COVID-19 has had an unparalleled impact on the economy with a slowdown expected in most sectors including retail. In the short to mid-term, COVID-19 and subsequent nation-wide lockdown has further worsened the challenges faced by Indian retailers. With broken supply chains, it has led to a disconnected demand and supply making it difficult for retailers to cater to customer needs. It has also forced customers to rethink their purchase requirements and has led to a shift to contactless mode of deliveries, which is bound to become the new normal going forward. Establishing the right balance between demand and supply becomes key for retailers The Holy Grail for retailers is not only to identify the target customers and their real-time needs but also to proactively procure the right products to cater to the identified demand. This is even more critical amidst the COVID-19 pandemic, when due to broken supply chains there has been a massive demand supply mismatch. Digital enterprises that are utilising the data generated across the retail value chain and customer touchpoints to deploy AI-powered solutions have a significant edge over others. Here are my top 10 picks for AI use cases that can be a good starting point for retail enterprises (specifically amid the pandemic) in their journey towards becoming an intelligent enterprise. These use cases will definitely help retail enterprises survive the crisis and thrive in the long term. Customer Segmentation – Use of AI for creation of customer segments and personas based on real time transaction, demographic and behavioural data, enabling retailers with dynamic pricing for its products, predicting customer behaviour to target and personalise communication, and create cross-sell models. Demand forecasting – Using machine learning and leveraging contextual data to build models enabling retailers to optimise product availability, and gaining a better understanding of sales patterns and anomalies. Store Assortment Optimisation – Customers are restricting their store time with the fear of COVID-19 and that makes getting the right product assortment critical. AI helps store-level customisation of assortments based on store data (returns, purchases, and receipts data). This can also be done for online stores to help increase customer retention. Hyper Targeted Campaigns – It is critical for retailers to identify the right time to push a particular product to ensure maximum sales. AI-powered systems are helpful in suggesting the product and time slot in which it needs marketing. Personalised Marketing – For successful hyper-targeted campaigns it is also important for retailers to ensure the right marketing channel and the right message. Based on a customer’s past behaviour, AI-powered system picks the right way (channel, messaging, and discounts) of communication and sends personalised messages. Fraud Detection – The risk of potential frauds also increases amid these trying times, with a huge volume of online orders. AI-based system can predict potential frauds based on customer profiles and past purchase/returns data. On Time Delivery – With majority of customers opting for home delivery of products, it becomes critical for retailers to ensure on-time delivery. Predictive analytics and AI algorithms can help determine the most cost-effective and energy-efficient route to the destinations. Omni-Channel Customer Service – With restricted access to physical stores, consumers are opting for Omni-channel services. By connecting experiences across channels, building customer knowledge through data and creating discussions within user communities, AI platforms help brands acquire, retain and grow relationships with their customers. Customer Service Chat bot – The need for contactless deliveries has forced many consumers to opt for online purchases. The high volumes also result in larger volumes of queries and concerns. AI-powered chat bot can understand customer’s queries and respond. It can understand a customer’s emotion and can prioritise and alert human customer service agents to intervene. Visual Workforce Monitoring – AI system to detect safety compliance of the workers. This is specifically important in the current COVID-19 times when hygiene factors are critical. If the system detects any violation of safety norms, it can alert and share images for review. NASSCOM Research, NASSCOM CoE – DS&AI along with EY released a report titled “Indian Retail: AI Imperative to Data-Led Growth” focusing on AI opportunities in India’s retail sector. The report provides a unique periodic table of 100+ AI use cases across the retail value chain. The use cases identified in this article are also a part of the report. The report also highlights best practices across retail enterprises that have implemented these use cases. Download the report now: https://tinyurl.com/y9johts2

This blog highlights different ML algorithms used in blockchain transactions with a special emphasis on bitcoins in retail payments. The potential of blockchain to solve the retail supply chain manifests in three areas. Provenance: Both the retailer and the customer can track the entire product life cycle along the supply chain. Smart contracts: Transactions among disparate partners that are prone to lag can be automated for more efficiency. IoT backbone: Supports low powered mesh networks for IoT devices reducing the needs for a central server and enhancing the reliability of sensor data.

Artificial intelligence (AI) has been making serious technical progress over the last several years, but not in the political sense. Tech giants like Microsoft and Google, and online retailers like Amazon, have found new ways to accelerate their products using AI-driven algorithms. AI isn't exactly the correct term, however -- at least not in the sense that general consumers know it, in regard to machines like HAL from 2001: A Space Odyssey or Skynet in the Terminator movies. There's a difference between AI and Machine Learning, but it's such a new concept for the zeitgeist, the two are easily confused. AI is a broad term encompassing technology that employs advanced computer intelligence, but it's Machine Learning (ML) that really gives computers that human-like intellect seen in science fiction.

