Situation
Protobrand’s disjointed quota management system was causing researchers to spend exorbitant hours forcing difficult requests into an inadequate system.
Task
Implement a new quota system that could handle more complex client requests, while being easy to understand and straightforward to use.
Action
Extensive UAT helped identify pain points in a ground-up redesign built for ease of use and flexibility for complex client sample composition requests.
Result
Initial promising results need to be monitored further to determine the full effect of this recent development.
Situation
One of the core functionalities of a survey program is to control the number of respondents that enter a survey based on demographics, category or brand questions, concept stimuli and more. A representative sample of a target group is the foundation for good research. Protobrand’s legacy system for controlling respondents used a variety of different quota tracking options, the inflexibility of which made them incapable of meeting client demands. For researchers, creating a design using multiple different tools that often interacted with each other in ambiguous ways quickly became frustrating for complicated sample requests. Client demands (such as sequential monadic display with rotating topics between evenly distributed cells with representative demographics) needed hours of researcher brainpower to figure out a way logic, profiles, concepts and maximums could be applied to fit the required specification.
This complicated concatenation of parts delicately put together was causing delays in client timelines and imprecision in recruitment. However, the biggest fault was the labor cost of extra planning, building, and testing a quota setup, and additional data processing demands to adapt to these convoluted solutions.
Task
Our task was to simplify and modernize the quota infrastructure, both in the backend calculation (for consistency, reliability, and flexibility) and the frontend functionality (for clarity and ease of use). We set out to create a more flexible and intuitive system that could handle complex client requirements without the limitations of the previous infrastructure. The new solution needed to reduce the time researchers spent on survey design and programming while improving their ability to meet client specifications.
Action
Research and initial system design
The first step was competitor research. Other survey platforms such as Qualtrics and Survey Monkey were researched for inspiration before designing a system that could match our specific needs. This research guided the team on best practices and, more importantly, what could be simplified and what could not.
After requirements were established and an initial draft of the new system was ideated, 15 recent projects where complex client requests had posed significant challenges were analyzed and replicated in the new system. This helped identify shortcomings that needed to be minimized and strengths that could be maximized while validating that the new system could handle even the most demanding scenarios.
Finalizing the structure and UI
Recognizing the potential anxiety and resistance from researchers due to the break from their usual workflow, the team prioritized a user-centric design approach with a clear structure and intuitive settings to reduce the learning curve.
My biggest learning from this project was the importance of usability and user acceptance testing. For this project, we put a big emphasis on consciously designing the user experience to fit both user needs and users’ intuition. I was able to see both how users reacted with the design elements of the new system, but also with the core methodology. This helped me identify any remaining pain points in the structure and finalize the UI design so it was easy to learn and simple to understand. We learned so much during this testing and realized how crucial it was to the success of the product and rollout later on. It laid the foundation for how we treated product design in future projects.
Training and rollout strategy
To ease the transition, the team was highly involved in developing training materials and working with users when learning the new product. This included updating the user manual, creating a training video, and holding multiple workshops. This, in combination with the intuitive structure and UI design, allowed for a small learning curve for researchers.
Our rollout strategy was slow and controlled to promote trust in the product from users. Users were allowed to choose between the new and old quota system for a few months before the old one was removed. If users decided to work with the new system, the product team worked closely with the respective project team to ensure success.
Result
As this project was recently developed, the results in regards to key KPIs are still being measured. However, researcher feedback so far is unanimously positive.