AI News, Strategies for Retailers To Drive AI While Keeping Customers Top of ... artificial intelligence

Introducing the First-Ever Customer Data Activation Platform, Powered by Blueshift

While Iterable is a solid solution for email-dependent organizations, the platform wasn’t built to support today’s rapidly changing landscape of marketing channels.

Our platform enlists an individual’s historical data to trigger event-based campaigns without requiring the marketing team to activate a specific promotion.

let us help you book another great vacation.” This feature, which we call transactional modeling, allows you to connect and segment lifecycle events/behaviors through a common identifier.

Blueshift’s customer record doesn’t have a single primary key, but can be created and executed against any of the following keys: email, phone, device UUID, customer_id and Blueshift’s anonymous_id.

Meanwhile, Iterable’s current documentation states the inability to send mobile push notifications, SMS, web-push, or the like to anyone without an email, as email is a required field.

Blueshift also stores behavioral data related to anonymous profiles so you don’t lose valuable data insights just because someone hasn’t signed in yet.

Even the best of the best will occasionally experience hiccups, but those that are designed from the ground up to be scalable will be far more stable than those that aren’t (over the last 18 months, Iterable has experienced 61 incidents which degraded the performance of the application compared to Blueshift’s 6).

Walmart's artificial intelligence-powered 'store of the future' might sound like hype, but AI has big potential for retailers big and small

There's a lot of talk out there about how AI can help retailers boost profits and delight consumers, like with Walmart's AI-powered 'store of the future' and Sephora's shade-picking technology.

Luq Niazi, IBM's global managing director for consumer industries, said that retailers are increasingly doubling down on adopting the technology in three key categories: shopper experience, production, and supply-chain logistics.

'You're seeing organizations dig deep into those major categories, depending on where they are as a company,' he said.

'And then once people are there, they realized that there's additional benefits to be gained if they can link those different areas, whether that's in their own organization or across organizations.'

Global consulting firm Oxford Economics surveyed 324 executives about their thoughts on AI in retail in 2018.

Ed Cone, the technology practice lead at the global consulting firm, said that the executives with more exposure to AI technology offered a particularly telling insight.

Only 13% of respondents agreed or strongly agreed that current AI applications 'live up to the hype.'

To the contrary, Cone said that respondents largely took a 'mature' view of AI integration: that it's both important and still developing.

For retailers, Niazi said that one of the biggest pitfalls linked to AI is the same issue affecting all other business sectors: the issue of bias.

The challenge lies in transitioning AI integration from a trendy new project to an integral part of the business.

But there's one significant chunk of retailers that may be holding themselves back: smaller retail operations.

'We've found that for small businesses, which we qualify as businesses that have less than $10 million in annual revenue, only 15% say that they have implemented AI-powered chatbots,' Oxford Economics assistant editor Matthew Reynolds told Business Insider.

The percentage jumps up to 39% for businesses with over $10 million in annual revenue and 67% for companies worth $1 billion.

'With AI, retailers can achieve a better understanding of what consumers are shopping for during different times of the year, which will help forecast demand and automate merchandise allocation,' Mitchell-Keller said.

Mitchell-Keller cited SAP's 'Best Run Beauty' showcase at the National Retail Federation's Big Show this year, where the company used 'machine learning technology to garner consumer insights about products across social media platforms.'

'With this level of insight, retailers can achieve stronger understanding of shopper inventory sentiment, helping them strengthen their offerings and competitive differentiation,' she said.

'While empty shelves give the appearance that products are selling, it's also a sign that inventory management isn't properly managed, which can lead to missed opportunities and frustrated consumers,' Mitchell-Keller said.

'Retailers can access and review real-time data for in-stock products, ensuring they are fulfilling customer orders accurately and efficiently,' she said.

NextOrbit is an AI services provider with clients like India-based online jeweler CaratLane, US grocery services firm Grocer Exchange, and Percentil, a used fashion retailer based in Spain.

Rajgopal said he's particularly passionate about offering AI tools to retailers for a reasonable price, so that the technology isn't exclusively in the hands of giants like Walmart and Amazon and other contenders that can foot the bill for 'huge armies of data scientists.'

of a certain product by learning about consumer preferences and taking into account local events that might bolster or dampen demand.

Cone told Business Insider that the mega-retailers of the world will likely continue to use AI to power their massive logistical operations, complex demand calculations, and 'fleets of robots.'

