Grocery shopping has fundamentally, likely irrevocably, changed during the pandemic as more consumers have opted for online grocery shopping out of convenience or necessity. But what about people who rely on food stamps? According to a recent Pew survey, a full quarter of adults have had trouble paying bills during the economic melee attributable to the pandemic. As of July 2020, over 40 million Americans were on food stamps, officially known as the Supplemental Nutrition Assistance Program (SNAP). Food stamps exist to help low income individuals, including those enduring a temporary hardship, bridge a crucial financial gap to access food.
Slot-filling refers to the task of annotating individual terms in a query with the corresponding intended product characteristics (product type, brand, gender, size, color, etc.). These characteristics can then be used by a search engine to return results that better match the query's product intent. Traditional methods for slot-filling require the availability of training data with ground truth slot-annotation information. However, generating such labeled data, especially in e-commerce is expensive and time-consuming because the number of slots increases as new products are added. In this paper, we present distant-supervised probabilistic generative models, that require no manual annotation. The proposed approaches leverage the readily available historical query logs and the purchases that these queries led to, and also exploit co-occurrence information among the slots in order to identify intended product characteristics. We evaluate our approaches by considering how they affect retrieval performance, as well as how well they classify the slots. In terms of retrieval, our approaches achieve better ranking performance (up to 156%) over Okapi BM25. Moreover, our approach that leverages co-occurrence information leads to better performance than the one that does not on both the retrieval and slot classification tasks.
Machine Learning aids e-commerce to foil attempts at payment fraud, as they happen. Long before the pandemic led to an avalanche of online shopping, e-commerce had become a way of life for many Americans, especially Millennials and Gen Zers. In fact, 60% of Millennials bought online in 2019, while 24% Gen Zers strongly prefer to purchase online and 13% through mobile. This has led to variety of online shopping choices, including e-shops, online banking, online insurance and other online services. As Hil Davis, Co-founder of the online men's retailer, said, "E-commerce and mobile commerce have dramatically changed the way brands reach customers, making it faster and easier for consumers to make purchases on the fly while avoiding the hassles of going to the store."
Retail will be transformed by emerging tech. The pandemic has left irreversible changes within the retail industry as consumer behavior during a time of limited and regulated movement evolved. A PYMNTS 2020 Remote Payments Study reported that mobile devices are the most popular device for online shopping, with up to 72% of consumers using their mobile devices to shop. The explosion of online shopping saw an increase of 146% in online retail orders as of April 21, 2020 when compared to the same period last year. While e-commerce's growth has accelerated by "four to six years" according to a recent report published by Adobe indicated that on the ground retailers are now embracing digital-first approaches in order to acclimate to the new norms of social distancing and minimized contact.
With the continuing shift to digital, especially in the retail industry, ensuring a highly personalized shopping experience for online customers is crucial for establishing customer loyalty. In particular, product recommendations are an effective way to personalize the customer experience as they help customers discover products that match their tastes and preferences. Google has spent years delivering high-quality recommendations across our flagship products like YouTube and Google Search. Recommendations AI draws on that rich experience to give organizations a way to deliver highly personalized product recommendations to their customers at scale. Today, we are pleased to announce that Recommendations AI is now publicly available to all customers in beta.
Commerce has evolved to eCommerce, and the'e' stands for more than'electronic'. It's more about'easy' now – easier to find products, compare across brands, purchase them and get them delivered on time. An important factor that helps make informed purchase decisions with ease, is to have complete product information on the product page. But, ever wondered what happens behind the scenes to create this seamless and'easy' experience? Imagine if a retailer needs to sell a simple, black office chair. How would the retailer describe that black office chair?
Applitools, provider of a next generation test automation platform powered by Visual AI and Ultrafast Test Cloud, announced the Applitools Holiday Shopping Hackathon. The holiday themed contest provides developers, quality assurance (QA) professionals, and test automation engineers with a fun, real-world scenario that shows how next generation test automation cloud and Visual AI can help online retailers deliver perfect apps to make the most of the holiday sales season. Each participant competes to win prizes as they spend roughly two hours to test the functional and visual quality of the "Applifashion" retail app and make sure it is impeccable ahead of the busy online holiday shopping season. Holiday e-commerce sales totaled $167.8 billion in 2019 according to the National Retail Federation. This year, 96% of retailers expect online holiday sales to increase.*
We hear all around us that the future is digital. However, what exactly does the future hold in terms of eCommerce? This article covers the top ten trends that will be changing the future of eCommerce. As a digital marketer, it is important to be well-versed with these trends. Let's dig in and find out!
"Since the coronavirus outbreak, online retailers like Wayfair, Etsy Inc. and Pinterest Inc. are ratcheting up efforts to leverage data from a surge in e-commerce to get better at helping customers find what they are looking for--even when they don't know what that is," a Wall Street Journal article noted. "To do that, these Web-only stores are supercharging search-and-recommendation engines by feeding data into sophisticated algorithms, building predictive models with a level of accuracy unimaginable just a few years ago." "Not all of the capabilities are new--algorithms have been around for decades," the article added. "But the rapid expansion of computing power and cloud storage in recent years has enabled sellers to gather and crunch data on a massive scale. Shoppers generate data on retail websites every time they place an item in a virtual cart, hover over product pages, click on product recommendations and ultimately make a purchase. Stores create more-robust customer profiles by adding their shoppers' ages and genders, where they live, the local weather or seasonal events and holidays--and in some cases data drawn from all over the internet by third-party services."
Many of us have already encountered artificial intelligence (AI) in product searches, online shopping processes, and out-of-home advertising. It seems to offer unlimited potential, from AI-powered automated content to predictive targeting and personalized product recommendations in real-time. AI's capability to collect and analyses vast quantities of data in record time is also a big advantage in marketing. AI is already providing guidance at many touchpoints along the customer journey with personalized content, product recommendations, and dialogue with virtual assistants such as Hallo Magenta, Amazon Alexa, or the Google Assistant. The latter uses RankBrain, a machine learning-based search engine algorithm, to increase search result precision.