Shelf-mounted cameras paired with artificial intelligence facial recognition software that can identify a person's age, gender, and ethnicity were one of the emerging systems being pitched to retail companies during this year's National Retail Federation Big Show in New York in January. The idea was to give physical stores demographic information that could guide how they market to individual customers. It's something that could give them a competitive edge against online retailers such as Amazon, that have been leveraging customer data all along. But using cameras to capture photos of your customers in a way they may not even notice seems like it could be crossing that line between cool technology and creepy technology. Beyond that, there could be other problems, too. What if the software misidentifies a man as a woman and offers him a discount on feminine hygiene products?

The retail market is becoming increasingly competitive. Customers are expecting more personalised offers and are also more aware of their choices. Operational costs are rising and the amounts of data retailers need to factor in when setting prices are accumulating nonstop. As a result, businesses are hunting for new strategies to increase revenue. Currently, the pricing process is in chaos.

Amazon SageMaker makes it easier for any developer or data scientist to build, train, and deploy machine learning (ML) models. While it's designed to alleviate the undifferentiated heavy lifting from the full life cycle of ML models, Amazon SageMaker's capabilities can also be used independently of one another; that is, models trained in Amazon SageMaker can be optimized and deployed outside of Amazon SageMaker (or even out of the cloud on mobile or IoT devices at the edge). Conversely, Amazon SageMaker can deploy and host pre-trained models from model zoos, or other members of your team. In this blog post, we'll demonstrate how to deploy a trained Keras (TensorFlow or MXNet backend) or TensorFlow model using Amazon SageMaker, taking advantage of Amazon SageMaker deployment capabilities, such as selecting the type and number of instances, performing A/B testing, and Auto Scaling. Auto Scaling clusters are spread across multiple Availability Zones to deliver high performance and high availability.

Shall we take another case study to be precise? You want to buy a new washing machine for your Bangalore home. So, you go to your favorite home improvement retail store to check out the model. Also, you had already bought a water purifier from the same store before three years. Now on the website, you select a washing machine model along with your family and want it delivered to the home.

Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values. Users' demands, implying strong purchase intents, can be the most useful way to promote products sales if well utilized. Previous recommendation models mainly focused on user's general interests to find the right products. However, the aspect of meeting users' demands at the right time has been much less explored. To address this problem, we propose a novel Long-Short Demands-aware Model (LSDM), in which both user's interests towards items and user's demands over time are incorporated. We summarize two aspects: termed as long-time demands (e.g., purchasing the same product repetitively showing a long-time persistent interest) and short-time demands (e.g., co-purchase like buying paintbrushes after pigments). To utilize such long-short demands of users, we create different clusters to group the successive product purchases together according to different time spans, and use recurrent neural networks to model each sequence of clusters at a time scale. The long-short purchase demands with multi-time scales are finally aggregated by joint learning strategies. Experimental results on three real-world commerce datasets demonstrate the effectiveness of our model for next-item recommendation, showing the usefulness of modeling users' long-short purchase demands of items with multi-time scales.

OVER the last few years, retailers have been working hard to leverage AI in a way that maximizes customer loyalty and spends. According to one Juniper Research study, the retail industry is set to spend some US$7.3 billion on AI solutions in the next three years, as compared to US$2 billion last year. The study highlighted that more retailers are seeking to emulate Amazon.com's Smart shelves have the potential to boost the dwindling foot traffic to traditional brick and mortar outlet. These futuristic shelves, currently being piloted by Krogers features LED displays that will show targeted ads and promotions.

It offered'queue-less shopping' – a self-service concept that allowed consumers to shop the store by pushing a metal trolley around the aisles rather than waiting in line at a counter to be served. Today, retailers are becoming increasingly reliant on customer experience innovations such as this to ensure their continuity, as the industry is entering the most transformational period of its experience in response to the current crisis hitting the UK high street. Already a disruptor with its convenience-focused online retail service, Amazon redoubled its efforts to disrupt brick-and-mortar retail outlets by launching its own physical store, Amazon Go, in 2016. By and large, Amazon Go resembled any other supermarket: products on shelves, arranged by aisles; an assortment of baskets and trolleys for transporting goods; and a bright, fresh, welcoming atmosphere to attract customers. It's revolutionary move, however, was to use intelligent innovations in IoT technology to provide the most convenient shopping experience yet.

In the United States and Western Europe, many traditional grocery retailers are seeing their sales and margins fall--and things could get even worse. Here's how to reverse the trend. To put it bluntly, much of the $5.7 trillion global grocery industry is in trouble. Although it has grown at about 4.5 percent annually over the past decade, that growth has been highly uneven--and has masked deeper problems. For grocers in developed markets, both growth and profitability have been on a downward trajectory due to higher costs, falling productivity, and race-to-the-bottom pricing. One result: a massive decline in publicly listed grocers' economic value.

Walking into the first chamber of Ocado's robot warehouse system in May, I was immediately struck by three things: how cold it was, how enormous it was, and how quiet. No shouts from warehouse staff, or tinny radios playing pop hits. The robots collect groceries from crates beneath them, and drop them off at a packing station. Occasionally they would all simultaneously come to a halt, green lights blinking, awaiting their next command, received via an unofficial 4G network custom-built by Ocado. Hundreds of swarm robots at Ocado's automated warehouse gathering grocery orders - they have a top speed of 4m per second.

The boss of Apple's retail stores is stepping down amid concern about how many iPhones the company is selling. Angela Ahrendts, who ran Apple's 506 retail stores as well as the online store, had been one of the company's most prominent executives. She regularly appeared at its global launch events, and was often touted as a potential successor to Tim Cook. She had been paid millions of dollars to move to California from London to join Apple from Burberry. After her arrival, she led a major change in the way the stores worked, redesigning its businesses and moving towards seeing them as town halls.