Time to revisit this journal before I make too much progress and forgot what I have been working on. I have been on a long yet decisive journey, selecting the methods in order to gain the insights that I need in order to provide the foundations and information architecture required for structural design of the future app.
This post provides a rundown of the methods that I have used so far and why I have used them. This is a good place to pause briefly and capture this information as this week represents the cusp where I need to complete the Explore phase and transition to the Creation phase. Otherwise known in this case as the time when I need to start designing some low fidelity prototypes to get the flow right and then swiftly move to the visual design phase.
1 - 27.2.17 Sprint
- Created product backlog and the Scrum-style Trello board. This was a useful way to translate the Lean Business Canvas to actionable user stories. I did realise that since the UX happens before the software development it would not be possible to tackle the user stories until the functional prototype phase (or at least putting the design in place for the software development to truly satisfy the needs of the stories later). Nevertheless it was a good way to capture the top-level requirements.
- I completed some in-person Service Safari's (Stickdorn & Schneider, 2014) in order to understand the experience of buying a Smart Home product in a shop. This was incredibly helpful and has informed my observations since. I completed some verbal journaling for this and took photos (where I was able to do so covertly).
2 - 6.3.17 Sprint
- I write the survey questions thinking carefully about the type of questions to include. SEE the first survey post for more information.
3 - 13.3.17 Sprint
- The survey received 62 responses. Processing the responses took some time and some crafty Excel and Google Sheets formulae magic.
- The data allowed me to identify two main audience segments, those who own smart home devices and those who do not but are interested and are therefore potential customers. I used this principle to order the data into short reports to summarise the key findings from the data.
- Based on my previous project for IDM13 related to trust-based perception of IoT systems, I decided to ask some trust-based questions as trust is an important factor as part of the purchasing process. This information gained for this will inform the structural and aesthetic design of the app.
- Completed the General Characteristics for users of the app. This will be added to the design specifications document later on.
- As 4 out of 5 the folks I was able to arrange interviews with were smart home device owners. I decided to focus on finding out what they had done and where they had encountered frustrations in order to figure out what conceptual spaces the new app could occupy.
- This was inspired by the ethos of Jobs to be Done, but used Ellen de Vries questions as a starting point (de Vries, 2017). I also only had 20mins for each interview so I had to be quite strategic. I really wish that I had found this article from David Wu at Google at that time as it provides a really nice structure and covers many of the questions that I used in the survey, however I also know that all my interviewees had also completed the survey so it might of felt a bit repetitive for them.
- The purpose of the interviews was also to consider what content and information would need to be included in the app.
- The key decision was to focus on providing the try-before-you-buy functions only for the Nest Thermostat to begin with and then for additional design patterns to be added later
4 - 20.3.17 Sprint
- Completed version 1.0 of the characteristics lists for the two audience segments: Potential smart home device owners; Smart home device owners.
- What I realised from the completing the characteristics list was that unlike other projects I have worked on it was hard to pin down specific demographics beyond economic pre-requisites.
- I would normally create personae at this stage but some of the information from the interviews led me to reevaluate this idea. As part of the interviews I had asked people to draw pictures of their ideal smart home setup. This question was designed to find out how interviewees perceived the existence and operation of these devices in their home environments. Several people drew floorplan type drawings which is a good sign for the main functionality of the app. However, it also identified how much their thinking differed and I felt like I needed a more complete picture of the thought processes that the two user groups were engaging in around these devices.
- Enter the decision to create a mental model (Young, 2008). I decided on a mental model, because a goal of the app is to support onboarding, the smart home devices present new usage paradigms that users need to wrap their heads around. Therefore the app needs to have a long-term and clear gauge of the user's mental model as they make decisions related to researching, purchasing and using a device. Just like JTBD the mental model does not change over time but the means to complete the intentions outlined in the model might (Klement, 2016, p. 27; Ulwick, 2016, Ch. 2)
- The mental model provides a framework for the app to sit alongside and will also allow for gaps and pain points to be addressed incrementally across different releases.
- I also realised that both audience segments are potential customers for a thermostat, as they may own other types of devices but not a thermostat.
- This was also the week of UX Camp Brighton, which also made me think that a mental model was the correct solution here. SEE the Skeptics post for a brief summary of the presentations that I felt were relevant to this project.
5 - 27.3.17 Sprint
- I completed the transcription of the final few interviews and then began the long process of the interview summary process as outlined in Mental Models by Indi Young (2008). As it was only myself as opposed to a team job, I used the Rapid Mental Model method (Kalbach, 2016, p. 302). Luckily my interview questions had resulted in answers that were akin to the short stories that are normally required for this method; between the interviews and viewpoints gleaned from the survey I was able to come up with 130 summaries. I suspect that this number will go down through iteration slightly.
- This process took much of the week to complete.
