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Using competitive and comparative analysis to see how Identity Guard measured up to other identity protection services and how IBM Watson matched up to other AI leveraging platforms.
Through screener surveys and conducting user interviews, we assessed the habits of both users who are currently proactive in their money management and online identity protection habits and those who are not.
Market research and user research were both used to conduct a feature prioritization and draft a medium fidelity solution to solve the user's problem.
Usability tests were conducted on the first iterations of a medium fidelity solution to further refine features and produce a final product.
Upon conducting a competitive analysis, it was clear that what Identity Guard had over their competitors was their use of AI via IBM Watson. However, upon conducting a comparative analysis and comparing the platform’s use of IBM Watson to services like Siri and Bank of America’s Erica, it also became clear the company was not using their AI in an engaging manner. The user entered extensive personal information during the on-boarding process, and simply received notifications fueled by AI from then on. As long as the user remained safe from identity theft, there was no reason for the user to see the app as a necessary part of their routine.
A heuristic evaluation allowed us to analyze the app’s flaws in regards to UI and sort them in order of severity.
Information was not presented effectively as it did not provide the user with the “why”. Additionally, the navigation was not organized or labeled intuitively and iconography and color were not used effectively. The style of iconography was almost too simple and did not have a clear relationship with the accompanying words and the little color that was used was only used to indicate navigation.
Using a brand prism, we examined the brand identity and found that aside from AI, the brand colors were a main thing that made the brand stand out against its competitors. We suspected the brand colors could be used to convey a feeling of friendliness and make the main features of the app feel less serious for the user; but we wouldn’t know this for sure until we conducted our user research.
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Keeping the above issues in mind, user research was conducted with two main objectives:
User interviews were conducted to expand on these topics and dive into the habits of individual users. My team conducted the interviews virtually via zoom due to the pandemic, but this allowed us to interview users from several different countries.
While common themes in responses were apparent immediately upon completing interviews, my team engaged in affinity mapping, a process used to synthesize user interviews and organize commonalities amongst users, to synthesize these themes in a granular way. This allowed us to come away with some key feelings users expressed:
"I have concerns about AI"
"I wish I knew more about financial literacy and online security"
"I have poor password management habits"
Overall, most interviewees had mixed feelings regarding AI, didn't know where to begin to find resources to improve their financial literacy and online security, and felt they had poor password management and online security habits.
It was apparent that those who considered themselves proactive in identity theft protection and those who did not had similar habits and therefore would not require a drastically different app experience. We knew through our affinity mapping that users overall had mixed feelings about AI due to privacy and the lack of a personalized experience, but that they did appreciate when it felt as if they were speaking to a real person.
We also knew that users wanted the app to have an interactive, learning component as a result of feeling as if products similar to Identity Guard are constantly throwing information at them with no context or substance. Excited to hit the ground running with many initial ideas in mind, I was tasked with creating one specified user persona to keep the team focused on key user needs and streamline the design process.
Our research informed a solid problem statement for the team to reference throughout the design process:
“Tessa, who feels discouraged about buying a home with her fiancé, needs to proactively improve her credit score and spending habits, but faces feeling lost in regards to finding the tools to help her.”
Keeping this statement in mind, our team began asking ourselves, how might we solve Tessa’s problem?
When asking ourselves, how we might solve Tessa’s problem, we had three top considerations when conceptualizing proposed solutions:
Keeping our top considerations for Tessa in mind, we conducted a feature prioritization using the MoSCoW method to identify must have, should have, could have, and will not have features. One thing we did know is that we wanted to make Watson an interactive caricature. Given our research data, we felt this would be the most effective way to incorporate the AI while meeting the user need for education and a feeling of security.
A user flow was then created in order to provide a visual, step-by-step representation of how the user would interact with Watson and the other main features of the app.
The team began sketching separately in order to ideate solutions for the final design.
We reviewed these solutions as a team and combined the best components of each to create medium fidelity, graybox wireframes using Figma.
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Once our wireframes were finalized, an interactive prototype was created in order to conduct usability tests in order to gauge how users felt about the new features and layout of the app. I conducted usability tests, giving each user a series of tasks and making note of how long each task took, mistakes made or things that confused each user while clicking through the prototype, and their general thoughts on each screen and the prototype overall. After organizing these findings into a detailed report for my teammates, I proposed which features needed to be tweaked, eliminated, or added, in addition to small UI or copy changes.
As a team, we iterated on the findings from the usability tests and produced the final product. Unlike the medium fidelity wireframes, the final solution included the following changes:
Overall, users liked the dashboard page and Watson. The usability test feedback was used to inform which user-specific data Watson would offer additional information on as the user moved through the app, presenting the data in a more educational, informative way.
It would be beneficial for future iterations of this app to leverage user data combined over time. This information can be used to provide even more detailed reports and even possibly allow Watson to provide recommendations, as opposed to just presenting data and answering questions.
For someone like our user, Tessa, it would also be great if future iterations of this app included a feature for merging Identity Guard accounts, allowing users to see a breakdown of combined and individual spending in a household.
Additionally, this was my first time working on a design solution with a team and it is apparent that our ability to communicate efficiently, candidly discuss and leverage every member's strengths and weaknesses, and work both individually and collaboratively on tasks all allowed us to create and present a design we felt successfully addressed both user and business needs.