WooCommerce is a powerful e-commerce plugin for WordPress, designed to turn your website into a fully functional online store. It’s the most popular e-commerce solution for WordPress, trusted by millions of users worldwide. Here’s a comprehensive explanation of WooCommerce:

1. Installation and Setup: WooCommerce is a free plugin that you can install directly from the WordPress plugin repository. Once activated, it guides you through a setup wizard to configure essential settings such as currency, payment gateways, shipping options, and tax settings.
2. Product Management: WooCommerce allows you to sell physical products, digital downloads, memberships, subscriptions, and even bookings. You can easily add, edit, and manage products through the WordPress dashboard. Each product can have attributes such as size, color, and price variations.
3. Storefront Customization: WooCommerce seamlessly integrates with WordPress themes, allowing you to customize the appearance of your online store. You can choose from a wide range of free and premium WooCommerce-compatible themes or use custom CSS to modify the design according to your branding.
4. Shopping Cart and Checkout: WooCommerce provides a user-friendly shopping cart system that allows customers to add products, view their cart, and proceed to checkout effortlessly. It supports various payment gateways, including PayPal, Stripe, Square, and more, enabling secure transactions. The checkout process is customizable and can be optimized for better conversion rates.
5. Order Management: WooCommerce offers robust order management features, allowing you to view, process, and fulfill orders from the WordPress dashboard. You can track order statuses, send order notifications to customers, print packing slips, and manage refunds or cancellations efficiently.
6. Shipping and Tax Calculations: With WooCommerce, you can set up shipping methods based on weight, location, or flat rates. It also integrates with popular shipping carriers like USPS, UPS, and FedEx for real-time shipping rates. Additionally, WooCommerce handles tax calculations based on your store location and customer’s location, ensuring compliance with tax regulations.
7. Extensions and Add-ons: WooCommerce provides a wide range of extensions and add-ons to enhance the functionality of your online store. These include additional payment gateways, shipping methods, product bundles, subscriptions, memberships, marketing tools, and more. Many of these extensions are developed by WooCommerce itself or third-party developers within the WooCommerce ecosystem.
8. Reporting and Analytics: WooCommerce offers built-in reporting tools to track your store’s performance. You can monitor sales, revenue, top-selling products, customer behavior, and other key metrics using intuitive dashboards and reports. Additionally, you can integrate WooCommerce with Google Analytics for more advanced insights.
9. Security and Scalability: WooCommerce prioritizes security and follows best practices to keep your online store safe from threats. It regularly releases updates to address vulnerabilities and ensures compatibility with the latest WordPress versions. Moreover, WooCommerce is highly scalable, allowing your store to grow as your business expands, without compromising performance.

Overall, WooCommerce provides a comprehensive solution for creating and managing an e-commerce website on WordPress. Whether you’re a small business owner, a large retailer, or a digital entrepreneur, WooCommerce offers the flexibility and functionality to build a successful online store.

The post Mastering E-Commerce: A Comprehensive Guide to WooCommerce for WordPress Websites appeared first on Artificial Intelligence.

Conversational AI is Advancing towards the Sector of Education

May be ‘Talking tom’ was just not a toy but also an epitome of Conversational AI that is reigning the present and will continue to do so in future as well. Besides, the pandemic happens to be a significant inducer of artificial intelligence to such an extent that AI is now a habit and a lifestyle. In the current era of technological modernization, enterprises are augmented with digital transformation. This stands true especially for Fortune 500 companies that are leveraging AI to best of their advantages to enhance and refurbish their business dimensions.

Conversational AI is one of the most advanced forms of AI bots that are heavily employed by e-commerce platforms. While Conversational AI has occupied a significant space in the e-commerce industry, it is also transforming the education sector as well.

The Socio-economic Status of Education Sector during Covid-19

With social distance as the norm, education and pedagogy have become mobile, in its entirety. Education institutions are seeking novel methods to disseminate extensive teaching methods without any loop holes. AI has been a significant and constant filler of gaps that often arise in research and education, thereby, delivering personalized and impeccable standards of education.

According to reports by World Economic Forum, the global education technology investment had touched a high growth of US$18.66 billion. The education sector is thriving by adoption of high-end technologies, eliminating all the possible drawbacks entailed by online education. Hence, it can be confidently asserted that the socio-economic status of education amidst the pandemic has remained unhindered.

Incorporation of Conversational AI in Education

Conversational AI has emerged to be an inseparable limb of modern day teaching and learning methods. This innovative AI tool stimulates smooth communication between users and computers by enabling natural dialogue. Natural dialogue is one of the cardinal requisite in virtual education. With the support of natural language processing (NLP), Conversational AI aims to attend and clarify queries from students and deliver human-like responses in short periods of time.

