CE Vision


This project was to re-build a research tool used by Investors, Hedgefunds and Corporates for Consumer Edge, a data research company with insights on consumer spending, delivered in a Software as a Solution (“SaaS”) model. This application is intended make the researching data process streamlined and intuitive for clients with a great User Experience that's not as noisy as the original system. Below is a much shorter version of what the team and I researched and my UX process for the final solution. I was lead UX/UI Designer who worked with the stakeholders, product team and data analysts.

Pain Points

  • Weren’t always sure where to find certain fields that they need data for
  • System is too slow
  • Clunky and unintuitive
  • Too many clicks


  • Upgrade existing researching experience to be more intuitive
  • Create simple process for the least experience users
  • Create a more advanced but well organized process for the more experienced users

Information Architecture

  • The information architecture was mostly standard to the industry (ex. Income range was grouped within Demographics)
  • Some reorganization was done working with data analysts, they were my SMEs since they’re most familiar with the industry and clients(users), but I also had to ensure things weren’t more complex than they need to be and are in their expected places
  • For the simple users a brand new set of fields and grouping had to be worked through based upon clients feedbacks, we conducted a survey asking for the common fields clients (users) would use to research and there a common correlations with the ones we decided with below. We ended up with 4 parent groupings which only had one or a few fields within each. They represented 50% – 60% use case of the research done within the system.
  • For the advanced users it took more than a survey but actually interviews so we could better understand of actual real life scenarios where they would have to research for insights within the system. We ended up with 6 parent groupings based upon what they would dig for within their research. We limited 3 fields per grouping due to complexity (ex. multi-select) of each field but some groupings. The familiar groupings such as Geography and Demography are things people are familiar with in terms of what belongs within each, it wouldn’t benefit to break these down any further. They represented 40% – 50% use case of the research done within the system, so we still had to them into heavy consideration.
CE Vision Simple User Information Architecture CE Vision Advance User Information Architecture

User Flow

CE Vision SImple User UserFlow CE Vision Advance User UserFlow CE Vision Advance User Additional UserFlow

Hypothesis / Assumptions

  • For the simple user (we called basic) a majority of them don’t enter cohort information, so although I had this in a minimal 4 step process, it’s unnecessary for them to see the cohort information, so made this a choice
  • The advance user has so many paths to solve for, even though I isolated the use cases to ensure they could complete their tasks, there were still complex intuitions. A visual connection would help connect Industry, Subindustry and Brand as a group.
  • The industry lingual is standard, however can be challenging for those who are unfamiliar with the industry. Because competitors use similar language, it wouldn’t be best practice for us to come up with one of our own, instead I added a further explanation of each field to help users understand
  • The advance user will require continuous iterations post production which will have to be solved for, but designing with the proposed flow gave us a good start


  • Tracking for this application was beyond google analytics but recording of every user actions, which we used that data to learn more about the advance user’s process


CE - Vision - Landing - Transaction Dates CE - Vision - Wizard Setup - Initial CE - Vision - Wizard Setup - Simple 2 - Date CE Vision - Wizard Setup - Advanced 2 - Transaction Dates CE Vision - Competitor Merchant 2 Added

Final Results

  • Average time to complete simple search
  • For advance search, due to the complexity of the different flows and types of charts, this is something we’d have to best learn over a year time and use Machine Learning. Many clients compare Year over Year records and are dynamically changing their searches, due to different trends, a year would give us the ability to learn the following
    • Most common search and the path users take to get there
    • Time user takes to complete each type of search, which we’d have to isolate and analyze further to reiterate the experience
    • Pattern where spending behaviors/trends is affecting fields being used to research



  • This application was pre-existing, but I redefined the primary colors to be accessible but fits to branding
  • I used a dark grey for all text (with a secondary lighter gray)
  • Did a re-assessment of the branding of colors across marketing materials and the company sites so I introduced a new color which was also accessible against white.


  • HK Grotesk
  • The original font of the application, easily readable that clients (users) were already accustomed to


View Invision Prototype