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

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 guest data triples in a year 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:

Data obtained Information derived Usage
Time of session Guest usage patterns Manpower planning for support
Volume of data transferred Historic growth in usage of WiFi within the hotel Capacity planning for data lines
  Proportion of guests who transfer given volume of data Setting volume levels for tiered access
  Upload and download data for the hotel Determining the (lack of) appropriateness of asynchronous data lines
Device type & manufacturer Understanding of proportion of handheld devices connecting to the network Setting signal strength standards to ensure weaker mobile devices are also able to connect and determining the number of connections to be permitted per “purchase”
  Knowing the manufacturer of devices connected to the network Ensuring that support understand more than just Windows support methodology
Price paid Usage levels at different price points (including free) Setting the optimal price points to fit with the brand’s standards

Clearly 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.

Client 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:


Data obtained Information derived Usage
Various signal strengths experienced for a given hotel Average signal strength and variability for given hotel Prioritise investment across a group of hotels
Various signal strengths for a given AP within a hotel Average signal strength and variability for a given AP Identification and resolution of areas of poor coverage
Connection success rates Connection success at various signal strengths Confirming correct signal strength standards have been set
Wavelength of connection Proportion of 5 GHz connections Determine timing of upgrade to 802.11ac


A pictorial 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?


Graeme has assisted both Eleven Wireless and iPass in working out how best to provide and utilise data analytics for the hospitality industry