According to MarketsandMarkets, the global retail analytics market will likely more than double in size during 2015-2020, totaling about $5.1 billion at the end of the forecast period. The adoption of analytics solutions is increasing as more enterprises worldwide are realizing significant returns from using BI and analytics platforms and services. Of late, many retailers seem to be jumping on the AI bandwagon to improve their marketing efforts. Sailthru, a marketing technology company, published a study that included a survey with more than 200 retail marketers. Over the course of 2017, these retailers plan to leverage AI to expand their mobile and social media marketing strategies aside from improving customer journeys.

Big data, the Internet of Things, and social media have all forever changed the art and science of marketing and that's already old news. To even remain relevant in the near future, marketers need to provide personalized experiences at the speed and scale only automation can fulfill. Artificial Intelligence for Marketing provides marketers with a comprehensive introduction to the field of data science without requiring a background in advanced programming and mathematics. Today's marketing decision-making happens in real time, every day, and artificial intelligence (AI) and machine learning (ML) give marketers a hands-free way to quickly and effectively respond to data from a customer or potential buyer and tailor fit a product and buying experience. Buyers on the fence can automatically receive special incentives to buy, and other customers can be directed to related products and services based on data-driven insight.

The store found some help in its war against Amazon. Walmart is coming off an 11-day rally, it's longest since 1995. In the future, Walmart shoppers may not have to ask for help. Just a simple scowl could summon a helpful worker ready to assist. A recent patent filing suggests new ways the nation's top retailer wants to get even closer to its customers: a video system that a system keeps tabs on customers' facial expressions as they move through the store and the movements at checkout lines.

There's no denying that Google Home and Amazon Echo (or the less-expensive Echo Dot, if you're not using it for music) have changed the way we interact with our homes. Turning on the lights has never been easier, nor has it been simpler to field the latest traffic report or order delivery for dinner. The future is here, and we're reveling in it! But the proliferation of these devices around our homes leaves room for error. Google's and Amazon's connected speakers must always listen for us to utter their magic "wake" words--OK Google or Alexa respectively--in order to perform their tasks.

Whilst the use of machine learning in marketing seems to be skyrocketing, there's a dearth of coverage in the media that tries to get to the bottom of this technology in terms a layman can understand. I am irrefutably a layman, and have written a little on the topic of AI for Econsultancy, often specifically about ecommerce. In this blog post, we'll be talking to the founder of LoveTheSales.com, Before we start, let me point out that this year's Festival of Marketing has a whole stage (one of 12) dedicated to AI. View the agenda and get your tickets here. Machine learning is used to classify these products, tagging them to enable the website to sort them into the right categories and to show a user products they may be interested in.

What lies at the heart of this book is a great story of a relationship between man and machine, changing as D acquires human-like intelligence – and a human-like body - which provides the framework for an exploration of artificial intelligence and the potential for AI's self-development, where the future of AI may lead, and how we might interact with these bots as they form personalities of their own. Skillfully written with a fast-paced story, it's a fascinating read that has kept me thinking since I finished reading it.

Launching a webstore in 2017 has become easy. Despite that, growing and scaling an online business remains extremely difficult even for the most seasoned brands and eCommerce experts. Ecommerce sales are growing, often at the expense of retail store foot traffic, but many retailers are struggling to capitalize on their digital sales channels. The secret to success is no longer just to get it out there and see how it performs. The most successful retailers are strategic and targeted in their efforts, both offline and online, incorporating sales strategies as well as data to gather a deeper understanding of their customers.

This is a real mega-work on advanced topics and implementations in Python. My only reservation is one I have about all his books, that language gets very contorted and unclear in the middle of things that need elucidation. Sometimes I'm unsure I've read something more than gibberish. Often he could explain things in a far simpler way. His drive to appease different computer-language religions and Python versions generates a lot of clutter in the learning process.

First some general, higher-level thoughts and comments before I dive into specifics: MY BACKGROUND: Data Scientist; B.S. in Economics and M.S. in Business Analytics; experienced (though by no means expert) user of Scikit-learn OVERALL THOUGHTS: I've purchased and read (virtually) every Machine Learning book that aims to teach the reader the basics of ML using the Scikit-learn library as the main focus. I've found them to be...less than satisfactory. The examples in other books often use ML techniques in contexts for which they are not intended to be used and/or contexts they are not used in out in the real world (among other issues I have found within them). In stark contrast, Python Machine Learning by Sebastian Raschka is stunningly-impressive, not only for the breadth and depth of coverage, but also in the manner the information is presented to the reader. To date, I have not encountered a book on ML that incorporates multiple levels of learning in a manner such as this.

Data mining is not an intuitive activity. It requires skills and techniques which can be honed by using books like data mining: practical machine learning tools and techniques. This book is an ultimate guide for applying machine learning techniques and tools in real-world situations of data mining. The book will help you interpret outputs, evaluate results and prepare inputs and will provide the algorithmic methods for efficient data mining. You will not only find the explanation of concepts in this book but will also come across practical advice for successful data mining.