6 - 3.4.17 Sprint
- It took the better part of a week to build the mental model and check it through. I printed the first version on the 10.4.17 and annotated it with changes. It will continue to be a living document throughout this project
- I also concluded a basic competitors analysis as a spreadsheet. This has been going on the back burner since sprint 1 as I could not locate any direct competitors other than say a combination between an instruction manual and floor planning app. At the moment there is not an app or website that I could locate that provides information pertaining to these devices and planning tools, prior to purchase of a device (at which point you get access to the device's app). Therefore I looked at the closest indirect competitors.
- I derived the (closest indirect) competitors from the survey results from questions about trusted retailers and where people would go to buy a smart thermostat.
- I looked at the closest indirect competitors for information, including retailers (online and in-person) as well as the manufacturer site.
- In addition I considered the closest indirect competitors in terms of functionality, so floor planning apps that allow the user to try out furniture.
- The closest competitor is Amikasa (, an app that allows you to plan out furniture in a model of a room in your home and also preview them using your phone's camera and augmented reality.
- Amikasa's profit model is interesting and offers potential for the app that I am designing. Essentially they charge furniture designers and electrical manufacturers to have their device's included in the app. They also provide paid customised versions of the app. That may be the outcome for the MVP as the broader functionality of mapping other types of devices and an entire house full of rooms, adds substantial complexity. More than can be achieved during this project.
Why didn’t I use Jobs-to-Be-Done?
In short I found that there were too many interpretations of how to do JTBD. I really liked the concept behind it, but as I am working with a concept which is already quite convoluted and hard to pin down I decided to follow a more traditional path. Although JTBD is supposed to be good for finding the gaps where they might not be apparent, I found after reading a few interpretations that in this context it didn’t offer me anything that I thought I couldn’t achieve through more traditional methods. I was concerned that I might be adding a level of complexity that might be beyond the scope of this project. As it is with the mental model it is already complex and I could see where mental models and JTBD would have some overlap (in fact I am not the only one who thought this, Kalbach, Padilla, Thapliyal and Kasper (2016) talk about a project where the two were combined in Mapping Experiences(2016)). I did find some validation for this decision partway through a video about JTBD, the video shows the results of a factor analysis for Smart Homes to reveal the core functional jobs. The combination of my survey, interviews, characteristics and the mental model uncovered the same information. So yes, JTBD works, but so it seems do the old ways. I think it is a case of the right combination of methods for the need, in my case I knew I would not be able to get the numbers of responses needed in order to create a factor analysis so I needed to employ my own intuition, informed by market research and my earlier project, instead.
Two final thoughts/considerations
Why do images of Nest Thermostat always look like they were created in Adobe Photoshop? - This does not inspire my trust in the device as a real object. I will consider this more in the design later.
In the interviews the devices that talk become anthropomophised e.g. she tests herself. I need to think about this with regard to Hone, is the app like Jeeves, Clippy or something new. Does it have a human or anthropomophised touch or is it like Amikasa where humans are completely absent. Something to think about. This made me think about the section Machines That Induce Emotion in People in Norman’s Emotional Design (2005, pp. 188-191). Specifically, the story of Eliza, a programme developed at MIT by Joseph Weizenbaum in the 1960s (p. 189). Eliza was programmed to respond to patterns of text typed in by humans with replies that repeated parts of what they had written to form a reply, humans, being humans read perceived emotional depth to the answers. Even though of course there was none!
de Vries, E. (2017). Elevator pitch [PowerPoint slides]. Retrieved from https://studentcentral.brighton.ac.uk/bbcswebdav/pid-2877935-dt-content-rid-5404586_1/courses/IDM22_2016/Elevator%20Pitch.pdf
INDG Digitale Communicatie B.V. (2017). Amikasa (Version 2.7) [Mobile application software]. Retrieved from https://itunes.apple.com/us/app/amikasa-3d-floor-planner-with-augmented-reality/id918067772?mt=8
Kalbach, J. (2016). Mapping experiences. Sebastopol, CA, USA: O'Reilly Media.
Kalbach, J., Padilla, J., Thapliyal, E., & Kasper, R. (2016) Indentifying opportunities: Combining mental model diagrams and Jobs to Be Done. In J. Kalbach, Mapping Experiences (pp. 39-44). Sebastopol, CA, USA: O'Reilly Media.
Klement, A. (2016). When coffee & kale compete. Retrieved from https://static1.squarespace.com/static/572e411322482e952aaee764/t/58ea7b9e725e255182f59a77/1491762089597/When+Coffee+and+Kale+Compete.pdf
Norman, D. A. (2005). Emotional design, Why we love (or hate) everyday things. New York, NY, USA: Basic Books.
Stickdorn M., & Schneider, J. (Eds.). (2014). This is service design thinking. The Netherlands: BIS Publishers.
Ulwick, A. W. (2016). Jobs to be Done: Theory to Practice [Kindle edition]. Retrieved from http://amzn.eu/7BpXFp0
Young, I. (2008). Mental models, aligning design strategy with human behaviour.. Brooklyn, NY, USA: Rosenfeld Media.