By Bülent Çelebi, Executive Chairman and Co-Founder, AirTies
The growing dependence on Wi-Fi in the home as the new “last mile†for broadband services is becoming better understood by operators and their technology providers. Less well known is the crucial role big data can play in ensuring this new access layer delivers the best quality of experience to end users accessing services on wireless devices around the home. Big data can score on two counts, firstly for monitoring the immediate performance of the Wi-Fi network itself and secondly to gain valuable information about usage broken down in various ways, such as by user, service or location within the home. Such usage information itself can help improve performance by identifying black spots with a poor signal but can also enable operators to improve the overlying broadband or TV service in a number of ways, such as targeting individual users within the home.
Big Data then has the potential to improve the overall broadband experience over Wi-Fi, augmenting core technologies such as AirTies’ wireless mesh and client steering that address Wi-Fi performance and home coverage directly. Some of the data that can be collected does indeed relate directly to performance, such as monitoring interference levels that may be associated with nearby Access Points in neighbouring networks.
There is the potential to identify issues specific to each home that are causing intermittent performance degradation. The key point here is that many Wi-Fi performance issues are circumstantial or transitory, arising for example when somebody takes a tablet to the bathroom and starts watching Netflix there. These circumstances change constantly but by capturing traffic data over a long time frame such as a week, recurring usage patterns may be identified that help diagnose corrective actions. This might involve deploying a new Access Point near the bathroom for example.
This in turn can help bare down on churn and also increase use of Wi-Fi data, which can boost revenues. A recent survey in the UK found that as many as 40% of residents with Wi-Fi linked to their fixed broadband were instead using 3G or 4G mobile services to access the Internet, citing slow Wi-Fi speeds and patchy coverage with periodic complete drop outs as the main reasons. This represents lost potential revenue to the broadband operator and should not be happening because Wi-Fi on paper delivers higher speeds and superior quality of service.
The other aspect of Wi-Fi Big Data – monitoring usage patterns – is perhaps even less well appreciated and yet has just as much potential to improve the overall customer experience. As the new final access layer, Wi-Fi, far from obscuring the operator’s view of the end device, actually makes it clearer by being able to monitor each one separately. Until recently broadband providers and pay TV operators had little idea which members of a household were accessing services at a given time. As a result they had to treat a household as a single aggregated entity from the perspective of service consumption, even though this might include children, parents and elderly relatives all with very different usage patterns.
But with a good Wi-Fi cloud data collection platform it is possible to access accurate data consumption information down to individual devices and therefore users. Video consumption over Wi-Fi can be measured and separated by device or by the room in the home. At a time when more and more viewing is via mobile devices such as tablets rather than fixed TVs this is the only way to gain valuable information such as how much viewing is done in the bedroom or kitchen. This can help with targeting of both content and ads, since individual preferences tend to vary with time of day, device and viewing location. If people are viewing in the kitchen early in the morning they may be more receptive to ads for breakfast cereals than when watching in bed at night. By combining time of day with location of a given user’s device, such fine grained targeting becomes feasible and potentially highly profitable.
There is other useful information that can be obtained, such as the number and location of all devices per household that are active at a given time and a breakdown of the types of clients connected in each household. Total numbers of hours connected per household broken down by device and room can also be collected.
Then given further development there is scope for detailed analysis of each traffic type. Above all though operators can segment the household by each member as well as by location and time of day and target both programming content and ads accordingly.
