I was discussing the challenges of interviewing people as part of a course on ‘sustainability leadership’ with my supervisor, Dr Jonathan Chapman, Professor of Sustainable Design at the University of Brighton. I told him how my experience was that conversations get locked down in a certain way by being framed within sustainability, and I felt that people became more concerned with giving ‘good’ or ‘ethically correct’ answers rather than just saying what they think. I also told him how I would like to ‘give something back’ as part of the interview process, even if that was only a case of making the half hour interesting rather than boring. Dr Chapman suggested using personas as the basis for the interviews.
I thought that using personas was a great idea. It would provide the necessary evidential underpinning to allow the interview to be quite unstructured, which I felt, given my intended participant group (leaders and UX designers in energy monitoring businesses) would provide a much more rich output. I read what Martin Maguire said about the use of personas in human-centred design and inclusive design (Maguire, 2001 and Marshall et al 2013). I reflected more on the framework for understanding energy cultures articulated by (Stephenson et al., 2010) as ‘patterns of norms, practices and/or material culture.’ Then I re-read my collected literature, reviewed my analysis of the public data on energy and technology use (broken down by housing tenure, or by age and sex). Finally I took myself off to the National Theatre to be in a creative environment and wrote my personas: the ‘unusual users’ who were thinking about buying energy monitors: Tamsin, David, Rebecca and their respective households.
The ‘unusual users’ I created were based on: older people who are not highly technology literate (Tamsin); young people who are technology literate but live in a complex household energy culture (David), and a family with young children (Rebecca).
Each of those cases could represent an opportunity for energy monitor providers to increase market share or create deeper and richer use experiences. It will be interesting to see at the end which user case is of widest interest.
I was quite concerned that I had not based the personas on my own research with real users. However the Marshall (2013) paper discussed the value of using datasets as a proxy for real users because of the shortness of time of much research and recognised the value of using existing research. Further my main focus for the study was (in line with the CISL focus on business leadership) on finding out how the businesses producing energy monitoring systems would react to ‘unusual users’. I was also very fortunate to build on amazingly rich research such as the ethnographic study on energy use carried out by Lockton, (2014) which described the voices of real people talking about energy monitors. Other studies that helped enormously were by Hargreaves et al., (2010) looking at householders in East Anglia and their energy usage, and Fell and Chiu, (2014) who studied children and parents interacting around energy use. Further, it would be complicated to identify and access householders who might potentially be interested in energy usage. I tried to piggy back on a few locally organised energy events (an energy efficiency advice session), and a focus group being carried out by a not-for-profit group by my local council. These organisations were willing to let me come along and promote my research, in the hope of attracting participants, but the numbers interested were too small to enable me to have anything like a representative sample of users in my interest groups. So after a lot of swithering, I felt it was more valid to have based on my personas on the work of researchers who have gone down this road before, and draw these to the attention of the energy monitor businesses.
People who are not in the habit of using internet-enabled technology
This persona is significant because of early mapping and conversations with the energy monitoring and smart meter industry, and their emphasis on using internet enabled devices (desktop computers, laptops, tablets and smartphones) as the user-interface, as well as or instead of a standalone display unit. Early conversations and media monitoring showed that accessing the energy data via an app was seen as the direction of travel in the industry.
This led to a concern for whether design would facilitate use for people who are less likely to use the internet. Government and other publicly available data demonstrated that these people were likely to be older people, or people who lived in social housing (the social housing group also had lower incomes than other tenure types).
Children and parents together
Reviewing how people access the internet, or receive data more broadly also led me to two other ways of accessing data, of which computer games, smart TVs and wearables such as the Fitbit were important for some consumers. The population accessing the internet via computer games and smart TVs skews to the younger age group. The Fell and Chiu (2014) study made it clear that children could grasp the concept of the energy monitor (they could observe a spike in the numbers, even if – probably like many adults – they did not know what units the numbers signified). So I became interested in the unusual use case of the children and young people for whom apps may only be one way of acquiring information, and sensory signals (such as the buzz of the Fitbit or a computer game console) provide a possible design opportunity, as well as social media and gamification principles.
Sharers of a home who are not related to each other
Reviewing how people access the internet, or receive data more broadly also led me to two other ways of accessing data, of which computer games, smart TVs and wearables such as the Fitbit were important for some consumers. The population accessing the internet via computer games and smart TVs skews to the younger age group.
Building on the Stephenson work in energy cultures, I was interested in the difference between households where this a presumed bill-payer and decision-maker, and houses with more complex cultures where there is no such authority. Some tech companies have emerged to serve such markets, eg www.locatable.com who make an ‘app that’s the best way to split bills and track costs with your housemates!’ and whose slogan is ‘we build stuff to make life a little more awesome at home’. They don’t make energy monitors but the app does split utility bills.
Giving my personas a material context
I gave my personas a physical environment, i.e. a home and a social and technological environment depending on what the public data indicated was a statistically likely tenure type and income bracket.
Reviewing the literature on energy usage, I could see that studies of people living in rented housing (whether privately rented at market rates, or rented from a local authority or social landlord) were also relatively scarce. These property types have different energy efficiency characteristics. Private rented households are over-represented amongst the most energy inefficient households compared to other housing types, but renters may have little control over the fabric of the house.
Social housing is in fact disproportionately more energy efficient, and several social housing providers are carrying out interesting experiments with ‘connected homes’ using internet of things technology. This is partly to renew their own business model: providing opportunities to help their tenants (e.g. to manage energy bills) but potentially to protect vulnerable tenants. I heard Property Tectonics speak at a conference about the possibility of spotting that an elderly tenant had not used any energy in cold weather, and being able to go in and check that they are okay.
I layered these different factors into the personas. I did not have one all-singing all-dancing dataset underpinning the personas but each sentence represented a data-point. I felt confident that they were ‘data-rich’ as opposed to ‘assumption-based’, to use the terminology from Marshall (2013). My expectation is that different energy monitor makers will have different angles, and not every persona will be equally interesting to every company. For instance, some might see their product as having something particular to say to the technology use angle, the child/parent dynamics angle, or the non-family household angle.
Incidentally my one area where I diverged from Maguire on the use of personas was that I have not provided a visual picture. This was due to my ethical concern about widely sharing pictures of real children in the Rebecca persona, as it would have taken another chunk of time to explain to the children and parents how the picture would be used and to get their permission.
Hopefully the personas will produce some creative findings and lead to renewed interest in unusual users – in my dreamworld one firm will be inspired to co-design a product with children, and we’ll see children being really engaged as active and conscious consumers in their own homes. But that’s another whole study….
One thought on “Using personas as a research tool”
Interesting to read about personas, or fictional characters, in your research. Shlomo Goltz in his blog (https://www.smashingmagazine.com/2014/08/a-closer-look-at-personas-part-1) defines it as follows: “A persona is a way to model, summarize and communicate research about people who have been observed or researched in some way. A persona is depicted as a specific person but is not a real individual; rather, it is synthesized from observations of many people. Each persona represents a significant portion of people in the real world and enables the designer to focus on a manageable and memorable cast of characters, instead of focusing on thousands of individuals. Personas aid designers to create different designs for different kinds of people and to design for a specific somebody, rather than a generic everybody.”
My guess is that it would take quite a bit of time and research to create credible fictional characters. While I find your approach fascination, you would have to explain ‘how’ you arrived at the various personas used in your research.