Few instances of artificial intelligence in e-commerce include customer-centric search through video, and image recognition Virtual Assistants to is a …

How do we know if a customer is going to shop or walk away? Understanding the customers is crucial to any seller/store/online platform. This understanding can be important in convincing a customer who is just browsing to buy a product. In offline stores, the inferences derived influence the placement of objects in the store. When the same experience is translated to an online store, the sequence of web pages browsed to reach a product becomes important.

Data scientists are often tasked with working through massive data stores to provide workable insights. These insights are then analyzed in order to identify patterns related to business intelligence or even human behavior. However, it may be one thing to construct data queries and machine learning pipelines, employing all types of optimizations and clever algorithms. It is entirely another thing to be able to communicate the results of arduous data collection and modeling to colleagues that do not share an intimate knowledge of data processing. This is where data visualization arrives to save the day. In his book "Good Charts," Scott Berinato exclaims: " A good visualization can communicate the nature and potential impact of information and ideas more powerfully than any other form of communication."

Amir Konigsberg is the current CEO of Twiggle, a business that enables e-commerce search engines to think the way humans do. Watch any recent interviews with Amir and he will tell you that consumers often abandon e-commerce experiences because the product results displayed are often irrelevant. To tackle this problem, Twiggle utilises natural language processing to narrow, contextualise and ultimately improve search results for online shoppers. Another business that is trying to improve e-commerce search is US-based tech start-up Clarifai. Clarifai's early work has been focused on the visual elements of search and, as their website states, their software is'artificial intelligence with a vision'.

Deep learning, an advanced form of artificial intelligence (A.I.), is all around us, and it's growing increasingly ingrained in how we live and work. Deep learning tech is "the brains" behind the automated traffic control on our city streets, our sophisticated translation systems, and the fast, accurate facial recognition at our airports. Additionally, deep learning can also vastly improve both shopper experience and retailers' sales, on and offline. Technically speaking, deep learning uses artificial neural networks, software constructs that are inspired by the biological structure of the human brain. Neural networks are excellent at rapidly processing and understanding unstructured data such as video, images, audio or large amounts of text without requiring intervention from human engineers, who could not possibly keep pace with the A.I. Neural network tech particularly shines at visual perception, natural language understanding and the ability to predict behaviors such as online shoppers' desires and purchase intent.

Python codes for the above analysis are available on my GitHub, do feel free to refer to them.

E-commerce is experiencing continuous evolution and has revolutionized retail industry significantly. This evolution is a vital requirement to meet the changing needs of people and make online shopping easier for them. The industry has seen steady growth in the last couple of years and doesn't look like it is stopping anytime soon. Speaking of its growth in recent years, a study, revealed that global e-commerce sales worth a whopping $3.453 trillion were made in 2019, and projected to even grow to $4.135 trillion in 2020. In 2021, the industry is expected to grow even further to hit the $4.878 trillion mark. At the start of the e-commerce wave, it was fairly limited in its capabilities due to limiting technology.

Product suggestions are an ingrained part of the ecommerce experience. With the up- and cross-selling opportunities that a good system can provide, a thoughtful ecommerce experience is invaluable, as Amazon's paid search and display advertising strategy has shown. For consumers, suggested products should bring real value. Rather than being haunted for weeks by a product they searched for once, consumers should experience helpful product suggestions which complement their purchases. Often, this is the case.

How many of you think that e-commerce is come of age? A friend of mine was telling me how she finds browsing through 80,000 dresses online to find that one perfect dress for a date the most tedious thing to do. And rather prefers to go to one store with 20 limited and options and pick one dress that is the best from among them. The dilemma of choosing from thousands, lakhs and crores of products to find that one particular things has put online shopping at the risk of saturation. In this world of fast entry and exit, continuous updating of technology is a must in any sphere and for e-commerce the ship will soon sink if it doesn't change its current strategy and include disruptive technology as a part of its growth plan.

Fraud is a major concern in business, and Internet fraud is perhaps the most worrying. But what if we told you that artificial intelligence can prevent Internet fraud? Sounds too good to be true, we know, but there are quite a few ways in which machine learning and AI are being used to stop Internet fraudsters in their tracks. The Internet is an incredible tool that allows us to stay in touch with friends from all over the world, watch unlimited cat videos, learn a new skill, and even shop from the comfort of our couch. However, it's also a tool rife with dangers as hackers lurk in the depths of the Net, looking to snag private data details from online shoppers when they least expect it.