Online shopping is a fast rising field where many people are opting to buy products and items online instead of going to physical stores and malls. This is pegged on the rising use of smart phones that have sky-rocketed the field of ecommerce.

Many people have ventured online to sell products and services to clients who prefer shopping online. For example, with the onset of online stores, such as Amazon, many business people have come between Amazon and the consumer by writing product reviews and guides that have helped many customers to have a better understanding of the kind of products to buy and the products that meet their needs 100%.

The reviews of products as well as the pros and cons are exactly what customers will get when they visited a physical store or mall, including a product review site. This means that online shopping has become the next big thing!​

Business Finance Commerce E-commerce Online Shopping

As I mentioned in the Kibo Code Review, Making Money online is not a tough job, if you know the secrets mentioned in kibo code e-commerce training program. I have mentioned the steps above to get you an idea about the program

E-commerce Make Money Online Kibo Code

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 Performance Metrics Sales Revenues Buy-to-Visit Ratio Revenue Throughput Potential Loss 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'.

E-commerce Performance Metrics Sales Revenues Buy-to-Visit Ratio Revenue Throughput Potential Loss Throughput

e-Commerce prediction rate for evaluation.

This is DRAFT and non-functional

E-commerce Prediction Rate

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 Performance Metrics Sales Revenues Buy-to-Visit Ratio Revenue Throughput Potential Loss 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'.

E-commerce Performance Metrics Sales Revenues Buy-to-Visit Ratio Revenue Throughput Potential Loss 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'.

E-commerce Performance Metrics Sales Revenues Buy-to-Visit Ratio Revenue Throughput Potential Loss Throughput