Developing our understanding of audience behaviour is a key to UX research and statistical data plays an interesting role in helping the team ask the right questions.
As a UX researcher I read through a lot of industry data, relaying it back to our UX team in a more digestible form. One of the many interesting graphs that I have seen lately comes from the BBC iPlayer Performance Pack for January 2014, illustrating the average daily peak for both live TV broadcast viewing and BBC iPlayer usage.
Sources: BBC iStats, 2014; BARB, 2014; Nielsen, 2012
While the number of iPlayer users has increased year-on-year, the shape of this graph has changed very little, showing a consistent trend in peak viewing for both TV and iPlayer usage.
Data such as this provides excellent food for thought for our UX team, allowing us to identify patterns in behaviour and start to formulate questions and hypotheses around the reasons for these behaviours. This provides a foundation for our UX research, giving us a tangible line of inquiry. Pursuing this, we take a step closer to uncovering the answers to that all important question - why? I asked some of the team at Ostmodern for their thoughts on this graph to see if we could start to unravel what exactly the data is telling us, developing hypotheses from our own experiences as VOD professionals.
A key point of discussion was the way the iPlayer usage peak and drop off curve lags almost exactly an hour behind live TV, suggesting the possibility that catch up services may extend the live window of TV viewing in the evening. This trend hasn’t gone unnoticed by the BBC, who are planning to launch their own timeshifted channel BBC One + 1.
Ostmodern’s product analyst Yoav Farbey makes these observations: “It would be interesting to know where viewers are watching iPlayer, and on what device. An assumption can be made from looking at that data is that people are watching iPlayer away from their TV on laptops and mobile devices before they go to sleep. Another great thing to find out is what shows the viewers are watching at that peak time – are they watching shows that were on air a few hours ago, or shows that were from earlier in the week.”
As Yoav's comments highlight, analysing statistical data only scratches at the surface of understanding how TV and video viewing behaviours are changing with developments in VOD and catch-up platforms. While this analysis does provide some useful hypotheses for us to explore through deeper research, it is not possible to draw definite conclusions from this alone. As Yoav points out "many assumptions can be made from this graph, but they cannot be proven without more granular information”.
To answer these questions fully, we need to develop our lines of enquiry. Complementing statistical data with some deeper qualitative research, such as a diary study or contextual interviews with users, we can build up a clearer picture of people’s engagement with the service and how this fits in with their lifestyle. From this, we gain an insight into the goals, barriers and triggers of VOD and catch-up service users, which in turn informs how we can design for their needs.
The value in analysing statistical data, such as the iPlayer graph, is that it allows us to formulate a set of questions around which to base our research. Using a combination of qualitative methods to find the answers, we can identify the underlying causes of the behaviours which, for us as digital product designers, provides key insights which enable us to create products which are both intuitive and relevant to the user’s lifestyle.