While conversing with AI chatbots can seem intimidating at first, Conversational AI performs like humans to meet the needs of students. Here is a list of multiple benefits generated by Conversational AI:

1. Personalized Learning Experience: The demands and requirements of teaching and learning keeps varying. Conversational AI is capable of providing customized and personalized learning that suits an individual the best.

2. Automated Administrative Works: Administrative works are crucial parts of the education sector. While handling administrative tasks can be overwhelming at times, the onus can be put on Conversational AI that automates all the administrative works.

3. Authentic Delivery of Feedbacks: Feedback is imperative for both faculties and students. Conversational AI organizes the right feedback for students and teachers. Feedback paves ways for opportunities to fill up the gaps in the process of teaching and learning.

Conversational AI is the New Pygmalion of the Education Sector

Integration of conversational AI in the education has demonstrated results that have helped to transform the face of education sector, making virtual education a part of the new normal.

The post FROM E-COMMERCE TO EDUCATION: CONVERSATIONAL AI IS ONE SIZE FITS ALL appeared first on Artificial Intelligence.

The key focus of Big Data in E-commerce market report is to evaluate the performance of the industry in the ensuing years to help stakeholders take better decisions and expand their business portfolio. The document highlights the key growth trends as well as the opportunities and how they can be exploited to generate maximum profits. In addition, it empowers industry partakers with methodologies that can be adopted to effectively deal with the existing and upcoming challenges. Besides, it gauges the impact of COVID-19 on this business sphere and attempts to monitor its future implications on the market scenario for a stronger realization of the growth prospects.

Key pointers from COVID-19 impact assessment:

• Socio-economic impact of COVID-19 on the global economy and Big Data in E-commerce market.
• Shifts in supply and demand share.
• Predicted long-term COVID-19 outlook on the growth of the industry.

Summary of the regional analysis:

• Geographically, the Big Data in E-commerce market is split into North America, Europe, Asia-Pacific, South America, Middle East & Africa, South East Asia.
• Contribution of each geography to the overall growth in given in the report.
• Growth rate, revenue, and sales of each regional market are discussed extensively.

Other crucial pointers from the Big Data in E-commerce market report:

• The Big Data in E-commerce market, based on the product terrain, is categorized into Cloud-based andOn-premises.
• Information regarding the estimated revenue and volume share of ever product type is documented.
• Data pertaining to growth rate, market share, and production pattern of each product category over the forecast timespan is given as well.
• The report segments the application spectrum of the Big Data in E-commerce market into Online Classifieds,Online Education,Online Financials,Online Retail,Online Travel and Leisure andOthers.
• Each application segment’s market share and predicted growth rate are thoroughly discussed.
• Leading organizations influencing the market dynamics are Dell Inc,Teradata Corp,Data Inc,Facebook,Hewlett Packard Enterprise (Hpe),Palantir Technologies, Inc,Hitachi, Ltd,Twitter,SAP Se,Whatsapp,IBM Corp,Oracle Corp,Microsoft Corp,Splunk Inc,Amazon Web Services, Inc andSAS Institute Inc.
• The study examines the mentioned firms with respect to their market share, gross margins, market remuneration, pricing pattern, production capacity, and product & service portfolio.
• The document elaborates the prevailing competition trends and their implications on businesses.
• A granular analysis of supply chain, including details about providers, consumers, as well as manufacturers is encompassed in the study.
• Moreover, the study determines the investment viability of a new project through several practices such as Porter’s Five Forces analysis and SWOT assessment.

Key features of the report:

• Intricate details of each organization.
• Information regarding market share, product sale price, manufacturing base distribution, total revenue generated, and sales.
• Latest developments of the leading players.
• Sales amassed by each company with respect to their operational areas.

Highlights of the Report:

• Accurate market size and CAGR forecasts for the period 2020-2025
• Identification and in-depth assessment of growth opportunities in key segments and regions
• Detailed company profiling of top players of the global Big Data in E-commerce market
• Exhaustive research on innovation and other trends of the global Big Data in E-commerce market
• Reliable industry value chain and supply chain analysis
• Comprehensive analysis of important growth drivers, restraints, challenges, and growth prospects

The scope of the Report:

The report offers a complete company profiling of leading players competing in the global  Big Data in E-commerce market with a high focus on the share, gross margin, net profit, sales, product portfolio, new applications, recent developments, and several other factors. It also throws light on the vendor landscape to help players become aware of future competitive changes in the global Big Data in E-commerce market.

