Portfolio View

The first step towards allowing users control over their data

Product Manager: Trevor Higginson

Product Designer: Erin Lloyd

Supporting: Matt Thurman (Lead Designer), Josh Dean (Product Design Manager), Mark Larson (Product Director), Kristina Linova (Product Director), Cody Nevels (Subject Matter Expert), Bryan Smith (Engineering Manager), Zeina Zeitouni (Head of Product Management & Design)

Status of project: Greenfield from the very beginning

Impact to the Business

We successfully launched with 14 alpha users and confirmed that users not only wanted an in-app “Schedule of Investment” experience but also full control over their data. This feature has been critical to the business, addressing longstanding customer requests. Many users had threatened to leave or already churned due to its absence. By implementing this feature, we’ve saved existing contracts and secured new ones. The research Trevor and I conducted for Portfolio View played a key role in achieving this success.

Context

For many Venture Capital professionals, the “Schedule of Investments” (SOI) is a key report in a fund’s financial statements. It details all the companies or assets the fund has invested in, including investment amounts, valuations, and ownership percentages. This report provides transparency for investors and is also used internally to track each company’s performance.

The challenge is that creating and maintaining SOIs is a highly manual process. Teams often compile data into spreadsheets, reconcile it with legal documents, and resolve discrepancies between the formal details and the team’s practical understanding of the fund or investment structure. Aumni’s rigid, document-backed system can make this even harder. This has led to repeated requests for tools that allow users to control and organize their data in a way that works best for them.

Hypotheses

  • Customers highly value a unified, all-in-one view of their data, and Aumni can be the tool to provide that experience.
  • Customers will find greater value in Aumni as a data repository if they can easily manage and control the data stored there.
  • By offering customers a customizable, unified experience, Aumni will become an even more essential tool for them.
  • Customers care enough about the accuracy of their data in Aumni—whether actual or perceived—that they’re willing to manually enter their own data points and portfolio companies.
  • We can quickly gauge customers’ willingness to manually input data if we offer them the option, even with minimal or low-scope opportunities, outside of Custom Fields in reports.
  • There’s a small, specific set of data points—both summarized and unsummarized—that customers deeply care about and want the ability to input or override themselves.
  • Customers, for the most part, prefer to input data at the transaction level rather than at the aggregated company level, as the transaction level is the foundational building block of their data.

Research

Pulling Customer Feedback

I went on a deep dive into our customer feedback vault, carefully built by our awesome CS team, and pulled out 22 pieces of feedback that really hit the mark. There’s probably a lot more in there, but these stood out as the most relevant to our big question: would users want to create an in-app SOI?

The best part? This gave us a quick list of customers to reach out to for testing—ready to get the ball rolling!

The feedback highlighted themes:

  • like overrides
  • adding notes
  • capturing their own data
  • admin control
  • a single source of truth, or as we came to call it, a “Single pane of Glass”.

Assumption Stacking

As a product group, we ran the exercise twice to refine our process. The first time, we focused on mapping our thoughts around the desirability, viability, feasibility, usability, and ethicality of our early low-fidelity designs.

The second time, Matt Thurman led us through this exercise, and we took a more focused approach by plotting our assumptions on a scatterplot. The x-axis represented “Least Evidence” to “More Evidence,” and the y-axis ranged from “Less Important” to “More Important.” This approach made it easier to prioritize our assumptions and identify the areas that required the most attention and validation.

How much control do we give users?

I gathered sketches that everyone had created laid them out, carefully listing the pros and cons of each.

This exercise allowed us to fairly weigh all of the sketches. We broke them out all by control level, where in the app they would be, granularity of data touched, Aumni-data and Customer Inputs Co-existing together, Transactions being added.

Competitor Analysis

Some patterns I looked through were Carta, Tactyc, even Fullstory and how they use their ability to change metrics. We later looked at reconciliation patterns in YNAB (You Need a Budget) part of a larger scope of this project.

User Story Mapping

Matt and I spent time and mapping out the flow of users; going from activities to tasks to subtasks. This helped me and Trevor understand user flows.

