Data Analytics and Hotel WiFi
One of the rapidly emerging business trends at the moment is what is known as Big Data. Wikipedia defines Big Data as "an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications".
There are many aspects of yield management where Big Data can be and is being used to benefit hoteliers. Indeed both IHG and Marriott have been quoted as having used Big Data to improve the guest's experience. However, there are also significant benefits to much simpler data analytics. I want to examine some of these benefits with respect to WiFi with a hospitality environment.
As an example I will consider two different types of data sets with which I have worked and some of the inferences that can be drawn from them: gateway based data (where the data is obtained from the gateway used to connect the guest to the internet) and client based data (where the data is obtained from a connection client that the guest uses).
Gateway data should be readily available from all HSIA providers and may also be obtained from wireless controllers / routers in certain situations. It includes the following types of information:
Number of sessions Time session started Length of session Data transferred (both upload and download) Device manufacturer and type Price of purchase In May, Swisscom Hospitality used data such as this to develop the following press release http://www.swisscom.ch/en/business/hospitality/news/guest-data-triples-in-a-year.html which communicated the significant growth in both number of devices and traffic per device connecting to their gateways. Additionally, at HITEC, Eleven Wireless presented such data to a packed and fascinated audience and have since hosted several webinars on the topic of "Optimise and monetise your guest internet"
Hoteliers can use data like this to understand more about their guests and develop appropriate policies as outline in the table below:
Cleary just using the above information it is possible to significantly enhance both the guest experience and the benefit to the hotel simply with some thought around this basic gateway data.
However, if we look at client data analytics, even more data and hence more information is available. In this case I am using client as a generic description for software that is utilised as part of your standard connection. Within the hospitality environment, this would include, among others, an iPass client.
The client device might record significant additional data such as:
Signal strength of the access point to which you are attempting to connect MAC address of the access point to which you are attempting to connect Whether the connection attempt was successful or not Service Set Identifier (SSID) to which you are attempting to connect Wavelength of connection (i.e. 2.4 GHz or 5 GHz) Location This now gives you the ability to obtain even more useful information such as average signal strength for a particular brand, hotel or even access point (AP). It also gives you the opportunity to compare providers or access point manufacturers.
Now our table of possible information has expanded to cover the following:
A pictoral comparison of average signal strength and variability of that signal strength across a group of hotels could result in a scatter plot such as this (I have added the implications).
An owner of a number of different hotels could use information like this, together with usage information to ensure that expenditure is allocated in as cost-effective a manner as possible.
The same sort of analysis and focus could be applied to APs within a hotel (although a longer sample period might be needed) and the information on signal strength and variability used to identify "coverage blackspots" and resolve them quickly rather than try and deal with the rather more troublesome "WiFi coverage was poor" comment on Trip Advisor.
So we can see that with relatively simple data analytics it is possible to:
Set appropriate tiered bandwidth limits for guests Identify hotels in need of WiFi upgrades and prioritise expenditure accordingly Rapidly identify areas of poor coverage within a hotel and address the problem Examine trends in 5 GHz device connection and determine the appropriate time to upgrade to 802.11ac If this is the result of basic data analytics, what might be possible when hotels apply Big Data analysis techniques to their WiFi networks?
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