Reasons to Buy the Report:

• Upgrade your market research resources with this comprehensive and accurate report on the global Big Data in E-commerce market
• Get a complete understanding of general market scenarios and future market situations to prepare for rising above the challenges and ensuring strong growth
• The report offers in-depth research and various tendencies of the global Big Data in E-commerce market
• It provides a detailed analysis of changing market trends, current and future technologies used, and various strategies adopted by leading players of the global Big Data in E-commerce market
• It offers recommendations and advice for new entrants the global Big Data in E-commerce market and carefully guides established players for further market growth
• Apart from the hottest technological advances in the global Big Data in E-commerce market, it brings to light the plans of dominant players in the industry

Table of Contents:

Industry Overview of Big Data in E-commerce Market

Industry Chain Analysis of Big Data in E-commerce Market

Manufacturing Technology of Big Data in E-commerce Market

Major Manufacturers Analysis of Big Data in E-commerce Market

Global Productions, Revenue and Price Analysis of Big Data in E-commerce Market by Regions, Manufacturers, Types, and Applications

Consumption Volumes, Consumption Value, Import, Export and Sale Price Analysis of Big Data in E-commerce by Regions

Gross and Gross Margin Analysis of Big Data in E-commerce Market

Marketing Traders or Distributor Analysis of Big Data in E-commerce Market

Global and Chinese Economic Impacts on Big Data in E-commerce Industry

Development Trend Analysis of Big Data in E-commerce Market

Contact information of Big Data in E-commerce Market

New Project Investment Feasibility Analysis of Big Data in E-commerce Market

Conclusion of the Global Big Data in E-commerce Market Industry 2020 Market Research Report

The post Big Data in E-commerce Market Size, Share 2020 By Development, Trend, Key Manufacturers appeared first on Artificial Intelligence.

A decade ago, when everyone was still wrapping their heads around the idea of cloud computing, one ongoing point of concern was the fear of vendor lock-in. The idea was that if you relied on a single company to provide all of your cloud services from top to bottom, you would be locked in and lack the flexibility to try new services from other vendors until your contract expired — even if another company offered a better product. If you wanted to add other services, you’d be on the hook for substantial expenses to migrate.

Eventually, the tech industry listened and learned that customers don’t want a one-size-fits-all approach; they want the ability to pick and choose different pieces and services depending on their specific needs. Unfortunately, some e-commerce platform vendors didn’t get the memo because they’re still operating as monoliths.

While some e-commerce platforms have learned the value of flexibility, for others, the pendulum has swung in the opposite direction in favor of microservices, an architecture composed of loosely coupled services connected through APIs to create a complete system. Think of microservices like Lego blocks without an instruction manual to piece them together to build that creation you had perfectly envisioned.

A microservices approach can be tempting for online retailers for several reasons. First, commerce is becoming increasingly focused on content and customer experience. By decoupling the front end (the part customers interact with) from the back end (the part that enables transactions and powers the store), developers can customize and manage the two sides separately.

Second, merchants can have multiple front-end experiences connected to one back end, creating opportunities to implement several new customer-facing touch points (e.g., a blog, a traditional website or even a unique app). Third, developers have more flexibility to adjust code on the back end without affecting the part customers interact with. They also have choices for different services to power the site instead of using only what the monolith vendor offers.

While microservices offer some significant advantages over monoliths, that approach has flaws of its own. If you’re an online retailer, you need to think hard about whether you want a monolith or a pure microservice system. The monoliths lock you in, making it difficult to customize your experience without a team of expert developers and a lot of costs.

Microservices, while providing choice and flexibility, put more responsibility for security and payment card industry (PCI) compliance in your hands, as well as leave you to manage uptime and support. Managing the various microservices requires cross-functional, vertical teams that must work together to maintain the site. It can be extremely difficult — and potentially expensive — to put all of those Lego pieces together in a way that creates a compelling and manageable customer experience.

One emerging way to build an open system that prioritizes the customer experience is headless commerce, which provides a safe, secure platform while also enabling simple, fast integrations with other applications to provide the service merchants need and customers want. For experience-focused brands (e.g., lifestyle products), direct-to-consumer brands and companies that rely heavily on influencers, native advertising or other content, headless commerce has its advantages.

Headless gives retailers the flexibility to mix a content-focused customer experience with a safe and secure commerce back end. Many brands today are using content to create unique, personalized and exciting experiences for customers. With headless, they can take advantage of the flexibility of content platforms like WordPress or Drupal without sacrificing security behind the scenes.

While microservices require various teams to manage the different pieces, headless doesn’t require specialized teams. That said, headless does bring together two separate tools and, therefore, requires owners to manage two tools instead of one. However, everything connects through the core e-commerce platform, making it easier to manage. Headless also provides flexibility for developers by allowing them to work in the system they are familiar with. Still, some businesses might not want to do that even with the benefit of creating a more experience-driven site.