Researchers at Valuates Reports say the global use of artificial intelligence (AI) in retail markets is set to reach a market share of $14.7 Billion by 2026 from $2.7 Billion in 2020, at a CAGR of 32.7%. The versatility of the technology makes it valuable for optimizing the supply chain, using existing data to improve conversion, and customizing shopping experiences through predictive modeling and micro-targeting. The company's report, titled "Global Artificial Intelligence(AI) in Retail Market Size, Status and Forecast 2020-2026", points out several trends and factors influencing the use of AI in retail markets. Some major companies in the AI arena include IBM, Microsoft, Nvidia, Amazon Web Services, Oracle, SAP, Intel, Google, Sentient Technologies, Salesforce, and Visenze. For more details, ask for a sample of the "Global Artificial Intelligence(AI) in Retail Market Size, Status and Forecast 2020-2026" report.

Fashion retailers require accurate demand forecasts for the next season, almost a year in advance, for demand management and supply chain planning purposes. Accurate forecasts are important to ensure retailers' profitability and to reduce environmental damage caused by disposal of unsold inventory. It is challenging because most products are new in a season and have short life cycles, huge sales variations and long lead-times. In this paper, we present a novel product age based forecast model, where product age refers to the number of weeks since its launch, and show that it outperforms existing models. We demonstrate the robust performance of the approach through real world use case of a multinational fashion retailer having over 300 stores, 35k items and around 40 categories. The main contributions of this work include unique and significant feature engineering for product attribute values, accurate demand forecast 6-12 months in advance and extending our approach to recommend product launch time for the next season. We use our fashion assortment optimization model to produce list and quantity of items to be listed in a store for the next season that maximizes total revenue and satisfies business constraints. We found a revenue uplift of 41% from our framework in comparison to the retailer's plan. We also compare our forecast results with the current methods and show that it outperforms existing models. Our framework leads to better ordering, inventory planning, assortment planning and overall increase in profit for the retailer's supply chain.

Users can create a room in app, invite friends or family and users can sing along in karaoke-style without the vocals. Music students, teachers, or anyone who wants to improve their singing skills can also use it for solo practice. If you're shy about singing karaoke, you can enter a private or solo room and listen to the isolated song vocals. To use MusicBucket for a karaoke social, invite your friends to a karaoke room. They can select a song or upload their own song and start singing, as shown in the following screenshot. Users take turns singing songs with the background music.

Launched at AWS re:Invent 2018, Amazon Sagemaker Ground Truth is a capability of Amazon SageMaker that makes it easy to annotate machine learning datasets. Customers can efficiently and accurately label image, text and 3D point cloud data with built-in workflows, or any other type of data with custom workflows. Data samples are automatically distributed to a workforce (private, 3rd party or MTurk), and annotations are stored in Amazon Simple Storage Service (S3). Optionally, automated data labeling may also be enabled, reducing both the amount of time required to label the dataset, and the associated costs. As models become more sophisticated, AWS customers are increasingly applying machine learning prediction to video content.

GPUs can significantly speed up deep learning training, and have the potential to reduce training time from weeks to just hours. Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. In this post, we focus on general techniques for improving I/O to optimize GPU performance when training on Amazon SageMaker, regardless of the underlying infrastructure or deep learning framework. You can typically see performance improvements up to 10-fold in overall GPU training by just optimizing I/O processing routines. A single GPU can perform tera floating point operations per second (TFLOPS), which allows them to perform operations 10–1,000 times faster than CPUs.

Online retailers have long lured customers with the ability to browse vast selections of merchandise from home, quickly compare prices and offers, and have goods conveniently delivered to their doorstep. But much of the in-person shopping experience has been lost, not the least of which is trying on clothes to see how they fit, how the colors work with your complexion, and so on. Companies like Stitch Fix, Wantable, and Trunk Club have attempted to address this problem by hiring professionals to choose clothes based on your custom parameters and ship them out to you. You can try things on, keep what you like, and send back what you don't. Stitch Fix's version of this service is called Fixes.

Microsoft has acquired Snoqualmie, Wash.-based Orions Systems for an undisclosed amount. Orions Systems makes AI-powered vision systems, and its founder, Nils Lahr, has worked on imaging, content distribution network (CDN), and streaming media services. "Orions Systems has developed a strong reputation and leading technology for organizations seeking to gather and analyze high-value data specifically in the areas of video and image content. The acquisition will bring additional technologies that will allow solutions like Dynamics 365 Connected Store and the Microsoft Power Platform to offer retailers and other organizations a way to build and train their own AI models to customize and optimize how they can learn from their physical space. This extra set of tools will deliver on scenarios beyond what is offered out-of-the-box today and can adapt to the truly unique dimensions and needs of their physical spaces," said Microsoft Dynamics 365 Corporate Vice President Muhammad Alam in a July 7 blog post announcing the deal.