2021 global trends in e-commerce—an AI analysis

Written by
Andy Pandharikar
January 10, 2021

Commerce AI published a comprehensive report on global ecommerce trend for 2021. Here is what we saw:

As the pandemic hit the world in 2020, the world had to shift to digital platforms. All the aspects of people's lives from work, entertainment, shopping, learning, and communication now moved online. No matter how robust Artificial Intelligence (AI) algorithms have been built, nothing could have predicted the pandemic's outcomes. The pandemic has completely transformed people's lives as decades of digital transformation has taken place in months. The companies that are well equipped to handle their operations online could successfully capture the market compared to physical stores.

Pandemic and E-commerce

The paradigm shift to digital has caused a massive increase in online shopping, which has generated new buying trends of people to emerge. Initially, as the panic set in, people started to buy products in bulk in anticipation that products will run out of stock. This was when the suppliers were unable to produce enough goods to satisfy the unprecedented increase in demand over the world. People started to stock up medical supplies, tissues, food for the pantry, canned products, and other similar items to peacefully spend their time at home without running out of essential utilities. For entertainment purposes, board games, puzzles, and video games also saw an increase in demand during the pandemic's initial phases.

After the basic needs of people were met, the demand increased for other goods required to spend time at home. This included purchasing computer accessories, fitness equipment, self-grooming items, and other electronics to prepare food more efficiently. During this buying spree of people, E-commerce websites such as Amazon saw an increase in traffic, which became increasingly unmanageable. There was another problem that people had to buy products without physically seeing the products. For this purpose, Amazon reviews became a common source of knowledge about the listed products' usability and performance.

With all this increase in demand, other logistical issues were faced by E-commerce companies. There was not enough labor power or warehousing available to cater to the needs of the people. As Amazon would have to ship large quantities of products, storage became a significant challenge as the space was limited. New storage spaces were bought or rented temporarily to keep the business running. Delivery drivers had to work long, tiring hours to deliver the products on time to people. This increase in demand created problems for many people working in Amazon warehouses, and this issue was later resolved by hiring extra labor.

The future is AI.

Predicting demand has become more comfortable with the use of AI. We are surrounded by AI systems most of the time. While typing on the search bar, as soon as we order a letter, a wide array of suggestions drops down, based on previous searches or searches that the system expects you to choose from. There have been instances when people searched for a particular product and that product popped up on all their social media. Using maps to seethe traffic, making coffee, cleaning floors, automatic scanning of products, auto-recording, and other tasks have been eased by AI. From YouTube recommendations to using maps to locating issues in the human body, all of that has been made possible through machine learning and AI.

Earlier on, there was great emphasis on the people's productivity, the mechanical processes, their drivers, etc. People would be doing the same thing repeatedly, and the decisions would be made mechanically, taking a lot of precious time. Now, times, as well as the metrics, have changed. Data now drives the world. Data is everywhere, while companies are now embracing that some structures need to be changed for substantial growth, and they should incorporate artificial intelligence. The dynamics of the scale, scope, and learning are changing.

Compared to the typical processes that have been around for a while now, the AI systems are very much scalable. Similarly, the AI firms do not face any immediate reducing returns after some time, as do the typical processes. In the regular companies, there comes a level after which they start getting diseconomies of scale. AI now works as a system that is spread across the company. It is not relating to the specific department or area of expertise as has been happening in the organizations' usual structures.

Discovering the patterns

For discovering patterns in data, analytics is conducted on the data using different modern tools that are readily available. To find meaningful insights and patterns, the accuracy of data is essential. With the help of AI, data collection has become very efficient due to the shift from physical shopping to E-commerce. Every action of an individual can now be recorded, such as the amount of time spent on a page, where the clicks have been made, what products have been purchased, and other useful data. With the help of modern technology, a large amount of data is being collected, which needs to be refined to discover insights.

The focus has now shifted from collecting data to conduct data analytics on the vast amount of data available. The highest-paying research fields are directly related to data analytics, which proves a strong emphasis on the subject. The more successfully the data can be analyzed, the better predictions can be about people's purchase decisions. Predicting future trends of demand for different products would also be more comfortable with data analytics.


The whole world has now shifted online due to the pandemic, and the shopping habits of people have been transformed. All the activities, including but not limited to working, shopping, socializing, and entertainment, have shifted to digital platforms. This digital use can be recorded by the robust AI systems that have been built by large organizations to collect data and information. Analytics can then be performed on the available data to predict future trends in demand for various products.


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