If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The ultimate role of AI in marketing is to add value and purpose to both brands and customers. AI is a great tool that can augment the idea and capabilities of human marketers. According to the 2017 State of Inbound Report by HubSpot, generating traffic and leads and proving RoI, and lack of budget for marketing activities are the leading challenges that marketers face even today. Data overload from multiple sources, limited resources and time to implement activities, a crowded marketplace and increasing customer expectations make the job all the more difficult. Traditional marketing campaigns are far less efficient in gaining and retaining a customer.
The Pentagon's research chief is deep in discussions about the newly announced Joint Artificial Intelligence Center, or JAIC, a subject of intense speculation and intrigue since Defense Undersecretary for Research and Engineering Michael Griffin announced it last week. Griffin has been sparse in his public comments on what the center will do. But its main mission will be to listen to service requests, gather the necessary talent, and deliver AI-infused solutions, according to two observers with direct knowledge of the discussions. Little else about the center has been decided, they say. "We are looking right now, as we speak, at things like how we structure it, who should lead it, where it should be, how we should structure our other research.
Can artificial intelligence begin to make a dent in the bloated healthcare system? Here are a few ways you could see that tech first. Healthcare has grown increasingly sophisticated, but will remain perhaps one of the most people-intensive businesses around. Throw lots of ever-changing technology in with large essential workforces and ever-tighter margins, and you have a system that continues to groan under its own weight. Can artificial intelligence begin to make a dent in this bloated system?
There are plenty of reasons to use voice assistants, but one of the key drivers is speed. The majority (82%) of voice assistant users say that fast and accurate replies is the most compelling feature that causes them to use the voice assistant. These are among the findings in the Conversational Commerce study by Capgemini Digital Transformation Institute, which surveyed 5,000 consumers in the U.S., U.K., France and Germany. Although the study was focused on consumers making purchases via voice assistants, some other interesting insights about consumers who use voice assistants also were found. For example, more than two-thirds (69%) prefer to use their voice assistants in their living rooms and 61% in the kitchen.
In today's world, every customer is faced with multiple choices. For example, If I'm looking for a book to read without any specific idea of what I want, there's a wide range of possibilities how my search might pan out. I might waste a lot of time browsing around on the internet and trawling through various sites hoping to strike gold. I might look for recommendations from other people. But if there was a site or app which could recommend me books based on what I have read previously, that would be a massive help. Instead of wasting time on various sites, I could just log in and voila! 10 recommended books tailored to my taste. This is what recommendation engines do and their power is being harnessed by most businesses these days. From Amazon to Netflix, Google to Goodreads, recommendation engines are one of the most widely used applications of machine learning techniques. In this article, we will cover various types of recommendation engine algorithms and fundamentals of creating them in Python. We will also see the mathematics behind the workings of these algorithms. Finally, we will create our own recommendation engine using matrix factorization.
Ever wondered how you went to YouTube to watch just a 5 minutes video but ended up there for 3 hours? Or saw an advertisement on some page of exactly the same thing that you have been planning to buy for last 15 days and ended up finally buying it! Isn't it great how your computer knows what you have been desiring? Well, it's not your computer but the bots, the algorithmic bots that have been watching you all the time. So the question comes, how these bots are made, and how software testing concepts come into play here.
Well, before answering that, we must take a look at what they are. Chatbots are automated programs that take on mundane and repetitive work such as answering customer queries. Their surge is the result of an ongoing evolution of AI and machine learning (ML) that never seizes to amaze us with new capabilities. Chatbots facilitate online transactions and open up new growth opportunities. They handle interactions with customers and simulate the experience that they have in a retail store.
To boost sales in an overly competitive environment, many top brands have turned to new, inventive, and unconventional ways. They have embraced the use of Artificial Intelligence as a marketing strategy to create sales content, interact with customers, and for cybersecurity reasons. Sephora, for example, uses their Kik Messenger chatbot to communicate with clientele, and give them suggestions about upcoming Sephora offers based on the client's preference. The popularity of AI in retail is steadily growing, with 60% of ecommerce brands expected to implement the use of AI before 2018 ends. AI's appeals lie in its efficiency to market like or even better than humans, and the ability to help companies cut costs.
Even though artificial intelligence has been around for 60 years, it is only now that we can use AI to create personalized customer experiences, better self-service and key customer insights. For decades we've provided customers with experiences that were very non-human. All too often we don't know who our customers are, what's going on in their lives or what they need. Across the board, customer experiences are still made for the masses and not the individual. However, advances in AI have made it possible for brands to treat customers less like machines and more like people.
Previously, we looked at how Artificial Intelligence (AI) has changed the supplier side of the retail eco-system, especially on two fronts – Price and Product Offering. In this post, we shall analyze how it has affected the buyer's journey at almost every step of the way. As most of you will know, a buyer's journey starts from the awareness stage, where he comes to learn of a product or a brand, and then goes on to the following stages: research, consideration, purchase and retention; the latter is where a company tries to hold on to its customers. After all, history shows that people who have bought from your company before are most likely to be repeat customers if they are happy with the overall journey. AI retains the power to analyze vast tracts of data, and that includes human behavior.