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Definitely Doug 10/18/19
Posted: 12/06/2019

Sustainable Innovation
 
Sustainability can yield multiple benefits to hotels. Saving energy and water yields direct cost savings. Revenue can be generated by guests who prefer to deal with businesses that minimize their environmental impact. And many would argue that conserving scarce resources is simply the right thing to do.

Definitely Doug 12/6/19
Posted: 12/06/2019

Meetings Innovation
 
The sale and delivery of groups and meetings is perhaps the most significant and under-automated functions for many hotels. Even though groups often account for 30% to 60% of revenue, most group bookings are still handled manually for most if not all of steps, as they move from a meeting planner’s research to a confirmed booking.

The biggest enemy to any system is complexity. In a system of inputs and outputs, such as an enterprise system, more complexity means more parts are used in interaction with inputs to create the outputs. Every part that must be built and maintained costs time and money

Tracking the evolution of key performance indicators (KPIs) over time allows hoteliers to identify meaningful trends, create forecasts and budgets and assess the results of different strategies. To perform this kind of analysis, data has to be recorded within consistent time intervals and in chronological order. This is known as a time series.

Definitely Doug 11/15/19
Posted: 11/15/2019

Every time I turn around these days, I see a new vendor or product promising something called a complete Guest Experience Management, Guest Journey Management, or Guest Engagement (or some variation on those words). This week I looked at some of the emerging products claiming to be in this space, both to try to better understand it, and to see what promising ideas it may hold.



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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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