GENGAME.
Making domestic energy reduction simple and creating positive social impacts by using smart meter data.
MY ROLE: Individual project.
Customers using want to reduce domestic energy use, to contribute positively towards global warming but they also want to enjoy the quality of living they've become accustomed to. They want an easy way to reduce energy wastage without compromising the things they enjoy.
Who are the users?
Middle class privileged families, who enjoy living comfortably and are interested in new technology.
Why should you care?
Global warming is an increasingly pressing issue affecting everyone. By making it easier for everyone to play their part, we can slow the effects of climate change without reducing people's ability to enjoy their lives.
How did i fix it?
Created an app to gamify energy reduction by linking it to smart meter data to help automate the process. By reaching saving goals, money is donated to worth causes of user's choice.
Minimised effort needed yet maximised global and social positive impacts.
Where's the proof my solution worked?
User feedback was positive, SUS scale rating went from 52.5 (using current methods to save energy) to 70.0 (using app).
The problem space
The brief.
How might we design a mobile app that utilises smart meter data to reduce the negative impact of domestic energy consumption and positively engage households with climate action?
For this project, a research pack was provided with archetypes, personas and conducted primary research with users.
Exploring the problem space.
Smart meters.
Chosen target users.
Based on the archetypes and personas provided, the chosen user group is a middle-class, privileged family who are used to a comfortable lifestyle.
They are already well informed about climate change and feel a sense of moral need to contribute towards solving the issue.
They are early adopters of new technology and climate changing investments such as solar panels.
Journey map.
Creating a journey map of the key user (the father of the family) who has a keen interest in new technology and likes to invest in green measures.
COM-B model.
Looking at behavioural obstacles helped to identify where there may be opportunities for a possible solution.
Secondary research.
Using the IBM assumptions toolkit, assumptions were mapped and then prioritised into an assumptions priority matrix.
Secondary research was used to validate assumptions before progressing further.
Proto personas.
To create more empathy with the user, a proto persona of the key user was created to understand their goals and pain points.
Insights gathered.
All key findings were drawn out from the research pack and a laddering technique was used to pull out the deeper meaning behind the data and helped to develop these insights.
User needs statement.
As a busy father of two, he needs to find an achievable, realistic way to reduce energy wastage without sacrificing his comfortable lifestyle, so that he can contribute positively towards climate change and feel hopeful that he is helping to provide a better future for his daughters.
UX vision statement.
There is an opportunity to design a mobile app for a busy, tech loving family who want to positively engage in climate action and reduce their household energy wastage but still continue to use the tech they love when they want to.
Design phase
Design principles.
Engaging.
It makes energy saving interesting and insightful, without people feeling judged or forced.
Responsive
It adapts and learns from the family’s behaviour, only providing suggestions which are tailored to their habits
Community centred.
The core mission is to get more people involved in adapting better behaviours by putting more back into the community.
Ideating key features.
App features were brainstormed based on the user persona, design principles, intended outcomes and insights gathered.
The MoSCoW method was then used to prioritise the generated features.
The insights, user needs/goals, business goals and barriers to change from the Com-B model were used to then prioritise the app features.
Incorporating AI.
AI can be used in various ways to encourage more sustainable energy usage using the historic smart meter data. It has been shown to be successful in energy reduction by automating energy usage in numerous sectors.
For this app, the main usage will be to suggest personalised and realistic energy saving measures for this specific user, based on their past use and smart appliance data.
To determine to value of using AI to do this, a confusion matrix was generated.
Initial sitemap.
A user story was created to help focus the ideation of the design and the flow to focus on.
Now that the features have been defined as well as the context of use, we begin to determine the layout and flow of the app.
A simple site map was created based on the screens that were deemed as necessary.
Storyboard.
The storyboard was created to help visualise the product and the context of use, and identify possible issues before prototyping begins
This is heavily based on the assumptions made so far and the research pack. This can be later refined with further testing.
User (data) flow.
The chosen pathway from the sitemap was then developed into a user flow to visualise how the user might navigate through the app.
Through this flow, the user sees the personalised energy saving suggestion. This has been generated via AI using machine learning by assessing the historic smart meter data and smart appliance data (which can be linked to the app). The resulting suggestion should then be specifically targeted to the user and also realistic to the user’s lifestyle.
Once the suggestion has been completed, the data should reflect the changes and the user will see that they have completed the task on the following day.
Lo-fi prototyping
Lo-fi user testing.
For the user testing, a low fidelity version of the prototype was created. The screens were initially hand drawn individually onto paper.
The screens were then photographed and uploaded into Marvel to carry out ‘paper in screen’ prototyping.
The first stage of testing was to see if users agreed with the layout of the flow and whether any changes would be needed.
Mid-fi prototyping
Key findings from user testing.
- Some type of notification was needed to prompt app use.
- Some level of personalisation would be good, i.e. including the user’s name somewhere.
- The user liked the idea of a community reward but felt that the reward should be tailored to her in some way.
-The user didn’t like having the daily task on the home screen as it felt like it was a forced task rather than something to look at when she had time.
- More graphics are needed to make it more entertaining, as energy saving isn’t an interesting topic by itself.
-The user liked the idea of connecting smart appliances to the app and felt it would be helpful to automate some of these processes.
Validating assumptions.
The prototype was based on some assumptions that need to be validated and tested further. If given more time, this would be imperative before re-iterating the prototype and further testing.
Opportunities for improvement.
Providing the user with a choice of community goals so they can pick the one that motivates them the most.
Having a personal assistant to ask questions to help refine the suggestions and automate settings.
Create some type of family engagement or event through the app.
mirunaalini.alagarajah@gmail.com