Cone reiterated the example of a mom-and-pop shop using a chatbot to provide customer service on its website 24/7.

But for Rajgopal, the benefits of AI outweigh those challenges tenfold, especially when it comes to the smaller players in the retail space.

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Jul 18, 2019 (Innovative Reports via COMTEX) -- The continued enhancement in computer visualization has long been used for quality assurance by detecting product defects in real time.

But now that manufacturing involves more information than ever integrated with the fact that plant managers do not want to pay employees to enter information-AI with computer vision can rationalize how information gets apprehended.

A factory worker should be able to acquire raw materials reserve from the shelf and have the stock transaction created automatically based on a camera observing the process.

The global artificial intelligence in manufacturing market contributed $513.6 million in 2017, and is projected to reach $15,273.7 million in 2025, growing at a CAGR of 55.2%.

Development of artificial intelligence-empowered chips, robots, and others in manufacturing help enhance the overall production line, and thus, has significantly increased the adoption of artificial intelligence in the manufacturing sector.

Factors such as mass production, operational proficiency, and enhanced productivity achieved by implementing artificial intelligence in manufacturing industry and its processes are estimated to propel the demand for artificial intelligence in manufacturing, globally.

Moreover, the improvement of more powerful and reasonable cloud computing infrastructures is devising a robust effect on the growth potential of AI, which is further expected to drive the market growth.

On the contrary, rise in technological innovations and development of smarter robots by companies are anticipated to offer lucrative opportunities for the players in the industry.

The global artificial intelligence in manufacturing market is segmented based on deployment, technology, application, industry, and region.

By technology, the market is divided into machine learning, computer vision, context awareness, and natural language processing.

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For traditional retail giants, this means entering the playing field with the likes of e-commerce behemoths Amazon and Alibaba, both of which are leveraging big data and powerful AI algorithms to transform the retail space.

For example, Alibaba has turned more than 1M mom and pop stores across China into AI-backed smart stores that can predict surges in demand for certain goods and use heat sensor data to better analyze foot traffic, while Amazon is working on expanding its range of cashierless Amazon Go stores.

But in our 2018 analysis of 1,600+ publicly traded US retailers’ earnings calls, we found that only 9 of 50+ companies had started to discuss an AI strategy.

With recent advances like computer vision-based cashierless stores, an increasing number of retailers may be forced to improve their AI game in the coming years.

Below, we analyze how AI, machine learning, and computer vision-based technologies — including robots used for heavy lifting, navigation, and assembly tasks — are impacting all parts of the retail chain, from the manufacturing of goods all the way to their distribution.

We looked at the different stages in bringing a product to market, from manufacturing to delivery, and how companies are using AI-enabled automation — including facial recognition, demand forecasting, and computer vision-based robots — to enhance each of these stages.

In 2018, Tommy Hilfiger announced that it was working with IBM and the Fashion Institute of Technology on a project called “Reimagine Retail,” the goal of which was to use artificial intelligence to help Tommy Hilfiger better understand customer sentiment around its products and design better patterns, silhouettes, colors, and styles.

Researchers from FIT used IBM technology to analyze 15,000 of Tommy Hilfiger’s product images, alongside 600,000 runway images and almost 100,000 images of different generic patterns and clothing fabrics.

It also may give them the ability to design elements that will customize and personalize looks for certain markets or consumers.” — Steve Laughlin, General Manager of IBM Global Consumer Industries That corpus of information was then used to develop new styles, rooted in Hilfiger’s style but incorporating trending patterns and colors, for Hilfiger’s design team.

At L’Oreal retail locations, the company allows customers to virtually try on different kinds of makeup using both mobile apps and an interactive smart mirror technology developed by Alibaba.

The technology gives customers the ability to sample a range of products before they buy, and supplies L’Oreal with data they can use to better match a user’s facial features and appearance to their products.

We will produce 800,000 T-shirts a day for Adidas… Around the world, even the cheapest labor market can’t compete with us.” — Tang Xinhong, chairman of Tianyuan Garments, ChinaDaily Most of the heavy lifting will be done by the AI-driven robots, with human workers taking over jobs around robot maintenance and operation.

In fact, a 2012 DARPA contract awarded to SoftWear Automation states, “complete production facilities that produce garments with zero direct labor is the ultimate goal.” Moving textile production facilities from China to the US means moving them closer both to the source of cotton, a crucial production resource, and to the American market, where many of the clothes will eventually be sold.

