This product focused on using Avast existing AI performance technology to empower CCleaner's users, with the aim of enhancing users' computer device performance and health.
Workshops, competitor analysis, interviews, user testing, prototyping, wireframing, project management for UXD, analytics
Figma, Miro, Usability Hub, Microsoft BI
Senior UX Designer (Me), User Researcher, Product Designer, UX Writer, Developers x4, Project Manager, Data Analyst
With people using their computers for multiple takes over a long period of time, the performance of a device can decrease. How might we help a device maintain optimal performance overtime and retain battery life over a long period of time.
The design team decided to use the 'Double Diamond' process, which is a service design methodology. This process helped us to structure the steps we would take as a team to work on the project. However, there are adjustments to the process, which is having research as the foundation. By having research as a foundation, it allowed us to utilise the practice throughout the project, Whereby strengthening the importance of user centred design.
Working with the Project Manager to plan and create a brief for the UXD members working on the Performance Optimizer project.
Collaborating with team members from various specialty backgrounds i.e. developers, data analysts, project manager, designers and researchers.
Analysing existing products from Avast and competitors, creating user personas to find out their pain points, wants and needs. Brainstorming ideas for future exploration and development opportunities.
Collaborating with team members from various specialty backgrounds i.e. developers, data analysts, project manager, designers and researchers.
Analysing existing products from Avast and competitors, creating user personas to find out their pain points, wants and needs. Brainstorming ideas for future exploration and development opportunities.
With the service blueprint the team and I decided on key areas for development. This gave me focus on what low-fi wireframes needed to be created first for user testing.
By using teaming up with the User Researcher we used Usability Hub to gain general and specific insights on performance based products. The insights were quite difficult to gather due to our lack of resources to recruit a diverse range of users. However, the insights provided a good starting point.
In collaboration with the Avast 'Segmentation', who overlook the core user Research for Avast, we were able to work together to analyse existing data to create user segments for CCleaner. With the user segments the 'Performance' team were able focus on our core user type and help us to decide and prioritise certain features for MVP release.
Discovery User test - User comment
With the user insights and competitor analysis we were able to create areas of interests by separating ideas the affinity mapping technique.
To get a whole view of the product a service blueprint was create to sections and to setup milestones for development, I created
I organised workshops with other designers to ideate new design solutions based on user feedback and data collected from the user testing. Each workshop focused on different sections from the service blueprint
With the service blueprint and insights from the first user testing session the team and I decided on key areas for development. This gave us focus on what low-fi wireframes needed improvements for further user testing.
From insights gathered from CCleaner's previous product release 'Driver Updater', we saw an opportunity to improve how we provide narrative and knowledge to new and existing users through UX copy. We collaborated with a newly hired UX Copy Writer to create copy throughout all the sections highlighted in the service blueprint. Some of the sections were, Onboarding, Tour (How to use), Program list (Homepage) and more.
In collaboration with the User Research, we were able to construct a user testing script and hypothesis. The low-fi prototype was able to be used to gather insights for user experience and interactions.
An analysis and report were created to see how well the results of the user testing were to clarify if the hypothesis was able to answered. If the hypothesis was not answered, alterations to the prototype and another test would be organised.
Insights from the user testing and guidance will be shared with the team. I collaborated with the Product Designers by organising further workshops to ideate new designs and alter the user flows based on feedback. These changes were used for further testing.
A range of user tests were done with further changes to the design based on user feedback. We tested the general aspects of performance enhancement, the user flow, the design/banding, onboarding, tour (How to use), FAQ feature and copy writing.
User testing: Tour (How to use) - User comment
Taking the opportunity to expand design beyond on product, My team and I took the opportunity to create 3 design patterns which could be used within the existing desktop application ecosystem or beyond. We created design pattern documentation for 'onboarding', 'Tour (How to use)' and 'FAQs'. Since each design pattern were user tested and iterated on, they were used as a foundation to provide guidance and a foundation for other teams to use.
To improve the user experience after post release data points were added to the service blueprint and wireframes. The data points were shared with the Data Analyst, who setup Microsoft BI for data collection to analyse users' interaction with the product during MVP release.
We want our products and services to be used by everyone. Therefore, it is our mission to make our product accessible by meeting colour compliancy, screen reader compliancy, hardware input options i.e. Keyboard and multiple language options. To achieve this we create a central accessibility document and work with other specialist team members to enhance our accessibility options for our product.
On June, the MVP of Performance Optimizer was released to the public as CCleaner 6.0, a major release. The feedback was very positive, as users gave the product an average of 4.3 out of 5 for overall satisfaction.
MVP Survey Monkey - User Comment
By collaborating with he data analyst, key data points were able to be collected during the MVP release (6.0). We were able to see the amount traffic to each user flow section, successful completion rates, failure rates and much more. The data help the team to decide on future areas of the product to focus on, select successful element to be used as design patterns to work towards a unified design experience for the overall product; CCleaner.
Sale figures
Mircosoft BI and Survey Monkey
Based on the post-release user testing and data collected we are working a 'New home screen layout' and copy improvements. With these changes we can further enhance the frontend user experience.
Some technical issues were highlighted through a survey and user testing results. These findings were shared with the development team who are working on technical improvements.
It was a pleasure to have the opportunity to work with a team with a diverse range of skills, which helped to provide a wide range of prospectives and dynamics to produce a product that helps users. I enjoyed leading the design team to setup a strategy together from the beginning to highlight our milestones and KPIs to measure our progress and help share our progress with the whole team.
With the limited amount of time and producing a new product did bring its challenges with communication and sometimes organisation, but with our regular catch ups and retrospectives we were able to find solutions to solve the issues.
If I had the opportunity to do this project again, I would try to find more opportunities to work with Avast (parent company) design team to help us share more knowledge and to find ways to help them improve their product, which will help improve our product in future releases.