Trevor and I conducted eight internal interviews with the Customer Success (CS) team, gathering 94 individual pieces of feedback. Two main themes emerged:

Interviews with Internal Stakeholders

1. Information and Solutions – They shared key details we hadn’t thought of, important info to remember, and solutions for addressing pain points.

  • Differentiate visual treatment: Highlighting what is user data vs. Aumni data.
  • Important Data Points: Key data points like fair market value, currency, board seats, and costs often change and aren’t always backed by legal documents, so customers need the ability to update their own “Schedule of Investments” (SOI).
  • Cross pollination and overriding data: If we override data, we can’t cross pollinate with the latest data that others have uploaded, so there is a risk the users data will become stale and out of date.
  • Various solutions brought up: Some ideas from CS on what we could do to potentially fix or look out for as we are thinking through solution.
  • What customer’s care about: They want flexibility and to be able to override and not have to double check their work. They want to know that the number they see is the number they trust.
  • Outliers: Extra feedback, mostly around limitations in the app.

2. Problems Identified

  • Aumni Errors & Processes: The Unaccommodated Investee model is the closest fit for our data summarization form but still falls short in capturing transactions linked to investments. Reconciling data and documents to get investments to 100% accuracy is time-consuming and often incomplete.
  • Missing Documents: Some deals are over 10 years old, and finding legal docs is unrealistic. We shouldn’t require them.
  • Time-Intensive Setup: Getting Aumni fully operational takes significant hands-on effort.
  • Gatekeeping: Customers hesitate to share Aumni with peers due to unresolved data issues they can’t override.
  • Messy Audit Logs: Audit logs intended to guide reconciliation often overwhelm customers.
  • Contacting CS is a Hassle: Customers prefer to self-edit data in the app rather than relying on CS for updates.
  • Rigid Model: Our data model lacks flexibility, misaligning with some customers’ expectations.

Interviewing Customers

14 total firms added to the cohort:

  • 5 were Emerging size ($300M AUM)
  • 5 were classified as Traditional size (<$1B AUM)
  • 2 were Mega (>$1B AUM)
  • 2 were unspecified

Persona in the alpha cohort:

  • 4 Finance
  • 3 Operations
  • 2 Legal
  • 2 Investment Team
  • 1 Partner
  • 2 not specified (due to emerging status, these firms usually do it all as they are run by 1 to 2 man teams and don’t have proper roles.)

Trevor and I reached out to all 14 firms and only 12 responded, interested in interviewing for our user test. We were able to speak with 12 of them.

We ran structured customer interviews to learn more about user behavior and gather useful insights. We started with some introductory questions to understand their workflows and habits. Then, we had them complete tasks using a low-fidelity prototype in Google Sheets to see how they interacted with it. Finally, we showed them product designs I created and asked for their thoughts on how well the design might work. To keep things consistent and reliable, we followed a standardized script throughout the sessions.

As shown in the FigJam, Trevor created debrief cards for each session, using the format from Continuous Discovery Habits by Teresa Torres. These were crucial in helping us to remember all of our interviews.

So, what did we learn?

Insight 1:

Aumni can be as the Single Pane of Glass for Investment Tracking

Pain Points:

  • Customers juggle multiple platforms for data tracking, leading to outdated or incomplete portfolio data.
  • Difficulty in viewing a holistic portfolio picture; users prefer consolidating to one or two tools.
  • Users want all their data centralized for easy access.

Opportunities:

  • Combined SOI and valuation tracker.
  • In-app Statement of Investment (SOI).
  • Integrated valuation tracker.

Insight 2:

Aumni-Led, Customer-Supported Data Entry

Pain Points:

  • Aumni’s rigid data model prevents customers from inputting or overriding data.
  • Users want a hybrid experience: Aumni handles most data entry, while they supplement missing or more accurate data.
  • Users need flexibility for:
    • Adding transactions without documents.
    • Overriding calculations they disagree with.
    • Writing off investments they deem inactive.
    • Rolling up or splitting transactions across funds.

