For investment analysts data is everything, they love to dig into the data and understand all the nuances of a company, how its performing, what the market is honing in on, and more. Since they are so busy they need to be able to check data and get updates on how companies are performing in a quick way that gives them as much context as possible.

The highlights section sits on the very top part of PitchBook's company profiles, this section contains key stats or data points about the company. Each highlight has its own tile in the section and when you click into the highlight it opens a sidebar with more information. With this project my PM and I were tasked with adding new data points to our publicly traded company profiles since the current data points were primarily for privately held companies. The main users of the public company profiles are investment banking analysts or private equity analysts who are tracking the performance of a company or creating comps for a deal they are working on.

To start our project my PM and I wanted to get an understanding of what data points other financial data providers show on the top of their company profiles or in their "key stats" sections. To do this we took screenshots from various providers and then I created an Excel spreadsheet with all the data points that each data provider shows.

I analyzed this data by creating different pivot tables in Excel to narrow down which data points are mentioned the most.

The data points that were shown the most mostly surrounded stock price or trading data, which is expected for publicly traded companies. For our users while these types of data points are useful we suspected they weren't the most important so we followed up with user interviews to really hear what people wanted.

Following our competitive research we interviewed 6 PitchBook users and the Lead Emerging Technology Analyst at PitchBook. We asked them questions about their work, other data providers they use and like, about how they use PitchBook, and what data points are most important to them when evaluating public companies.

Following this research I created a board using Miro with customer quotes and organized them by Actions and Tasks users do, Visualizations they like, and data points that are useful to them, with the data points section being broken down into Morningstar data points, qualitative data, industry data, and whether not the quantitative data was time bound or not. In addition, I recorded who said which data points were most useful in their interviews and then I created a pivot table in Excel to see which were mentioned the most.

Now it was time for us to really hone in on which data points would be the most useful for our users. To do this we combined the competitive analysis and user analysis that we had conducted, narrowed down the data points, and then went back to our user specific data to make sure the needs would be met of the users we interviewed.

We narrowed it down to 35 data points that would be the most useful for our users. Next we checked with the development team to get an estimate for what the level of effort would be on their side to implement all these data points. After chatting with him we decided to phase this project into two separate groups with implementing the most important 11 data points in phase 1 and the remaining data points in phase 2 at a later date.

The 11 data points we decided would be added would be the new default data points shown for publicly traded companies: Market Cap, Enterprise Value, Total Revenue, EBITDA, Industries and Verticals, EV/EBITDA, EV/Revenue, Comps Set, Comps Set EV/EBITDA, and Comps Set EV/Revenue.

After narrowing down which data points we should show in the highlights section it was time to get started on the design work. To do this I started by familiarizing myself with how financial data is typically shown and communicated. I looked through various competitor websites, earnings call reports, and financial news sites to see how they visualize data and how it is typically presented.

Then in order to really understand the workflows of our users I pulled data from PitchBook myself and did my own analysis of Amazon to get the data I needed to create the designs for highlights.

I started sketching out different ideas for how we might convey certain data points and what the interactions might be.

Each highlight is broken into two parts - a highlight tile that is displayed on the profile page and then a sidebar view for when you click on the tile. In addition to creating the new 11 data points I also designed a new edit modal so users can create different views for private and public companies.

Each highlight was designed in a way to try to give the user the most meaningful information in a quick "snack"

We have the main data point in large bold letters, along with YoY growth % to provide more context, followed by a chart showing the past 5 fiscal years with the current value for the trailing twelve months, ending with the date that the data was most recently updated on.

In the sidebar view the chart is expanded so you can get more in-depth context on how the data point has been changing over time and then you have the opportunity to dig into the data deeper on the financials section through the link on the bottom.