Over 100 marketing leaders from the most prominent retail brands in the world weighed in on consumer engagement strategies in the 2017 study, "Building Lasting Consumer Relationships in Retail," released by Persado and WBR Digital. The survey was conducted via in-depth interviews with CMOs and other marketing leadership at US- and UK-based major retailers including Best Buy, Marks and Spencer, Williams-Sonoma, Urban Outfitters, and Nordstrom, amongst many others. The survey reveals that despite retailers' efforts to provide relevant content and experiences, they continue to fall short on the personalization front. How these companies are personalizing might be standing in the way of better communication. According to the study, marketers are overly relying on demographic and geographic data, while significantly underutilizing behavioral data, lifecycle stages, and psychographic indicators like emotional triggers.

Recommender systems (RS) help users navigate large sets of items in the search for "interesting" ones. One approach to RS is Collaborative Filtering (CF), which is based on the idea that similar users are interested in similar items. Most model-based approaches to CF seek to train a machine-learning/data-mining model based on sparse data; the model is then used to provide recommendations. While most of the proposed approaches are effective for small-size situations, the combinatorial nature of the problem makes it impractical for medium-to-large instances. In this work we present a novel approach to CF that works by training a Denoising Auto-Encoder (DAE) on corrupted baskets, i.e., baskets from which one or more items have been removed. The DAE is then forced to learn to reconstruct the original basket given its corrupted input. Due to recent advancements in optimization and other technologies for training neural-network models (such as DAE), the proposed method results in a scalable and practical approach to CF. The contribution of this work is twofold: (1) to identify missing items in observed baskets and, thus, directly providing a CF model; and, (2) to construct a generative model of baskets which may be used, for instance, in simulation analysis or as part of a more complex analytical method.

While many studies explore the distant future of AI, this article will focus on the exciting developments that we can see taking place in the online economy right now, and what we can expect to see in the next five years. AI is already changing how our work is done; reinforcing the role of people as drivers of business growth, while also improving efficiency through automation. Let's explore how AI is not only contributing to the growth of the online industry but also how it drastically improves the online shopping experience of customers. Check out a previous article on the economics of augmented reality for more reading on recent tech developments. Before we look at the here and now, let's consider where this path of AI may eventually lead us to.

News stories abound about physical retail stores closing across the United States. Yet, according to data the Commerce Department released earlier this year, online sales only accounted for 8.1 percent of total retail sales in 2016, meaning shoppers still spend the vast majority of their money in stores. And those stores could be getting a jolt, thanks to augmented reality. Augmented reality -- digital information brought into a user's field of view and overlaid onto the real world, which they usually observe through a smartphone or tablet's camera -- has the potential to reshape the retail environment. Not only can AR let customers see digital representations of products before they commit to a purchase, but it also can remove products from store shelves, highlight certain merchandise and provide more information about it.

Walmart took another shot at Amazon when it revealed its "Scan & Go" technology, which allows shoppers to pay for products in stores by scanning them on their smartphones. The service, which is currently being tested in some locations, directly competes with Amazon Go, a store without checkout lines. Walmart had rolled out its Scan & Go technology a few years ago, but it failed to catch on. However, new data from the 2017 Walker Sands Future of Retail Report suggests it could be more popular with consumers this time around. The annual report, based on responses from more than 1,600 U.S. consumers, found 60 percent of Americans are open to shopping in checkout-less stores.

Then, there's what Tang calls the "reverse-engineered" version of the Markable technology. When a visitor clicks on a piece of clothing on AKIRA's shop, they'll be able to see if it has ever been modeled by a celebrity or fashion blogger. Click on a shirt, and you may see a photo of when that same shirt was previously worn by Taylor Swift. Shoppers then have the option of "completing the look" by buying the rest of T-Swift's ensemble.

The steady decline in business profitability across the retail industry threatens to erode future investment, innovation and shareholder value. Fortunately, the age of AI-powered retail is upon us, promising an unprecedented amount of data and information that, if used in the right way, can help retailers grow their businesses and at the same time revolutionize the customer experience. Thanks to Amazon's Alexa and Google Home, the technology is becoming more pervasive. It's changing how we shop, how we experience brands and how we live our everyday lives. As AI becomes normal for today's consumers, it's hard to believe that, as recently as the turn of the Millennium, the technology was mostly restricted to science fiction and the horizon-scans of the most forward-thinking companies.

Here's this week's newsletter: Last week, VentureBeat invited Amazon, Facebook, Google, IBM, and other giants of AI into a big tent with brands like Coca-Cola, The New York Times, Tumi, and Walmart, as well as such promising startups as Bark.us, The gathering was MB 2017, and the need for practical AI was on nearly everyone's mind. Walmart, for instance, is using machine learning to better serve its 140 million weekly shoppers and to make new services possible. Laurent Desegur, vice president of customer experience engineering at WalmartLabs, explained the role of data science to make possible so-called Pick-up Towers within stores, which allow customers to order and pay online for items and then retrieve them, skipping the checkout lines. Desegur also described a 20-store pilot of Scan and Go shopping, a self-serve experience.

With a forecast 80 billion connected devices by 2025, the Internet of Things (IoT) is set to transform the way business operates. From AI to VR and the IoT, technology is already having a marked impact on the way businesses maximise profit, improve efficiency and operate. Virtual reality is set to change the way we train our workforces. It enables trainers to deliver learning in an interesting and interactive way. They can present large amounts of complex information visually, to aid learning and help students retain information.

Machine learning and AI are expected to be big with retailers in the coming year, as more than 86% of CMO's say they are planning to invest in it in 2017, according to a study by Persado that was emailed to Retail Dive. Almost half (47%) of respondents said they plan to invest up to $50 million in the technology, while a quarter plan to spend over $100 million on it. Effective customer engagement topped the list of benefits that marketers hope to receive from an investment in AI, where engagement "is considered foremost as a driver of revenue (37%) and to a much lesser extent, brand awareness (13%)," the report states. Despite the possibility of using technology to drive personalization, only 22% of those surveyed felt skilled at emotionally engaging customers through their content, something the researchers believe might be a result of data collection that is limited to demographic and location data. There's no question that AI is a growing field for retailers.