He credits the gains to advances in smart software. Rather than asking customers to browse through the entire catalog of mugs, he says, algorithms, artificial intelligence and troves of data "are doing the work behind the scenes." Since the coronavirus outbreak, online retailers like Wayfair, Etsy Inc. ETSY 3.99% and Pinterest Inc. PINS -0.97% 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. 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.
Amazon Kendra is a highly accurate and easy-to-use enterprise search service powered by machine learning (ML). As your users begin to perform searches using Amazon Kendra, you can fine-tune which search results they receive. For example, you might want to prioritize results from certain data sources that are more actively curated and therefore more authoritative. Or if your users frequently search for documents like quarterly reports, you may want to display the more recent quarterly reports first. Relevance tuning allows you to change how Amazon Kendra processes the importance of certain fields or attributes in search results.
Amazon Kendra is a highly accurate and easy-to-use enterprise search service powered by machine learning (ML). As your users begin to perform searches using Kendra, you can fine-tune which search results they receive. For example, you might want to prioritize results from certain data sources that are more actively curated and therefore more authoritative. Or if your users frequently search for documents like quarterly reports, you may want to display the more recent quarterly reports first. Relevance tuning allows you to change how Amazon Kendra processes the importance of certain fields or attributes in search results.
Being an early adopter of artificial intelligence and automation, Amazon always had an edge in using AI to improve its business efficiencies. Not only has it been using AI to enhance its customer experience but has been heavily focused internally. From using AI to predict the number of customers willing to buy a new product to running a cashier-less grocery store, Amazon's AI capabilities are designed to provide customised recommendations to its customers. According to a report, Amazon's recommendation engine is driving 35% of its total sales. One of the main areas where Amazon is applying continuous AI is to better understand their customer search queries and what is the reason they are looking for a particular product.
Google has released new behavioural insights into 2019 holiday shoppers, with tips on how to sell to US consumers. According to Google's data, mobile searches for "best deals" have grown by 90%. So it should come as no surprise that the #1 factor when consumers decide where to buy is whichever retailer has the lowest price. Consumers also appreciate being able to do what they want all on their own. It's also interesting to note that searches around "rewards apps" and "Black Friday deals" are up 200% this year.
Amazon.com Inc. has adjusted its product-search system to more prominently feature listings that are more profitable for the company, said people who worked on the project--a move, contested internally, that could favor Amazon's own brands. Late last year, these people said, Amazon optimized the secret algorithm that ranks listings so that instead of showing customers mainly the most-relevant and best-selling listings when they search--as it had for more than a decade--the site also gives a boost to items that are more profitable...
While many of us enjoy longer vacations and sunshine during the summer, major retailers spend the warmer months getting ready for the biggest sales events of the year. In fact, many retailers will report more than 50 percent of their annual profits from a single sales event during the coming fall months. In 2016, Alibaba's Singles Day grossed $17.8 billion in 24 hours, and that figure rose to $25.3 billion in 2017: In the United States, Black Friday and Cyber Monday 2016 sales combined brought in $6.79 billion: These events kick off with Labor Day sales (back to school), which is just around the corner, and end with the holiday shopping season. With online merchandise selling out in seconds and competitive price wars getting exceedingly high, it's no wonder retailers utilize summer months to prepare for eager shoppers. While retailers are focusing on campaigns, commercials, margins and inventory, what can a search engine optimization specialist (SEO) do to help a client's bottom line?
Maria Johnsen holds a degree in political economy from Kharkov University in Ukraine, Beauty Arts from Sorbonne University in Paris, BA in Information technology,BA in computer science and a Master of Science degree in computer engineering from university of science and technology in Norway and master degree in filmmaking and television from Royal Holloway University of London. Her professional background and education is diverse and includes skills in areas such as sales, multilingual digital marketing, content writing, business intelligence, software design and development. In addition, she possesses the experience and education in the management of complex Information Systems. Maria knows eighteen languages and possesses experience in language instruction, tutoring, and translation. She has also developed a unique teaching method for fast learning "Implications for Upgrading Accelerated Learning Practices In Educational Systems" This method is applied in China and Norway.
Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Such optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisions. Here we focus on multivariate optimization of interactive web pages. We formulate an approach where the possible interactions between different components of the page are modeled explicitly. We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. Our algorithm also extends to contextualization and personalization of layout selection. Simulation results show the suitability of our approach to large decision spaces with strong interactions between content. We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. After only a single week of online optimization, we saw a 21% conversion increase compared to the median layout. Our technique is currently being deployed to optimize content across several locations at Amazon.com.