My situation as an online shopper is somewhat like this, "I know what I need but not sure where to buy em…" Over and above, most of the online shoppers experience a very similar perplexity. And I know how much pain purchasing a set of socks can cause if you are not a brand maniac. Continuing to how web personalisation can heal the aforementioned agony, it is the process where you customize your website to the needs of a specific user (This will curtail the online shopping fatigue). By using the insights you gain from analyzing user navigational behavior on your website. In correlation with other info accumulated in the form of structure, content and user profile data. There are two ways to collecting data on a user's interest and activities on a website.

Technology has long since advanced to the point where it can make decisions better than people can, and yet grocery managers are still happy to use their own experience to drive decision-making, sacrificing speed, efficiency and savings. By optimising key strategic areas of pricing and replenishment, and automating decisions using machine learning, retailers can combine the speed of their decisions with their KPIs (margins, volumes, mark downs). Yet still retailers are not currently marrying the two in a responsive and effective way. As part of Blue Yonder's recent survey of 750 grocery retailers across the globe, we asked some probing questions about decision making and customer service. The research revealed that grocery retailers believe robotics, machine learning and artificial intelligence will be some of the key game changers for the industry.

Machine-learning is all the rage in fraud detection, with industry analysts, academics, businesses and technology media examining the advantages of algorithms and big data in the fight against e-commerce fraud. Especially for fraud analysts working in companies with small budgets, machine-learning tools are seen as a cost-effective way to tighten fraud controls while maintaining fast decision times, as Forrester noted in its 2015 cross-channel fraud report. There's no question that machine-learning tools can be an effective component of fraud reduction program, but relying on them to save staffing costs may not be cost-effective in the long run. That's because while machine learning is an invaluable tool in the fight against fraud, it relies on human input and insight to create a comprehensive solution that yields the best results. Algorithms are useful for identifying potential fraud quickly, but due to variability in consumer behavior – such as making online purchases while traveling abroad -- some transactions will be falsely flagged for decline.

If the basics of retail are elementary, then it should be no surprise that a technology named Watson is leading what may be one of the biggest trends in 2017. Watson is the name of an artificial intelligence technology (AI) by IBM; many may remember Watson for its $1 million winning streak on "Jeopardy." Today, several major retailers -- from Macy's to 1-800-Flowers.Com -- are using or testing the supercomputer's cognitive computing capabilities to more acutely predict (and serve) customer wishes. Most recently, Staples announced plans to implement Watson technology to bring to life its Easy Button. Infused with the technology, the button can now take Staples orders by voice, text, email, messaging app or mobile app.

From Alexa to Amazon Go, we use deep learning extensively across all areas of Amazon, and we've tried a lot of deep learning engines along the way. One has emerged as the most scalable, efficient way to perform deep learning, and for these reasons, we have selected MXNet as our engine of choice at Amazon. MXNet is an open source, state of the art deep learning engine, which allows developers to build sophisticated, custom artificial intelligence systems. Training these systems is significantly faster in MXNet, due to its scale and performance. For example, for the popular image recognition network, Resnet, MXNet has 2X the throughput compared to other engines, letting you train equivalent models in half the time.

If the basics of retail are elementary, then it should be no surprise that a technology named Watson is leading what may be one of the biggest trends in 2017. Watson is the name of an artificial intelligence technology (AI) by IBM; many may remember Watson for its $1 million winning streak on "Jeopardy." Today, several major retailers -- from Macy's to 1-800-Flowers.Com -- are using or testing the supercomputer's cognitive computing capabilities to more acutely predict (and serve) customer wishes. Most recently, Staples announced plans to implement Watson technology to bring to life its Easy Button. Infused with the technology, the button can now take Staples orders by voice, text, email, messaging app or mobile app.

Shopping in the future may feel a lot like shoplifting does today -- without the risk of getting nabbed -- if two artificial intelligence startups have their way. New Zealand's IMAGR and Silicon Valley's Mashgin aim to make checking out of grocery stores and company cafeterias a walk in the park. Many supermarkets offer self-checkout to save shoppers time. IMAGR founder William Chomley wants to skip the checkout altogether, so you can just walk right out the door. It's similar to the idea behind Amazon Go, being tested in a grocery store in downtown Seattle, which lets customers shop without ever stopping at a cashier on the way out.

Holden Karau is transgender Canadian, and anactive open source contributor. When not in San Francisco working as asoftware development engineer at IBM's Spark Technology Center, Holdentalks internationally on Spark and holds office hours at coffee shops athome and abroad. She makes frequent contributions to Spark, specializing inPySpark and Machine Learning. Prior to IBM she worked on a variety ofdistributed, search, and classification problems at Alpine, Databricks,Google, Foursquare, and Amazon. She graduated from the University ofWaterloo with a Bachelor of Mathematics in Computer Science.

Most people don't buy a jar of relish every week. But when they decide to buy one from Ocado--the world's largest online-only grocery retailer--they don't have to scrabble at the back of the store. Instead, they call on robots and artificial intelligence to have it delivered to their door. Ocado claims that its 350,000-square-foot warehouse in Dordon, near the U.K.'s second city of Birmingham, is more heavily automated than Amazon's warehouse facilities. The company's task is certainly more challenging in many respects: most of the 48,000 lines of goods that it sells are perishable, and many must be chilled or frozen.

Retailers have to refocus their marketing from driving traffic to stores to closing sales and managing loyalty, according to insiders at the National Retail Federation's 2017 Big Show, earlier this week in New York City. "We're only as good as the last transaction," said Greg Foran, CEO of Walmart U.S. E-commerce is now setting the bar for the in-store experience, and that, in turn, is changing expectations for the customer experience and technology deployed in stores, speakers said. Retail fundamentals will always be required, but merchants also need to master technological skills, said Bill Brand, president of direct retailer HSNi. "Technology is changing the way we shop [and] the way we enjoy products. Even the definition of what a store is changing " said Brian Krzanich, CEO of Intel.