Adidas’ internal goal is to build 50% of their shoes using automation, though for now, it forecasts the total production of these two Speedfactories to amount to about 0.3% of its total worldwide sneaker production.

“With 30% fewer steps and up to 50% less labor, we can produce a complete pair of uppers in just 30 seconds at scale with less waste.” — Eric Sprunk, COO at Nike Grabit has been secretive about its client base: a May 2017 press release only reveals that its material-handling robots are being shipped to a Fortune 100 “industry leading athletic shoe and apparel company.”

The road to automation passes through warehouses and factories where robots collaborate with humans. As more people shop for products online, there is greater pressure on order fulfillment centers to ship items on time.

We’re investing across all of these sectors, automation and robotics, data science and AI, big data and the cloud, and the Internet of Things.” — Time Steiner, Ocado CEO, Q2’17 earnings call In 2002, Ocado opened its first customer fulfillment center, which is “equivalent to 11 football pitches in size and stands 20 meters tall.” Since then, it has opened a second and third, each time adding to its technological capabilities and warehouse capacity.

search on the CB Insights platform for Ocado patents filed in the United States shows the types of warehouse automation technologies the company has been working on, from parcel sorters to robotic object handling to automated bag handling.

Ocado partnered with France-based grocery giant Groupe Casino in 2017 to construct a “latest generation, state‐of‐the‐art automated warehouse” for Groupe Casino, and on the software side provide solutions like a front-end web interface and last-mile routing.

Later in 2018, Ocado announced a partnership with Kroger to build 20 customer fulfillment centers for the North American supermarket giant over 3 years, with the first set planned for Ohio, central Florida, and the mid-Atlantic region.

As part of the deal, Kroger invested $247M into Ocado.  Startups like Instacart can help retailers offer fast, on-demand delivery to their customers, but it often comes at the expense of the customer’s relationship with the store.

In our 2018 analysis of 1,600+ earnings call transcripts from 50+ publicly-traded US retailers, only 9 retail companies had mentioned AI-related strategies for their websites or physical stores.

At the time, eBay had just begun to make it compulsory for sellers to write product descriptions, and was using machine learning to process that data to find similar products in the catalog.

In a March 2019 blog post, eBay’s Chief Architect Sanjeev Katariya talked about how eBay uses AI to better understand user and shopper intent, permit cross-border trade, and help sellers create product listings faster using natural language processing together with pattern matching.

Russian e-commerce retail giant Lamoda, for example, reportedly separates its visitors into 160 geographic segments, and recommends products based on the local weather in its banner ads., the second largest e-commerce platform in China, runs a collection of more than 20 cashierless convenience stores that also use RFID tags to keep track of merchandise and prevent theft.

In April 2019, Walmart announced that it would roll out autonomous shelf-scanning bots (designed to identify out of stock items) in at least 300 stores through the rest of the year, with 1,500 stores automatic receiving floor cleaning robots.

According to Walmart, a single robot can cut several hours of work off a human’s workload, allowing the company to allocate fewer employees to tasks like maintenance and checking stock.

But the sheer scale and complex networks of people involved in transporting goods — from freight forwarders and freight operators to retailers and warehouse owners — makes supply chain visibility a challenge.

Startups like ClearMetal are attempting to use machine learning to improve transportation visibility. The company is developing a predictive intelligence platform that collects data from shipment carriers, as well as aggregating data points like real-time weather and currency fluctuations, to help predict shipping events, shipment times, and shipping demands.

Amazon demonstrated the newest model of its fully electric delivery drone at re:Mars in June 2019, with the company claiming that it would begin making thirty minute residential deliveries in certain markets within the coming months.

In some ways, Alibaba is ahead of Amazon in its online and offline integration using AI. It relies on technology — like smart stores, deep learning, and AR/VR — as well new business models to bridge the online and offline divide in China.

In its efforts to bridge brick-and-mortar with online commerce and improve the overall retail experience for consumers, Alibaba has made it clear that the future of its retail ambitions is omnichannel — a cross-channel approach that fuses the physical and digital shopping experience.

Retailers may increasingly compete with each other — and with tech companies working in other industries — for AI startups and talent, as artificial intelligence continues to spread across the retail ecosystem.

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