Opportunities:

  • Allow customers to:
    • Add custom investments and transactions.
    • Edit data points, showing their version alongside Aumni’s.
    • Resolve audit log items using custom data.
    • Create placeholders for future workflows.
    • Link documents to custom entities.

Insight 3:

Increased Data Trust via Customer Control

Pain Points:

  • Users struggle to trust Aumni’s data due to inaccuracies they can’t fix.
  • Back Office gatekeeps Aumni due to these issues, limiting Front Office use.
  • Users want transparency between Aumni-reviewed and customer-inputted data without overlap.

Opportunities:

  • Clear UX distinction between Aumni-reviewed and customer-inputted data.
  • Interface to compare both data sets and choose which to display.
  • Features for confirming data accuracy without requiring documents.
  • Hyperlinking to data sources for transparency.

Insight 4:

Custom Fields Beyond Reports

Pain Points:

  • Custom Fields are limited to Reports, leaving other areas unable to track additional data points.
  • Users need flexibility to track unique data across the app.

Opportunities:

  • Add Custom Fields to:
    • Portfolio View.
    • Portfolio Company Overview pages.
    • Data points for additional context.

Insight 5:

Data Fidelity as a Means to an End

Pain Points:

  • Data inconsistencies hinder reporting and team sharing.
  • Users need reliable data for investment tracking, internal reporting, and LP updates.

Opportunities:

  • Data fidelity integrated across the app, ensuring customer-inputted data cascades throughout.
  • In-app report builder with presentation-ready outputs tailored for different audiences (e.g., LPs, internal teams, pitch decks).

What do they look like?

Designs I Created

Sometimes you just want to see the file.

LOW FIDELITY

Early Ideas

Obviously there are lots and lots of whiteboard scribbles and sketches that don’t mean very much to anyone than the folks that drew them, but here are a few meaningful shots of early low fidelity work and some turning points in our decision making process.

We first had to decide where we wanted to put the design. Should it live in a new place? Should it replace the homepage? Should it work with the homepage?

Option 1 – Adding to our existing homepage.

This shows adding a brand new legal entity.

Strengths of this Design:

  • We already have the infrastructure in place.
  • Users were familiar with how the site worked.

Limitations of this design:

  • The code is old and out-of-date due to migration efforts. Because of this, further development of the page would be problematic.
  • This design is really rigid in form and data. If users want a more flexible pattern and tool for their data, we wouldn’t be able to accommodate that.

A little higher in fidelity, mostly focusing on editing an existing Portfolio Company’s data that is backed by legal docs and not capturing a new Portfolio Company. You’ll see we are calling Portfolio Companies “Legal Entities” this is because there are many different kinds of Investments technically, not just portfolio companies.

Option 2 – Creating a spreadsheet and replacing our homepage

This shows the “SOI” where a user has added a brand now “Ghost” company that isn’t backed by any legal docs. This allows for users to enter their own portfolio companies without having Aumni to ingest them.

Strengths of this Design:

  • Users love the idea of a spreadsheet.
  • They get to navigate with their keyboards and can click away.
  • Familiar pattern.
  • Flexibility in how to show the data.

Limitations of this design:

  • It is a spreadsheet, which can make it long and the number of columns is arduous.
  • Development of spreadsheet tooling can be really complex.

Option 3 – Editing in line

Designs created by Matt Thurman

You will be able to click on a specific data point. This will give way to educating the user and being able to edit in a side panel. While we did end up exploring this design further for another project, we did not continue this design as a solution for this particular problem.

HIGH FIDELITY

Moving Forward

We ended up going with a spreadsheet concept, which in the end was the most familiar.

From a technological standpoint, we moved forward with AG Grid, which meant that I had to quickly become versed in their library and design system. See the Figma file for the final designs.

Overall we iterated through custom transactions and custom entities, adding in the ability to add custom input fields, which we took away once we implemented AG grid.

Lots and lots of iterations and learnings!

My favorite part is how elegantly the math works in this design. The custom transactions roll up to the custom legal entities and create an aggregated view of crucial numbers like Post Money Valuation, My Cost, and My Shares.

See a quick video of the final product in staging!