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Project Background
Conceptual Case Study
Duration
3 weeks
The Company
Recogneo is a new IT consulting firm specializing in enterprise application software. How might we design a solution to aid in prioritizing feature requests to improve collaboration and productivity across various teams?
One of the main problems with Recogneo's platform is the handling of feature requests. Typically, feature requests from users are handled through a web form and pushed into a table for consideration for each development cycle.
The problem is that this outdated method of handling requests results in it becoming increasingly difficult for the team to have an overview of what kind of features are being requested.
This results in:
Morever, Recogneo is beginning to take advantage of ML (Machine-Learning) technologies and AI, taking advantage of the predictive capabilities of emerging technology to project future trends to help the company scale sustainably and to adapt quickly to current market trends.
It is important to understand how the company currently handles feature requests.
The external application that Recogneo uses simply takes the highest voted features (by customers) into a backlog for consideration without being able to oversee feature request trends.
There is a little to no external analysis of rates of implementation for requests over time, which category of requests are highly needed, and the number of requests over time. This results in an over-simplification regarding which requests may make the greatest positive impact on the company.
The company has been using external ticket management tools and other forms of documentation to keep up with creating upcoming features. The team realized that this quickly gets out of hand as documents are scattered in the file system and are rarely updated (causing conflicts and confusion for future team members).
In this case study, we will be focusing on integrating a feature request segment on the existing admin dashboard by first understanding what users (in this case, the members of the team) would find useful to see.
How do we know when our solutions are impactful? KPIs (Key performance indicators) are defined to quantitatively measure performance regarding business goals.
In theory, Recogneo's KPIs can be defined as such:
1. The company increasing its revenue by x% due to a re-prioritization of requests.
2. Increasing the number of adopted feature requests by x%.
3. Reducing development time by x% in the next month.
4. Increasing user adoption rates within 3 months by 2%.
5 participants were interviewed to pin-point consumer attitudes and behaviours.
As Recogneo is a conceptual case study, the participants interviewed work at external companies. The interviews consisted of a form with several questions.
The questions simulate the experiences that would be found at Recogneo. The participants interviewed consisted of engineers, product managers, and UX designers.
Attitudinal questions
i. Is there anything you like or dislike about the way that feature requests are currently being handled?
Ii. What kind of transparency about customer needs does the current method provide for the company?
iii. Do you see any kind of potential in creating a better feature request system to better meet the team’s needs?
What do you think this kind of system would showcase? If so, what kind of benefits would this provide for the company? And on the contrary, why not?
Behavioural questions
i. What are some of the most important tasks you have to perform for feature request tracking?
ii. Which devices are you using for feature requests?
Iii. Which applications are you currently using for feature requests?
iv. What do you think your customers would say about the current feature request system?
"It would be good to have an overview of how we're progressing. Feature requests and bugs are sorted by category, but we can only really see what's going on by filtering a table." - Senior Product Designer
" It would be nice to be able to talk to Product about what we're fixing and why without long meetings. These meetings usually end up with us making documents that are often lost. We're not very organized." - Software Engineer
"We're using external apps like Zendesk. Many requests have the potential to increase user satisfaction, but it can be hard to sort through each request. Often times, they are forgotten because of other priorities in the project. " - Senior Product Manager
1. Product designers often state that there is often potential in feature requests, and it becomes difficult to analyze progress over time
2. External applications to process user requests that end up in a table with limited sorting capabilities.
3. Long meetings end up costing team morale, momentum, and productivity.
4. The lack of having a "single source of truth" when it comes to feature requests and updates is problematic. It results in lost time due to searching for updated documentation.
2 user personas are formulated from user research through the interview questions above and additional surveys.
As a result of the interviews conducted, it was discovered that there are two main roles that have been created in order to consolidate understanding throughout the team.
User needs can be distributed amongst two groups (design-oriented and data-oriented).
User needs can be distributed amongst two groups (design-oriented and data-oriented). The clustered needs and pain points of the users below support this decision.
Principles that were defined as guidelines and constraints in order to allow creative solutions to be conducive in meeting user needs for the two roles.
”Most users spend their time on other sites. This means that users prefer your site to work the same way as all the other sites they already know.” - Jakob Nielsen
Productivity tools are already well-integrated into Recogneo. They often use tools like Zendesk and Jira to help track issues.
Below, a SWOT analysis had been conducted to analyze the differences between various platforms that track feature requests to maximize the value proposition of integrating this into the adminstration dashboard.
Several commonalities are found between users as well as subtle differences that form key insights to provide a foundation for defining features for the dashboard.
The insights found are as follows:
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The consideration of potential differences in views that are role-dependent are also integrated into the design in order to improve workflows. For example, engineering-based roles will have Github integration views available, while design-based roles may not.
Recogneo has an admin dashboard that all employees have access to. This fulfills the requirement of having a "single source of truth". We will utilize this idea in the solution by incorporating the feature request segment in the dashboard for ease of access.
The admin dashboard also includes a product backlog tracking system. As a result, it will also allow employees to easily integrate the official backlog items along with the feature request segement. The feature request segment will increase filtering capabilities and provide an overview of what kind of requests are being submitted.
The feature request component also seeks to improve communication between various team members as analytics are surfaced.
Requirement Mapping
Employees considered the data below to be the most important. These will become components in the dashboard through various visualizations.
The information architecture of the feature request segment is shown below and highlights the differences that are dependent on roles.
Low fidelity prototypes were first created on paper using the Crazy 8s method. Then, transferred onto Figma and improved upon iteratively to define the screens.
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The high fidelity mockup is finalized from a series of iterations using Figma.
An interactive prototype that emulates user flow is then developed to understand usability at a greater level of granularity.
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Each feature below addresses the insights discovered to rectify user pain points.
Impactful outcomes are achieved through an iterative approach by testing and implementing features that...
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Testing & Iteration
Participants in the interview were asked to return to test the user flow of the dashboard and series of questions that related to their current experience of feature requests at their company compared to Recogneo.
The individual components of the dashboard were tested in terms of perception and usability.
The results were recorded in a rainbow chart.
I established a design system from the dashboard itself. The tone of the dashboard is minimalistic in order to emphasize the data visualizations in the dashboard.
Takeaways
1. The quality of communication between team members can be increased by changing the way each member processes what is being said.
2. High impact solutions need to be emphasized and systematically prioritized or they will not be implemented.
3. Presenting information can be simplified to save time and increase understandability.
Outcomes
1. 4/5 participants that were interviewed were impressed and see potential for the feature request tool for their own company.
2. 4/5 participants see it as a viable option to integrate the new tool onto a hosting tool like GitHub.
3. 5/5 participants see the value in re-creating a filterable table to easily view all feature requests.
Upon reflection, the user insights gathered from the first iteration brings up a few ideas for improvement.
1. One possibility for further improvements is to create great interactivity between the feature request tool and the backlog management tool. This will aid in helping employees visualize progress to a greater extent.
2. The ability to personalize the feature request tool (changing themes colours, and layout of graphs) can also be a great way for users to feel closer to the tool.
3. Thirdly, we can increase trust between Recogneo and its customers by delivering updates when a feature request has been implemented. This shows that the company takes the opinions of their customers into consideration.
All in all, creating enterprise SaaS applications involves a great deal of innovation. Research based methods are important to delivering a product that is accessible and useful. Simply integrating a solution that saves effort and time can be worth the investement.
The feature request tool seems to have improve workflows from end-to-end by increasing team morale through the positive attitudes of the interviewees.
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