Empowered renting using smart energy meter data.

At a Glance



GenGame + Loughborough University


Will Downard


User Research, Insight Synthesis, Persona Building, Concept Ideation, Experience Prototyping, Storyboarding, UI Design


January 2021
2 Weeks worth of work
Conducted during UK C-19 Lockdown


"Design a mobile app that utilises smart meter data to reduce the impact of domestic energy consumption"

Initial primary and secondary research data was provided by GenGame and Loughborough University academic studies for this project in order to put higher focus on user experience design in the limited time context.

Hunt Statement

I am going to research recent university graduates’ domestic energy use attitudes and behaviours, focusing on how they have changed due to inadvertently working from home in rented urban houses in the UK, in order to inform the experience design of a mobile app that will allow users to reduce the negative environmental impact of their household energy consumption.

User Research

Remote Interviews

Due to Covid-19 Restrictions, remote video interviews were used with several households that aligned with my hunt statement, these were setup through convenience sampling.

View Interview Guide

Remote House Tours

The semi-structured interviews involved remote video tours of the participants households, where dynamic questioning was used to investigate users energy habits and specific setups.

Interview Reflection Exercises

Part of the household interviews involved reflection techniques in the form of card sorting and self visualisation methods, decisions made by users in the activities then formed the basis of further detailed probes.


"Even knowing how much energy the oven used its not like I'm going to turn it off"

Using affinity mapping, findings from both primary and secondary data were compiled and synthesised to identify patterns. These were then worked up into four clearly defined insights which were then employed to inform a user persona that would form the basis of ideation generation and verification.




Using the generated insights and persona of Jerick, many 'How Might We?' questions were developed. These HMW's were then ranked in a potential matrix and the most promising and persona relevant questions were used to aid sketch ideation of possible concept solutions.

3 Distinct Concepts

Out of the ideation, 3 distinct concepts emerged. These were developed and fleshed out via additional combinational ideation. The concepts were then cross referenced with Jerick's pain points, task and experience goals. The 'Flow Shift' Idea was found to perform the best as well as feeling original in a way the other concepts failed to achieve.

Vision Statement

There is an opportunity, via smart energy meter data to reconnect the incentives of the tenant and landlord in terms of energy efficiency in the home.

By creating a platform that can calculate energy efficiency increases in the home via past smart meter data it could be possible to financially reward the Landlord using the money the user is already spending on wasted energy in the home.

Furthermore the expertise of the user within their homes could be leveraged along with AI to enable a frictionless inspection and installation process.

Key Design Principles


Shift should inspire confidence from the users by its precise and accurate technology that allows users to trust its judgement


Shift should offload as much friction as possible from the user in order to allow them to enjoy the benefits of the service without stress.


Shift should prioritise allowing the user to feel in control of the experience at all times, enabling a frictionless experience that serves the user.  

Experience Prototyping

Following the design principles and vision statement created through ideation, initial user experience journeys were developed for the concept. These was then tested via 'quick and dirty' remote experience prototyping with one household I had interviewed earlier in the research phase.


The first part of the journey requires users to explore their homes, looking for key parts that could be upgraded in efficiency. This was prototyped using the stock iOS measure app to simulate the scanning section of the app. Users were provided a guide of what specific parts of their house to investigate.


Users then simulated uploading the photos and scans they had taken of key features in their home. They were provided digital feedback of elements that could be upgraded and asked to book house upgrade appointments.


The final part of the user journey involved testing how the user would prepare their home for the installations taking place. This was prototyped by informing users of what upgrades were planned to take place and seeing how they would temporarily adapt their homes in order to facilitate this.


Empowered renting using smart energy meter data.

Shift enables renters to save money on their energy bill by splitting free energy savings with their landlord.

AI enables smart meter data to be used to calculate the efficacy of upgrades on renters energy usage, providing savings for the tenant, additional income for the landlord and energy savings for the planet.

User Journey

Key Features

Scan & Map

The app allows users to scan and map parts of their home to find out if it is worth being upgraded by more efficient equipment. Scanned areas can range anywhere from old lightbulbs to loft insulation.


The users data is then remotely analysed and a comprehensive home diagnosis will be sent to the tenants and landlords.


Users have control and freedom within the app to schedule upgrades to their home that the landlord has signed off. This allows renovations to fit around the tenants lifestyle and schedule.


Landlords have access to an in app local marketplace that allows them to easily contract out specific upgrade work for their tenant's property. The app also aids with applying for UK government green grants to aid financing the renovations.

AI Level Prediction

The users previous and future smart energy data is then used by the Shift AI to calculate the level of efficiency in energy usage gained.

Fee Shift

The AI efficiency level prediction is then used to split the users' regular payments between the landlord, energy provider and user savings. This provides incentive for the landlord to ensure upgrades are as worthwhile as possible.

Data Flow