Microservices can also require changes to the infrastructure and tools used to monitor them, and those changes will likely add to the total cost of the system. Headless, on the other hand, has the benefit of providing flexibility with fewer changes to existing infrastructure.

If you’ve been using a monolith, it might be intimidating to consider a full replatform, but another benefit of switching to headless is that it can be done incrementally. It doesn’t require big, instant change that would disrupt operations. You can decide which capabilities you want to decouple first, and then transition over time while building out other parts.

The biggest challenge to making the switch to headless is that it requires a shift from a product-first mindset to a content-first mindset. That can be tough for many merchants. In addition to managing inventory, production and all the pieces needed to get those products to customers, headless requires time to develop dynamic content and a commitment to keeping things fresh so it feels current for the audience.

Twenty years ago, monoliths or fully custom builds were the only options for e-commerce and other technology systems, but market demand for choice, flexibility and innovation has taken hold. For e-commerce brands, an open API-driven approach is a powerful way to provide positive customer experiences and distinguish themselves from the competition. And in today’s highly competitive online retail environment, everyone could use more ways to set themselves apart from the crowd.

The post E-Commerce Monoliths Or Microservices? The Answer May Be In The Middle appeared first on Artificial Intelligence.

our business operates under a rock on an isolated island in the South Pacific. So you know artificial intelligence (AI) is the big thing today. AI assists consumers in their day-to-day activities with or without their knowledge. Consider services like voice assistance, online shopping, content streaming, and even texting.

Machine learning (ML) is an application of AI. It gives machines the ability to automatically learn and improve from experience. And this happens without the need for previous programming. This makes AI and ML services very lucrative. And many reputable certifications and learning materials exist to get you started.

Over 50 years have passed since the term “Machine Learning” was first coined. And today businesses big and small get crack at the technology. Early adopters of AI reap appreciable results. Benefits include increased efficiency, cost reduction, revenue growth and more.

And yet, less than 30% of businesses and organizations incorporate AI in their business model. You’ll find many potential upsides of the AI revolution. So don’t let your business fall behind.

Machine Learning Benefits

Start to appreciate just how big a boon machine learning can be for your business. Check out three key benefits of ML for you to chew over.

Improves User Experience

It doesn’t matter whether you run an e-commerce store, a SaaS startup, or a design/marketing agency. User experience (UX) remains one of the most crucial factors to success. Do visitors are finding it difficult to navigate your virtual property? They won’t hesitate for an instant to leave and head elsewhere.

Netflix found customers give up after searching for a movie for more than 90 seconds. So the company uses machine learning to improve search results. And Netflix averts user frustration and customer churn. The company also saves $1 billion a year in potential lost revenue.

Amazon uses machine learning to provide highly relevant product recommendations based on the user’s purchase history, ultimately resulting in a pleasant and personalized online shopping experience.

So, basically, while users are browsing your website, you can use machine learning to provide them with personalized purchase recommendations based on their browsing history and previous orders. Furthermore, you can also offer discounts and special offers to returning customers to build loyalty and positive word of mouth.

Apart from personalization, machine learning also serves as the most efficient way to build chatbots – which are becoming more of a necessity for serious businesses than a mere good-to-have feature. Why? More than 53% of customers prefer to use online chat when they need assistance over calling.

Having a chatbot on your website can assist your customer service team by being the first responder to your customer’s inquiries. Chatbots can also help your customer service team by pointing users to various parts of your website, saving time and allowing your team to deal with more important tasks required to move users further down the conversion funnel.

Chatbots built using machine learning are far superior to rule-based ones, and serve to enrich the user experience and make a lasting good impression.

Automates Routine Tasks

For small businesses, every hour saved is a big win. Streamlining operations to make them more time-efficient is a big challenge for businesses.

Automating mundane tasks is the first step to streamlining your business operations. It would free up a lot of time for you and your team to focus on core, productive tasks instead of routine drudgery.

So, tedious tasks such as creating appointment reminders, responding to email, and sending invoices can all be automated effectively using machine learning.

Facilitates Smart Marketing

Marketing has gone fully digital. As discussed, website personalization and chatbots play a big role in determining your business’s user experience.

Now, when you consider the bigger picture of digital marketing for establishing your brand value, many constituents come into play.

For example, email marketing is still huge — it has one of the highest return-on-investment (ROI) at $38 for every $1 spent. Automating email marketing using tools like MailChimp is a great way to make AI work for you. Such automation allows you to create emails get sent automatically when triggered by a subscriber’s activity, inactivity, or a specific date like birthday or anniversary.

Essentially, if a subscriber takes a certain action, a machine-learned response is triggered, and so on down the line until the customer (hopefully) converts.

