Basics of AI and Machine Learning for Ecommerce Professionals

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Everyone seems to be talking about artificial intelligence (AI), and making sense of it all can feel overwhelming. It’s easy to get lost in technical jargon and FOMO. But knowing some of the basics will help you stay ahead of the game. This article offers a quick overview of some key AI terminology and shows how it’s already being used in eCommerce today.

How is AI Different from Traditional Programming?

In traditional programming, you define the set of rules according to which the computer will act. Following that, you feed in the data and then receive the output. AI is the exact opposite. You give the computer the data and the output, and then ask the computer to fill in the steps in between.

AI, Machine Learning, and Deep Learning: How Are They Different and How Are They Applied in Ecommerce?

Many people confuse ML with AI and deep learning. Let’s take a quick look at what defines these terms, along with examples of how they are commonly used in eCommerce.

  • Artificial Intelligence is a technique that enables machines to mimic human behavior, like composing songs or writing code. One common eCommerce application for AI are chatbots that customers interact with on your sites. These bots can help customers troubleshoot issues and steer them towards suitable products, all while cutting down on customer service costs.
  • Machine Learning is a subset of AI that trains machines to make decisions, and teaches machines to train other machines to define rules and take actions. ML is often used to secure web accounts using CAPTCHA images, to detect anomalies in website user behavior, or to perform certain aspects of customer segmentation.
  • Deep Learning is a subset of machine learning that utilizes multi-layer neural networks. A few typical uses for deep learning in eCommerce include demand forecastinginventory managementcustomer segmentation, and personalized marketing.

Supervised Versus Unsupervised Learning

An important distinction in AI is between supervised and unsupervised learning. Supervised learning involves a human teaching a computer to recognize data. In unsupervised learning (unsurprisingly) there is no supervisor. You give the computer a target and it learns how to get there. Let’s take a look at a few of the kinds of problems solved by supervised and unsupervised learning.

Problems for Supervised Learning


This involves repeatedly feeding the computer data and a description of that data. This teaches the computer to recognize the data and sort it efficiently. For instance, image recognition is a form of classification that might require you to feed a computer pictures featuring either cats or dogs and then tell it which animal the picture contains, teaching it to distinguish between them. In eCommerce contexts, this might be used to auto-tag products or identify violations of intellectual property laws.


This involves ranking data according to certain criteria or putting a line of best fit through a data set. The goal is usually to find correlations and relationships between images and objects. A typical regression problem might be figuring out how expensive a house is likely to be in a particular city, based on its size. In eCommerce, regression is often used for product recommendation systems that try to match the best product to a user’s tastes.

Problems for Unsupervised Learning


Clustering identifies similar unlabelled data points within datasets. One everyday example is your email account, where junk mail is sorted from your inbox. Junk mail is not essentially different from the mail we wish to receive, but due to traits shared by a lot of spam, it is usually possible to differentiate junk from your regular messages. 

Dimensionality reduction 

Dimensionality reduction removes unimportant features from datasets. Raw datasets are often huge and contain irrelevant information. This technique determines which elements aren’t helpful, and leaves only the pertinent features. For instance, if you’re examining a furniture store’s inventory, but only want to know about the tables, dimensionality reduction can remove all of the information relating to chairs and bookcases.

How AI is Used in Ecommerce

One of AI’s most valuable eCommerce applications is the ability to segment customers into groups and then predict their probability of buying, buying again, or using a coupon. You can then target them with personalized promotions, or send them to a part of the site that suits their needs.

In eCommerce, advanced AI techniques are used to offer tailored promotions and optimize websites for conversion, retention, and superior customer experience. To decide which coupon will be given to a particular user, and when, ML software deduces whether they are eligible, when they are likely to abandon the cart, and their probability of purchase, before proposing the incentive. This process involves both clustering and classification to segment customers as accurately as possible. To create the appropriate segments, AI software looks at factors like password saver, ad blocker, VPN, CPU availability, Wifi strength, battery status, dark mode, and number of tabs open.

For instance, our research has shown that 10% of customers use incognito mode while browsing, and these customers are 50% less likely to buy. On the other hand, customers with many tabs from your site open at once are much more likely to buy. Using this kind of information, we can target low-probability customers with large discounts, and high-probability customers with small discounts, or none at all. 

Embrace AI to Drive Ecommerce Success

AI can feel mysterious and overwhelming. But once you understand how it works, it can become a powerful tool in your arsenal as an eCommerce professional. Using techniques such as classification, regression, clustering, and dimensionality reduction, you can predict sales, narrow down data, and target customers with tailored discounts. These methods are fast becoming the norm. Embracing them early will position your company for success in the coming years.

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