AI in retail industry is rapidly transforming the customer journey and experience. The fundamental steps of shopping have mostly stayed the same throughout the years: enter a store, choose the ideal item, and pay for it. With personalization, automation, and more efficiency, artificial intelligence has the ability to revamp the conventional shopping experience totally. The retail sector has been going through a digital shift for a while now. Every branch of retail companies has seen a rise in speed, efficiency, and accuracy, largely because of sophisticated data and predictive analytics technologies that support businesses in making data-driven business choices.

Artificial Intelligence (AI) has begun to revolutionize the retail sphere. AI-powered solutions help companies optimize their business processes, enhance customer satisfaction, boost their sales, and maximize revenues. From this article, you'll get to know how exactly retailers can benefit from AI. All the technologies that will be described already exist. Businesses that managed to integrate them earlier than their competitors have made a lot of money from them.

In the FARFETCH Fashion Recommendation challenge, the participants needed to predict the order in which various products would be shown to a user in a recommendation impression. The data was provided in two phases - a validation phase and a test phase. The validation phase had a labelled training set that contained a binary column indicating whether a product has been clicked or not. The dataset comprises over 5,000,000 recommendation events, 450,000 products and 230,000 unique users. It represents real, unbiased, but anonymised, interactions of actual users of the FARFETCH platform. The final evaluation was done according to the performance in the second phase. A total of 167 participants participated in the challenge, and we secured the 6th rank during the final evaluation with an MRR of 0.4658 on the test set. We have designed a unique context-aware system that takes the similarity of a product to the user context into account to rank products more effectively. Post evaluation, we have been able to fine-tune our approach with an MRR of 0.4784 on the test set, which would have placed us at the 3rd position.

Thinkers360: Firstly, what is omnichannel commerce? Since the topic has been around for a while, what is unique about PROS' perspective on this topic and why now? Well, the topic's been around for a while now, largely thanks to patterns in the consumer world. The first thing that kind of comes to mind is what happens in a typical business to consumer or retail experience where you might purchase online but pick up in-store. And thanks to COVID right now, we've seen the rising popularity of curbside pickup.

As a data science intern, I have come to the realization that the amount of value you provide is directly proportional to the price tag you get. The number of skills you possess only matters if those skills can translate to added value to your customer in the form of increased sales or decreases costs. The more value you provide, the more valuable you are as a Data Scientist. In today's article, I will be going over a significant topic that I have come across repeatedly during my time as an internee and that topic is Price Elasticity. Price Elasticity tells us how sensitive sales of a particular product are to a unit change in its price.

Product risk assessment is the overall process of determining whether a product, which could be anything from a type of washing machine to a type of teddy bear, is judged safe for consumers to use. There are several methods used for product risk assessment, including RAPEX, which is the primary method used by regulators in the UK and EU. However, despite its widespread use, we identify several limitations of RAPEX including a limited approach to handling uncertainty and the inability to incorporate causal explanations for using and interpreting test data. In contrast, Bayesian Networks (BNs) are a rigorous, normative method for modelling uncertainty and causality which are already used for risk assessment in domains such as medicine and finance, as well as critical systems generally. This article proposes a BN model that provides an improved systematic method for product risk assessment that resolves the identified limitations with RAPEX. We use our proposed method to demonstrate risk assessments for a teddy bear and a new uncertified kettle for which there is no testing data and the number of product instances is unknown. We show that, while we can replicate the results of the RAPEX method, the BN approach is more powerful and flexible.

Artificial Intelligence is redefining how we look at technology and use it, especially the mobile app industry. A lot of people have used this technology to improve the engagement level within the app, and increase the conversion rate for the business. We don't have an Iron Man-like mobile app as of now but, we are constantly progressing towards making apps revolutionary, usable, and predictive. We are using predictive analytics' engine, and other coding methods to enhance the mobile apps and make them more experiential. There are many ways in which Artificial Intelligence has enhanced the mobile app industry.

Artificial Intelligence has grown massively over the past few years. You would have noticed how it is making the businesses strong and sufficient with its capabilities and power. From managing the customers at your store to improving their overall experience to providing them with personalized solutions, there is a lot that the technology can do for your eCommerce outlet. AI, though it is implemented in most businesses, is still a futuristic technology. According to a report by PwC, this technology will contribute close to $15.7Tn by 2030 to the global economy.

In earlier days, word-of-mouth was a powerful thing that was used for promotional purposes to get the revenue going. As time passed, affiliate marketing became an important component for revenue generation. Today, machine learning is being used effectively for various marketing strategies. Most of us use mobile devices to surf the internet for shopping or many other things. Here AI comes into picture, which is used to gather a variety of information around customers.

Do you ever think that your business needs a new marketing strategy? Or Do you feel that your business requires modern technology Or your business is lagging in the industry? If yes, then you better think "out of the box" while formulating marketing strategies and engage the latest technology in your business because it can help you achieve all the objectives and make your business reach new heights. Here are some of the exciting technologies that can help your business, such as Augmented Reality devices, Artificial Intelligence, Machine Learning, Drone cameras, Wearable Technology, and Chatbot for business. When we talk about fastest growing technologies, we cannot deny the fact that Chatbot for business have proved to be the new wave in the industry.