Next, you can also use machine learning to monitor and manage your business’s social media profiles quite effectively.

Machine learning actually plays a momentous role in modern digital marketing campaigns, from sentiment analysis and split testing to customer retargeting and predicting customer churn. Read this fantastic post to learn more about how machine learning is revamping digital marketing.

Final Words

Of course, implementing machine learning for your business is not exactly going to be a walk in the park. Sure, there are plenty of plug-and-play solutions to website personalization, automation, chatbots, and email marketing. But you’ll either need to hire a bunch of developers or learn how to do it yourself if you truly want to develop a customized solution tailored perfectly for your brand.

In the end, whether you like it or not, AI is here to shake things up in the business world, and it’s better to hop on board sooner rather than later.

The post 3 Effective Ways Machine Learning Improves Your Bottom Line appeared first on Artificial Intelligence.

Amazon is working with top media holding companies and brands to make its data more available for use in their media planning, according to people familiar with the plans.

The e-commerce giant has been huddling with the agency world—companies like Omnicom, WPP, Dentsu Aegis and others—about how they can partner on the future of advertising on the platform, especially when it comes to applying data to targeting ads and measuring how those ads perform.

“Amazon, for the first time ever, is starting to realize that monetizing the data they have and making it available for purchase, not personally identifiable information, could open a revenue steam that wasn’t there before,” said one agency executive who is familiar with the talks Amazon is having with agencies.

Amazon is developing data and analytics tools for brands, backed by machine learning and its ubiquitous web services. Amazon already is running experiments with different agencies and brands, some that look at targeting ads and some that measure attribution, showing which ads lead to business results. There is a flurry of ongoing trials, but advertisers say they expect it will eventually lead to a coherent concrete data service operated by Amazon.

Agencies and brands will be able to build customized ad bidders and reporting tools, buy ad inventory across the web, and import consumer data to learn more about them, and ultimately build ad targeting models that are more exact. “The Amazon cloud already has a bunch of utility built into it,” said a second agency executive who has worked closely with the technology.

The possibilities are greater than anything Amazon has offered so far through its first-generation self-serve ad platform or basic audience matching, advertisers say.

Amazon’s secret weapon in its advertising push against the Google and Facebook duopoly is Amazon Web Services, the cloud computing platform used by companies as varied as Kellogg’s, Comcast and Major League Baseball. Amazon Web Services has become one of the most significant technology layers undergirding industries around the world, and it is a key component to how Amazon will meld data and marketing, according to multiple advertisers.

To that end, Amazon’s ad team has been promoting what’s known as a “clean room” for complex data and analytics research, according to these advertisers, who spoke on condition of anonymity because they were not allowed to discuss details of their dealings with the company. “Clean room” is a generic name for a data-sharing platform that adheres to strict guidelines around privacy and tries to prevent any information from leaking.

That could also lead to more sophisticated data and analytics tools similar to Google’s Ads Data Hub, which is a platform for advanced research into consumer behavior and brands’ businesses.

“What Amazon is building will enable brands to craft a full-journey, attributable marketing experience,” said Chris Apostle, the evp and head of performance at Havas Media, who actively leads the relationship between the agency and Amazon, but said he can’t share further details on all of the retailer’s data ambitions as they are still evolving. However, he has heard the term “clean room” and knows what direction that will take Amazon’s advertisers.

“The digital data ‘clean room’ will provide [insights] into behavior across consumers’ purchase paths,” Apostle said. “This is very different than anything advertisers have been able to do with Amazon until now.”

Amazon wants to use data as a lure for big advertising spenders to commit to investing in its platform, where advertising is the fastest-growing segment of the business. Amazon is expected to hit $4.6 billion in ad revenue in the U.S. this year, making it the third-largest digital advertising platform behind Facebook and Google.

Basic stacks

Amazon already offers ad targeting technology, the kind that most digital ad rivals provide, like being able to their match customer e-mail lists to people shopping on Amazon to target ads to them. Amazon also has basic demographic and shopping data that let advertisers target consumers based on characteristics like age, location, gender and purchase history.

Amazon ad formats include search ads, which can be targeted to the queries people type into the search box. It also has display and video ads, which are not as easy to target because they don’t come with an immediate signal like search intent. Google solved this problem on YouTube, for instance, by targeting video ads based on a viewer’s search history.

Amazon is building a marketing ecosystem that could rival Google, though. It also has video, through Fire TV and Twitch, the streaming service similar to YouTube. Amazon also owns IMDB, which shows video ads, and also has publisher services that deal with websites just like Google’s publisher ad network, where it can target ads outside the websites it directly owns.

