Users of Analytics Lab have access to an abundance of data on funds that are included in Morningstar’s coverage list. Currently, we don’t provide notebooks with information on funds Morningstar is considering adding coverage on. This means users can’t keep tabs on new research and investments considered up-and-coming.
To amend this, creation of a notebook was proposed that would add the capability to filter through Morningstar’s ‘Prospect List,’ a list of funds Morningstar is considering adding coverage on.
I needed to create this notebook in order to fulfill user requests and maintain Morningstar’s competitiveness in the analytics space.
There were many limitations with this project when it came to space and branding. I wanted to take this project a step further, so once the notebook was complete I continued my exploration of it as a personal project. Throughout this case study I will discuss what was shipped in addition to my personal design.
I started by compiling questions I needed to answer.
What is the best way to visualize the data required in this notebook?
How many filters and settings are needed to best optimize that data?
What page hierarchy best fits these needs?
I conducted a brainstorming session with my team of analysts, researchers, and project managers to look for answers. It was uncovered that:
Users aren't just looking for data on prospects, but Morningstar’s analyst commentary as well (aka a fund summary written by one of our researchers).
The Morningstar Prospect List is small, which means it doesn’t require as many filtering settings as other notebooks might.
Users are looking for Morningstar’s ‘classic’ data points for comparison, like Morningstar rating and other basics like fund size. This would influence whether I chose a chart or table as the main component.
From these findings, I identified three key items that would influence the final product:
Analyst commentary would be included in the notebook as a main feature.
Filter settings would be simple and limited to one or two selections, like fund region and category.
For the main data section, a table is preferred over a chart.
As a starting point, I did some market research on competitors to investigate their offerings and get inspiration from the features I liked most. A couple that caught my eye:
Colorful, clear graphics. MSCI and Yahoo! Finance prove financial statistics don’t need to be boring to look at or hard to understand.
I then conducted a series of meetings with the team to get their thoughts on these findings and draw out their vision for a solution. Based on these meetings, it was clear there were a lot of commonalities in the product vision. We got specific and identified the following structure for the notebook:
Header
Intro copy
Settings
Region
Category
Data Table
Analyst Commentary
Appendix
Using my structure, I began mocking up the notebook.
I opted for a minimalistic look, keeping in the classic Morningstar styling. The notebook needed to feel cohesive with the rest of the product.
Final
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I wanted to see where I could take the project if I lifted all the tech and branding restrictions and pursued it just as a personal exploration.
For the overall look and feel of the notebook, I liked the simplicity Morningstar’s design provided but wanted to see the components highlighted more. After a quick color exploration for the notebook background, I landed on the third option below to bring forward components like the data table.
Header & Settings
Settings interaction
Data Table
Performance Graph (new component)
The Analytics Lab team had discussed adding a performance graph into the original design per user request, but due to restricted space it was cut out. I decided to include it in my version as it provides essential return data for the user.
My chart design allows users to select which funds from the Morningstar Prospect List they would like to compare, in addition to time period, currency, and chart format.
Interaction
Analyst Commentary
I broke up the commentary into sections to improve readability and included links to additional information related to the investment. A dropdown was added at the top for users to toggle between investments.
Before
After
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Our goal for this notebook was to reach 177 views per week (a 10% increase from the previous quarter’s most popular notebook). Once launched, the notebook showed an increase of usage and eventually hit our goal after 2 weeks of being published to Analytics Lab.
As next steps, I would like to establish a feedback loop with Analytics Labs users to get consistent feedback to inform future iterations.
