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100+ AI Use Cases & Applications in 2021: In-Depth Guide

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AI is changing every industry and business function, which results in increased interest in AI, its subdomains, and related fields such as machine learning and data science as seen below. However, we also note that since COVID-19 outbreak, interest in AI, as measured by Google queries about AI, has been stable / possibly declining. This may be due to increased interest in COVID-19 and its effects during this period. However, this depends on the specific industry and applications, we see increased interest in AI in manufacturing during the same period. As of 2018, 37% of organizations were looking to define their AI strategies. There has been significant progress since then and according to a recent O'Reilly survey, 85% of organizations are using AI. To integrate AI into your own business, you need to identify how AI can serve your business, possible use cases of AI in your business. Marketing can be summarized as reaching the customer with the right offer, the right message, at the right time, through the right channel, while continually learning. To achieve success, companies can leverage AI-powered tools to get familiar with their customers better, create more compelling content, and perform personalized marketing campaigns. AI can provide accurate insights and suggest smart marketing solutions that would directly reflect on profits with customer data. Marketing analytics: AI systems learn from, analyze, and measure marketing efforts. These solutions track media activity and provide insights into PR efforts to highlight what is driving engagement, traffic, and revenue. As a result, companies can provide better and more accurate marketing services to their customers. Besides PR efforts, AI-powered marketing analytics can lead companies to identify their customer groups more accurately.


123 AI Use Cases & Applications in 2018: In-Depth Guide

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We are tracking the most impactful AI use cases here. This is meant to be a list that grows over time so feel free to contribute with your comments, this list is definitely not comprehensive now. And share the knowledge with your twitter followers: @AndrewYNg claims that "AI is the new electricity". We compiled 100 applications runnning on this new electricity. Marketing can be summarized as reaching the customer with the right offer, the right message, at the right time, through the right channel while constantly learning. Optimizing product, pricing & placement allows marketers to create an attractive value proposition to customers. Gesture Control: Gesture control enables higher levels of activity and engagement by allowing users another mode of interaction with your digital products. Quantify the gesture levels and other engagements in order to provide meaningful insights. Pricing Optimization: Also called dynamic pricing or demand pricing, pricing optimization allows companies to optimize markdowns. Optimal markdowns minimize cannibalization while maximizing revenues. One of the easiest transformations a business can achieve, dynamic prices directly impact the bottom line and can be rolled out in a matter of days. Optimize markdowns to minimize cannibalization while maximizing revenues. Identify which products are of significant importance for customers.


Machine Learning Algorithms: A Detailed Primer

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Machine Learning has taken over the world, and it has come out from the fancies of the science fiction world to business intelligence reality. It can be termed as a new age business tool that entails multiple elements of business operation. The future of business intelligence is now dependent on machine learning. Machine learning is an important technology in the modern workplace. It has crossover from science fiction to business intelligence reality, where it can be used for multiple purposes such as improving marketing strategies or analyzing customer trends with great accuracy.


How market leaders use machine learning in eCommerce, and you should too! - Greenice

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Is your eCommerce business suffering from costly returns, customer churn, or margin decreases? Do you try your best to optimize prices but spend too much effort? Or maybe your online consultants are overloaded with claims and questions from customers and fail to deliver the best quality services? If you recognized some of the challenges of your business, then it may be useful for you to know how the most successful retailers in the world manage them with the help of Machine Learning. Come on and dive in with us into the ocean of the opportunities for your eCommerce business with ML! To start, I'll give you three examples of using Machine Learning for eCommerce: Big or small, most retail websites have similar challenges and goals.


How Machine Learning can boost your Predictive Analytics

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Every business seeks to grow. But only a handful of companies that successfully actualize this vision do so through data-based decision making. And to make these informed decisions, companies have been using machine learning-based predictive analytics. Predictive analytics is predicting future outcomes based on historical and current data. It uses various statistical and data modeling techniques to analyze past data, identify trends, and help make informed business decisions.