The nomination tool is the data analytics software used by the team at Open Road Integrated Media


Skills / Area Of Focus
Product Design
Roles & Responsibilities
Product Designer
Users & Audience
Open Road Integrated Media
January 2023 - ongoing




This was a real-world product for my UX/UI Designer role at ORIM.
In addition to a publishing catalog, ORIM offers marketing services for major publishers.
To learn more about what ORIM does go to the company website.


User Research
Design Research
Information Architecture
Low Fidelity
High Fidelity

Scope & Constraints

  • No budget
  • Tight deadline
  • I am not familiar with the product.


    The ORIM marketing team was experiencing frustration over the complexity of the data analytics software. It is overwhelming for the users to find books and create campaigns out of the many different filters and settings.

    Business Problem

      There is a need to create a higher volume of campaigns for marketing books because the company has expanded the business.

      User research

      A focus group was organized to collect feedback from the marketing team to determine the problems they are experiencing with the nomination tool. 

      Caption: Users mentioned they would like the most used filters to be more visible
      Caption: Users mentioned they would like the most used filters to be more visible
      Filter groups needed to be prioritized. 
      The ability to sort through columns and store their settings to reuse the filters they need. 
      Users want to be able to see their work as they select the filter.
      They need to be able to store it and send it to another user in their team. 
      They also needed clarification when selecting the number of columns. 
      They need an easier way to select columns and organize them according to their needs.

      Design Research

      I looked at similar sites with a high volume of filters helped me understand how the filters are being used. I also looked at the different layouts.

      Users can also filter through data with operators, creating their own equations.
      Walmart has many different filters to help users select a product
      The ability to see the selected filter settings, and use a strong hierarchy is frequent on most websites.

      the information architecture

      Users were asked to rank their most used and least used filters.

      The team was asked to prioritize the filters they mostly use and the least used. They were asked to rate each filter on a scale of 1-10. 1 for least used and 10 for most used. Once users responded, filters were prioritized according to to their ratings. 

      Results: Prioritized filters were divided into Pinned, Frequently Used, and Active as the three main groups where user can store their work and save their settings. New groups emerged from the rest of the filters that different teams use. Users were asked to sort through the groups in different teams. New labels were created that made sense to the user related to the functionality of the filters.
      Most Used
      Title Metadata: 23
      Proposed Filter Category: 21
      Medium use
      Amazon Site Data: 9
      Campaign Performance Data: 7
      Sales Data: 7
      Least used
      Global Campaign Processing: 4
      Campaign Run Dates: 2
      Exclusion Filters: 2


      Using the data from the research, solutions were developed around filtering groups, showing results, sorting columns, and sharing a setting with another user.

      My solution was for users to be able to browse filters vertically.
      Horizontal layouts were suggested by Project Manger

      High Fidelity Wireframes

      Additional feedback was collected from the team. A high-fidelity prototype was used to test the design with users.

      Most important filters were prioritized into pinned, active and frequently used categories.
      font lora
      Users preferred a sidebar navigation
      Montserrat font
      A sub menu was created for users to be able to navigate results page

      Outcomes & Lessons Learned

      What did I learn?
      • I learned to understand complex data points.
      • Understanding the different filters and gathering feedback from tech and marketing was challenging.
      • Still, it helped me understand the complexity and simplify the steps so that the team could access the information they needed from the database seamlessly.
      • Marketing team is happier
      • Business grew by 20%
      • Other areas of the business were developed
      • In the future, I would like to design self-service data analytics software that can be offered to clients.