What Amazon is missing is better technology and data to make sense of its sprawling marketing platform and unite its disparate formats. Advertisers say the design of its self-serve ad management platform is outdated and clunky. That’s partly the reason why the company has worked so closely with the agency world and third-party marketing tech platforms like Kenshoo in the past year, to help test and design new ways of buying ads on Amazon.

Amazon’s ad business was streamlined this year, too. For years, Amazon’s ad offerings were a jumble of services with different acronyms and run by separate teams. There used to be Amazon Marketing Services known as AMS, and Amazon Media Group known as AMG, and Amazon Advertising Platform known as AAP. Now, all those fall under Amazon Advertising.

Amazon declined to comment for this story, and the exact details of any planned data products are still in the works. Advertisers said they were still in discussions with Amazon about how they could help develop these next-generation data services, and anything concrete would likely come next year.

Data deals

What is certain is that sophisticated data partnerships are already forming. For instance, agencies have been using Amazon Web Services to analyze data from Facebook ad campaigns, according to a person familiar with the offering. That means advertisers are able to import data out of Facebook and dissect it in Amazon’s environment, which requires a partnership between the two web rivals.

Amazon declined to comment on the arrangement. However, advertisers said that the whole point of the “clean room” technology is so that no data from Facebook leak into Amazon.

“It could get tricky for a lot of brands,” said Andy LaFond, executive media director at R/GA Chicago, who is not involved in Amazon’s plans, but is familiar with the platform. “Data is Amazon’s power for sure and brands have to be really careful about what data they’re willing to share with Amazon.”

Amazon is already known for being able to give brands solid intelligence about ads on its properties and tell them when those ads lead to sales on Amazon. But the company is expanding its ambitions and wants to be able to help advertisers serve ads anywhere online and measure the impact, even if the final sale doesn’t take place on Amazon.

Earlier this year, Amazon developed a pixel, a common technology that marketers use to measure ad performance. In this case, Amazon wanted to show marketers that its ads are more efficient than its rivals, and brands could attribute the results of marketing campaigns to their spending on Amazon.

Amazon’s data ambitions include giving brands the ability to target their exact consumers, identifying what they bought down to the last nail, according to advertising executives. Also, instead of just targeting people by keyword when they’re searching Amazon, the same type of targeting could be applied to display and video ads across the web, the advertising executives said.

“This is a faster, more direct way to get to data,” said a digital agency advertising executive who works closely with Amazon. “From the search side it’s easy, but from the display side it’s a little harder to target that exact consumer who bought that exact product.”

Amazon data products could also provide unique insights about consumers who buy certain products, like figuring out what else they might buy, even when the link is not so obvious.

“Amazon is dealing with the most valuable data asset in the world, even more than Google’s search data,” said an executive at a top marketing technology and analytics firm. “If I want to know what someone will buy, then I have to know what they already bought in the past.”

The post How Amazon is talking about big data with Madison Avenue appeared first on Artificial Intelligence.

Those who cannot learn from history are doomed to repeat it.” – George Santayana

In early September, the media went abuzz with news of Amazon becoming the second US company to surpass the $1 trillion market cap. However, the news of Amazon’s success has eclipsed certain interesting facts:

• 2017 was the worst year ever for traditional retail stores thanks to e-commerce businesses like Amazon.
• More than 662 traditional retail businesses filed for bankruptcy in 2017 — a 30 per cent increase from 2016.
• Traditional retailers were the second biggest job loser in 2017 — contributing to a loss of over 36,000 jobs.
• Abercrombie & Fitch, American Apparel, Bebe, J.C. Penney, Macy’s, Sears, and RadioShack are just a few of the high profile traditional retailers that had to shut down stores due to internet companies like Amazon cutting into their revenue.

  Now, there’s no point crying over spilt milk. Experts believe that 2018 will end up being as difficult as 2017 was for traditional brick-and-mortar stores. All indications point to the fact that traditional brick and mortar stores will continue to go into obscurity for as long as the internet continues to thrive.

  What is most noteworthy, however, isn’t the fact that many of these businesses are going under but the fact that they ignored the internet, which eventually was responsible for their going under, for so long until it was too late. Eventually, smaller, underfunded, and bootstrapped startups like Amazon came over and ate their lunch.

  While not much can be done to help the traditional brick and mortar stores that are going under, there is another technological revolution — similar to the kind that forced many of these businesses to go under — on the horizon, and it is in artificial intelligence.

  I believe artificial intelligence will be as disruptive as the internet was, and, just as was the case with the internet, many big players are, and will continue, ignoring artificial intelligence. In fact, as reported here on TNW just recently, a recent research paper shows that China is on track to outspend the US on AI research by the end of 2018.

