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IoT is Coming, Jon Snow…
Posted: 05/21/2019

Hospitality is prime for the coming advent of the various devices that make up the Internet of Things. Estimates show the industry now represents 17.5 million rooms worldwide and savvy guests are demanding more personalization and an overall improved guest experience along their connected travel journey and belief is that IoT can bring this to reality. 

The forces driving local search rankings are constantly changing. But recent studies suggest that in 2019, four key factors make up the local search algorithm. 
 
The most significant factor is Google My Business (GMB). If you’re not on it, get on it now.

The robotic revolution in the hospitality industry might seem to have taken a step back. This January, the famously quirky Henn-Na Hotel in Japan fired half of its 243 robot staff. The robotic workforce reportedly irritated guests and frequently broke down.

Think about the moment when you first enter your hotel room. Look around: Does the room tell you anything unique about the hotel where you are staying? Or is it all beige walls and double beds with white covers, and you have to walk back outside and look at the sign on the hotel’s facade to even remember where you are?

Hotel guests commonly bring multiple devices with them during their stay. However, many hotel environments don’t provide easy access to charging outlets. This situation can lead to a guest feeling more than inconvenienced. A recent survey found almost 90 percent of people "felt panic" when their phone battery dropped to 20 percent or below.



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Big Data Analytics Needs Big Questions

12/02/2014

Data, by nature, represent the information about yesterday’s world. The more the data, the better we can learn about what happened. In conventional wisdom, the bigger the data, the more profitable the analytics will be. This, however, is not always true. The profitability of big data analytics is largely determined by the questions that we can ask.

Take hotel revenue management (RM) as an example. The objective of hotel RM is to influence demand via pricing such that the total revenue will be maximized. Demand for hotel rooms comes from a number of distribution channels, which may range from call centers to brand websites to online travel agencies, and so on. The success of implementing hotel RM requires a good understanding of demand patterns of yesterday and tomorrow.

A classical question that we frequently ask is: how demand behaves across the channels? To answer this question, a dataset of inquiries and bookings over the channels are often collected and analyzed. With the advance of IT technology, the dataset might grow bigger because we are able to collect additional information that were difficult, if not impossible, to get before. For example, for an online inquiry, the history of its clicking path can also be captured if a brand website is appropriately implemented. The use of additional data, in this case, is indeed helpful for us to better answer the question. It not only allows us to analyze how demand is distributed across the channels, but also to predict how it might change. From the viewpoint of traditional business intelligence (BI), this question appears to be perfect for data analytics.

Under the context of RM, this question seems to be not “big” enough. As we know, the ultimate RM objective is to maximize the total revenue for tomorrow. This question, however, has a false belief that limits us from achieving this objective:  the maximal revenue has and will be gained from the existing channels only. This belief is particularly unrealistic in this rapidly evolving world, where tomorrow’s channels might be quite different from those of yesterday. If we continue to ask questions based on this false belief, our data analytics will fail to capture revenue opportunities for the future demand.

Therefore, we need to ask big questions while performing data analysis. For instance, in addition to the above question, we may also ask: How would demand to the other channels migrate if the call center were removed? How would demand be displaced if a new channel like mobile were added? Would the change of channel landscape help increase the total revenue? And so forth. These big questions will challenge us to identify and collect the right data, but the resulting data analytics will help us move closer to the RM objective.

Data do not grow by themselves. Their growth is driven by the big questions we can think of. The big analytics based on the big

About The Author
Dr. Jian Wang
VP, Research and Development
The Rainmaker Group


Jian has more than 20 years of experience in designing and implementing mathematical and statistical models for a wide range of industries including engineering, gaming resort, hotel, multifamily housing, airline, car rental and more. As an accomplished practitioner of pricing and revenue management, Jian has published several papers in top journals, has contributed a chapter in a published book, "Revenue Management: A Practical Pricing Perspective," and is also frequently invited to speak at professional conferences and universities.

 
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