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We’re hardly out of the woods with COVID-19, and that means many properties will have to make do with a customer base mostly derived from local leisure, staycations and workcations from drive-to markets. With fewer overall guests, outside of cost savings efforts we must simultaneously look at maximizing the revenue per available guest (RevPAG), and there’s no better way to go about this than by sharpening your use of the PMS.

This is the last issue of Siegel Sez before this year’s CYBER HITEC event. HITEC is an event I have not missed in 30 years, and historically it has always been a great place to find innovation.

Toxicity Kills
Posted: 10/07/2020

It doesn’t matter if it is toxins in your physical environment or toxins in your mental environment. This stuff kills! 

It’s said that when someone’s mindset shifts, everything around them can change at the same time, and in our current setting, the importance of being in the right headspace, both personally and as an organization, can’t be discussed enough.

In my last installment, I introduced four areas of hospitality technology that I believe have been significantly changed by COVID-19. I covered contactless technologies in depth in that first article. This week I will turn to the other three areas: social distancing; health and sanitation; and communications.



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Big Data Analytics Needs Big Questions

12/02/2014
by Jian Wang

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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