  There are sparks in AI but we need fire

  As was chronicled here on TNW just a few months ago, sparks are already being created in the private sector when it comes to artificial intelligence:

  Among big companies, automaker Volkswagen (VWAGY) is already accelerating research and work on AI in an attempt to make self-driving cars a reality. In banking, a good portion of JPMorgan Chase’s (JPM) $10 billion tech budget for 2018 was assigned to AI, and their hiring decisions show that they are taking AI serious. They have already rolled out AI-powered services to help their clients better invest. In healthcare, Philips (PHG) has an AI strategy in focused on releasing AI-powered solutions to assist medical professionals in better assisting patients.

  Among startups, BioSig Technologies (BSGM) is using signal processing to develop a system that acquires and manages electrocardiographic and intracardiac signals in patients undergoing electrophysiology (EP) procedures. Phrasee is using artificial intelligence to analyze successful campaigns and create copy for organizations such as The Times and Domino’s. Vivacity Labs is using AI camera technology to gather data on transport systems and provide data for smart cities.

  While the above are all examples of good use of artificial intelligence, both among successful mega corporations and startups, we have just barely scratched the surface.

  Research from Adobe shows that only a measly 15 percent of enterprises are using artificial intelligence, and data from MIT Sloan Management Review shows that, while large companies with over 100,000 employees are the most likely to have an AI strategy, only half of them have one.

  In essence, it doesn’t seem the government is going to take a lead when it comes to artificial intelligence, and while the private sector is indeed making moves, adoption is still very slow. One thing is clear, however: we can’t afford to ignore artificial intelligence.

  Here are some of the reasons why we can’t ignore AI:

  1. Information can be processed, analyzed, and used faster: Due to AI’s adaptiveness, it is easy to process, analyze, and make available timely information at a much faster pace.

  For example, in fields such as healthcare where lives of users could be at stake, health information can be quickly analyzed to provide more timely solutions and in a way that reduces mix-ups, misdiagnoses, and other medical errors.

  2. AI can help fight crime better: When we look at how much resource is being wasted on fighting crime, a strong case is made for investing more into research on AI applications for fighting crime.

  For example, it was only recently that it was in the news that a Chinese fugitive was singled out and arrested from a crowd of 60,000 people attending a pop concert. Guess how he was arrested? He was detected by an AI-powered facial recognition system.

  There are several advantages to using AI to fight crime: just a few include the fact that it minimizes resources used, works faster and with better precision, and can be automated.

  3. AI can help make transportation more efficient: Self-driving cars, smart traffic systems, and self-managed fleets are just some of the ways artificial intelligence can help ensure a more efficient transportation system.

  4. Financial systems can benefit from AI: Banks and financial institutions can use AI to improve compliance, operational efficiency, develop effective investment strategies, and automate user activities.

  Conclusion

  I could go on and on, but the potential applications of artificial intelligence are limitless. While our government isn’t doing as much as they can when it comes to AI, the private sector has a lot at stake — we will have a bigger price to pay if we sit and do nothing.

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In a modern company like Amazon, almost all human activity is directed by computer programs. They not only monitor workers’ actions but are used to choose who should be employed. Yet it emerged last week that the company had scrapped an attempt to use artificial intelligence to select workers on the basis of their CVs since the results consistently discriminated against women.

This is a welcome decision that illuminates two important facts about machine learning, the most widely used technique of AI at the moment. The technical or operational point is that these programs, no matter how fast they learn, can only learn from the data presented to them. If this data reflects historic patterns of discrimination, the results will perpetuate those patterns.

That’s what Amazon found: by training its AI with the records of those job applicants who had been hired in the past, who were overwhelmingly men, it taught the program to discriminate against applications from women. Since the program had access to immense amounts of data about the applicants, it was able to infer their sex from factors such as whether they had attended an all-woman college. And since it had neither conscience nor consciousness, the machine behaved as if being female were a sign of inferiority, just as the industry it learned from had done.

This is an instance of a wider problem that has appeared in more sinister contexts, such as decisions over which prisoners should get parole. It is also one that is extremely hard to surmount. When you ask computers to detect patterns in data, which is the short description of machine learning, the patterns they find are usually genuine ones, even if we have not noticed them before.

This kind of mesh of inference is implicit in the way that language works, as Joanna Bryson, one of the authors of a ground-breaking study of the way that machine learning can expose the prejudices embedded in our use of language, points out. We can’t get away from it. It encodes both the wisdom and the folly of all those who have used language before us. Patterns of language describe the way the world is, whether or not it ought to be that way. So when we want to distinguish an “ought” from the “is” of usage, it requires a sustained collective effort.

