Keeping emerging AI trends in mind, marketers must learn how to navigate a world where big data and Automation are essential. After all, keeping up with innovation is now the key to marketing success. Businesses will need to understand and apply new apps, tools, and approaches to thrive. As we recover from the pandemic, businesses are putting more effort into company blogs, social media, and video. By merging these channels using machine learning and Automation, businesses can identify which emerging AI marketing trends could support their needs.
Imagine that you are planning a trip. A few decades ago, it would take you a lot of time and effort to research destination and accommodation options, book a flight, make a hotel reservation, rent a car, and do a bunch of other trip-related activities. Today, with the help of machine learning and AI, you can use a one-stop travel platform to plan and book everything you need. And the best thing is, you don't have to leave your home or even your bed. This convenience wouldn't be possible without machine learning and artificial intelligence technologies actively adopted by the travel, tourism, and hospitality industries in recent years.
Dynamic pricing is the practice of optimizing product and service prices according to supply and demand, competition price, or subsidiary product prices. It gained popularity in the 1980s as the airline industry in the US started developing software to adjust flight prices according to departure time, destination, season, etc. which results in 3-10% in profits according to the used module. The rise in popularity led other industries to leverage dynamic pricing, however, different industries use different dynamic pricing types according to their requirements and customers. In this article, we explore the different types of dynamic pricing, how to implement them, and industries benefiting from each type. Segmented pricing, also known as price discrimination, is where businesses set different prices for the same product based on customer data (e.g.
Alexa is getting some company. Amazon and Disney have unveiled an "entirely new persona" called "Hey, Disney!" "It marks the first time that we're making another voice assistant available alongside Alexa on Echo devices," Aaron Rubenson, Amazon's vice president of Alexa Voice Service & Alexa Skills told USA TODAY ahead of Tuesday's announcement. "Hey, Disney!" will be available both in-room at Disney Resort hotels and on supported Amazon Echo devices at home. "It's going to give guests the ability to sort of interact with our beloved characters in entirely new ways" said Dan Soto, vice president of Technology and Digital for Disney Parks, Experiences and Products. 'Alexa' tips and tricks:How to make the most of your Amazon Echo "Hey, Disney!" was designed to make the Alexa experience even more magical with things like jokes, interactive trivia with your family, personal greetings from our characters, soundscapes and more," Soto told USA TODAY. "It will absolutely include authentic character voices, original recordings, unique audio environments inspired by our films and our destinations around the world and over 1,000 magical interactions for our guests to discover." "Hey, Disney!" is expected to roll out next year. "Hey, Disney!" will be free for guests to access on Amazon Echo devices in Disney Resort hotel rooms. It's not yet clear how much it will cost to buy on the Alexa Skills Store for use at home. Details will be released on amazon.com/heydisney. Like Alexa, the voice assistant has its own unique wake words: "Hey, Disney!" While the bulk of "Hey, Disney!" features will be available both at home and at Disney Resort hotels, hotel guests will have additional offering through Alexa for Hospitality. "You can ask'Hey, Disney!' things like, 'What time is the park open?
Would you like to be in the aviation business right now? For most people, this would be one of the worst places they could be in. For FLYR Labs CEO and Founder Alex Mans, crisis equals opportunity. FLYR Labs, makers of the data and AI-driven Revenue Operating System for airlines, travel, and transportation, today announced it has secured $150 million in Series C. ZDNet caught up with Mans to find out more about the investment and FLYR's product and growth. "Growth" and "airlines" are hard to use in the same sentence these days.
Traffic flow forecasting is a crucial task in urban computing. The challenge arises as traffic flows often exhibit intrinsic and latent spatio-temporal correlations that cannot be identified by extracting the spatial and temporal patterns of traffic data separately. We argue that such correlations are universal and play a pivotal role in traffic flow. We put forward spacetime interval learning as a paradigm to explicitly capture these correlations through a unified analysis of both spatial and temporal features. Unlike the state-of-the-art methods, which are restricted to a particular road network, we model the universal spatio-temporal correlations that are transferable from cities to cities. To this end, we propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor comprising the data from its neighbors within close time points. Based on this idea, we introduce spacetime neural network (STNN), which employs novel spacetime convolution and attention mechanism to learn the universal spatio-temporal correlations. The proposed STNN captures local traffic patterns, which does not depend on a specific network structure. As a result, a trained STNN model can be applied on any unseen traffic networks. We evaluate the proposed STNN on two public real-world traffic datasets and a simulated dataset on dynamic networks. The experiment results show that STNN not only improves prediction accuracy by 15% over state-of-the-art methods, but is also effective in handling the case when the traffic network undergoes dynamic changes as well as the superior generalization capability.
Top aviation companies ANA Holdings Inc. and Japan Airlines Co. are planning to launch commercial drone services to deliver medical supplies and daily necessities to people living in remote areas. The two companies see the new services as playing a useful role in supporting local health care provision and disaster preparedness as well as expanding community infrastructure on remote islands and other far-flung areas. At the same time, the initiatives will help them promote management diversification and strengthen profitability as the coronavirus pandemic continues to take a toll on their overall business performances. ANA Holdings, the parent of All Nippon Airways Co., conducted a trial run jointly with a pharmaceutical company and other entities in March. Footage that it released shows a drone carrying a package of medical supplies from one island to another among Nagasaki Prefecture's Goto Islands at a speed of around 100 kph.
But AIs can review the content at machine speeds, and what the audit team is looking for in an audit are things like bogus vendors or payments that break trends. For instance, we had a guy that would book airfare months in advance, then book it again right before the trip. After expensing it, he'd return the second ticket and fly on the earlier cheaper ticket pocketing the difference. But you had to know about the potential as an auditor and then look for people who always seemed to book at the last minute, see if that was the nature of the job or an operation, and finally cross-check the airline records to see what the trip cost. All of this could be automated, and an AI could be trained to do all or part of this validating effort if it had access to the relevant data.
Contact centers have experienced overwhelming strain since the onset of the pandemic and for many organizations this chaotic trajectory has continued. In the travel industry, for example, airlines are currently facing record-breaking call volumes and their service agents are struggling to deal with a surge of inquiries. Delta reports call wait times of two to three hours and other major U.S. airlines have call wait times as long as 8 hours and 30 minutes. Extending superior customer experiences in these types of circumstances is challenging, if not impossible, and customer service agents are equally affected. The average customer service agent remains in their job for approximately one year, according to the U.S. Bureau of Labor Statistics.
In our previous work, we designed a systematic policy to prioritize sampling locations to lead significant accuracy improvement in spatial interpolation by using the prediction uncertainty of Gaussian Process Regression (GPR) as "attraction force" to deployed robots in path planning. Although the integration with Traveling Salesman Problem (TSP) solvers was also shown to produce relatively short travel distance, we here hypothesise several factors that could decrease the overall prediction precision as well because sub-optimal locations may eventually be included in their paths. To address this issue, in this paper, we first explore "local planning" approaches adopting various spatial ranges within which next sampling locations are prioritized to investigate their effects on the prediction performance as well as incurred travel distance. Also, Reinforcement Learning (RL)-based high-level controllers are trained to adaptively produce blended plans from a particular set of local planners to inherit unique strengths from that selection depending on latest prediction states. Our experiments on use cases of temperature monitoring robots demonstrate that the dynamic mixtures of planners can not only generate sophisticated, informative plans that a single planner could not create alone but also ensure significantly reduced travel distances at no cost of prediction reliability without any assist of additional modules for shortest path calculation.