightwads', 61% 'Average Spenders', and 15% 'Spendthrifts'.
Clone of e-Commerce Revenue Estimator
Gosto Zaum
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three c
Potential Loss Throughput Performance Metrics Sales Revenues Revenue Throughput Buy-to-Visit Ratio E-commerce
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three classes of e-Customers: 'Spendthrifts', 'Average Spenders', and 'Tightwads', and an operational profile based on these three classes, which assumes 24% 'Tightwads', 61% 'Average Spenders', and 15% 'Spendthrifts'.
Clone of e-Commerce Revenue Estimator
Robb Green
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three c
Revenue Throughput E-commerce Potential Loss Throughput Buy-to-Visit Ratio Performance Metrics Sales Revenues
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three classes of e-Customers: 'Spendthrifts', 'Average Spenders', and 'Tightwads', and an operational profile based on these three classes, which assumes 24% 'Tightwads', 61% 'Average Spenders', and 15% 'Spendthrifts'.
Clone of e-Commerce Revenue Estimator
hansolo
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three c
Potential Loss Throughput E-commerce Performance Metrics Sales Revenues Buy-to-Visit Ratio Revenue Throughput
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three classes of e-Customers: 'Spendthrifts', 'Average Spenders', and 'Tightwads', and an operational profile based on these three classes, which assumes 24% 'Tightwads', 61% 'Average Spenders', and 15% 'Spendthrifts'.
Clone of e-Commerce Revenue Estimator
Sharan Jammanahalli Mahesh
Insight diagram
E-commerce
FEM, Assignment 1
Anton Viklund
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three c
Potential Loss Throughput Performance Metrics Sales Revenues Revenue Throughput Buy-to-Visit Ratio E-commerce
The simulation model provides a holistic insight into the e-Customers’ dynamic behavior during online shopping sessions, and combines both qualitative and quantitative aspects of the interaction between e‑Customers and a hypothetical e-Commerce website. It takes into account the existence of three classes of e-Customers: 'Spendthrifts', 'Average Spenders', and 'Tightwads', and an operational profile based on these three classes, which assumes 24% 'Tightwads', 61% 'Average Spenders', and 15% 'Spendthrifts'.
Clone of e-Commerce Revenue Estimator
Michelle Arifin