The technical aspects of the story are not the only salient ones. What matters for the future is the recognition that the responsible actor in the story was Amazon itself, the company, and not the AI it built and used. Discussions of AI too often proceed as if the technology will appear among us like the monolith in the film 2001: something alien and immensely powerful but immediately recognisable and clearly distinguished from the hominids around it. It’s not happening like that at all.

AI is already all around us and is always a hybrid or symbiotic system, made up of the humans who tend the programs and feed them data quite as much as the computers themselves. Companies such as Google or Amazon – and even traditional media and retailers – are now partly constituted by the operations of their computer systems. It is therefore essential that moral and legal responsibility be attached to the human parts of the system.

We hold Facebook or Google responsible for the results of their algorithms. The example of Amazon shows that this principle must be more widely extended. AI is among us already, and the companies, the people, and the governments who use it must be accountable for the consequences.

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NEW DELHI: Online marketplace Snapdeal Wednesday announced an ‘AI Hackathon’ aimed at developing solutions based on the new-age technology that can help address challenges related to the e-commerce sector.

“This competition challenges participants to solve interesting problems around computer vision and natural language processing. The winner will be selected based on his/her ability to solve the real world business problems,” Snapdeal said in a statement.

Some of the key issues that could be taken include the ability to personalise search results to suit individual customer’s requirements and minimisation of fraud on e-commerce platforms.

As part of the 30-day competition, which begins September 10, the winner would have to beat the algorithm built by the internal team at Snapdeal as well as that built by other contestants.

Apart from a cash prize, the winner will also have an opportunity to join Snapdeal’s AI team.

Once a major player in the Indian online marketplace segment, Snapdeal was impacted severely by the intense competition in the e-commerce segment, led by Flipkart and Amazon. These two players alone have pumped in billions of dollars in investments to scale their operations in the country.
Last year, after its talks for a USD 950-million takeover by Flipkart fell through, Snapdeal founders Kunal Bahl and Rohit Bansal said the company will pursue a fresh strategy in the Indian market.
Dubbed as Snapdeal 2.0, the company has re-worked its business model and shed assets like Freecharge (sold to Axis Bank for Rs 385 crore) and logistics arm Vulcan Express (acquired by Kishore Biyani’s Future Supply Chain Solutions for Rs 35 crore).

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There is not even a single point of doubt that artificial intelligence (AI) is going to change the world over the next few years. It has already become an integral part of the status quo. AI is graced by almost every industry and sector globally including e-commerce.

Below are some of the examples that explain how we are going to live in the virtue of AI applications to advance our e-commerce business:

Predicting Sales

Machine learning is the supervised form of AI in which the sample data is given which helps to train it and process the data in the future. With more than 200 million products being traded, machine learning algorithms help us to forecast the sales volume based on the historical data of products to a higher degree of accuracy.

Targeting Right Groups

Traditionally, people make use of unsupervised learning algorithms which are segmenting the customer groups for the marketing campaigns. Marketers have defined the segment in such a way that makes sense to them via gender, age, and income. But AI is more on demonstrating that these are not the most efficient approaches rather it is based on more accurate predictor of tastes or interests.

Analyzing Rating and Reviews

Understanding the user reviews and ratings play an important role, but it is also a time-consuming work. By using and applying the structural algorithms of machine learning, a method which is most commonly used in the study of the formation of words(morphology), can efficiently collect and analyze the product reviews with text that is both negative and positive. Apart from this, structural machine learning can also help in mining the valuable information from the reviews and page explanation.

Classifying Products and Categories

Categorizing the products under each classification is a very arduous task. To solve and overcome this problem, a semi-supervised artificial learning algorithm can be used, which resamples the data until the algorithms learn how to process it in the best efficient way.

Improved Customer Experience

With the help of ‘reinforcement’ algorithms, now we are able to process data based on the user reaction with respect to the products they are shown. For example, let’s say, whether the user has clicked on the product when it was shown to them in the search result option or in recommendations. Reinforcement algorithms check how much ‘reward’ (positive response) is obtained when different types of products are displayed in response to different circumstances. With the combination of user’s past reactions at a certain point in time, this algorithm helps to know about the most efficient course of action to take when those circumstances occur again. And, with each reaction and response, this algorithm keeps on refining smartly.

Image Recognition

On the platform of the customer to customer interaction, deep learning techniques can be efficiently and effectively used for image recognition. Inspired by the functions and structure of the deep learning algorithms, one can develop the ability to see and recognize objects in a snap and categorize them automatically, which makes it easier for the users to post the products for sale.

E-commerce may or may not be the first thing which comes to the mind of the people while hearing the words “artificial intelligence” or “machine learning” but there is no denying in it, that it is already on the way we buy and sell online.

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