Bloomberg for the masses

TL;DR

Built an investment insight-driven product to bridge the gap between frictionless investing apps such as Robinhood and the often limited investing knowledge of their users. Utilized input from users, industry veterans and A/B testing to build a mobile app MVP centered around an investment insights feed. I ultimately deprecated this project due to a lack of clarity around its monetization potential.

Personal Objective:

I undertook this project to help bolster my consumer software product acumen by taking a B2C product from 0 to 1. Managing a small startup team and learning through trial and error were instrumental to building my understanding of what it takes to run a concept all the way from ideation to a functioning software product that brings real value to users.

Problem:

Robinhood was effective in introducing in a new cohort of investors to equity investing through its frictionless trading app. However, there has been a clear disconnect between the newfound ease of trading and knowledge level needed for these users to invest responsibly. Retail investors, who have historically been at a disadvantage due to limited investing knowledge, poor access to key data and a general lack of guidance, were prone to overtrading with the Robinhood app's emphasis on gamification to maximize user engagement. It didn't help that the reliance of Robinhood's business model on selling customer trading data to market makers may have aligned its incentives toward maximizing the number of trades made rather than prioritizing actual user outcomes (i.e. growing user wealth).

Solution:

With the ease of use and access to clean processed market data found in the Bloomberg Terminal as inspiration, I set out to develop an app that would bridge the disconnect described above by providing retail investors with relevant investment insights that would improve their ability to make sound investment decisions. Based on early user feedback, product research and given the real-time nature of markets and investing in general, I determined that the app should revolve around a central investment insights feed that would be regularly updated with the most relevant insights at the top.

Process:

I identified and extensively interviewed about 50 individuals with a variety of investment knowledge levels and portfolio sizes. These interviews helped me identify 3 key user segments:

1) Complete neophyte investors (have made a total of less than 5 lifetime stock trades)

2) Investors (typically make stock trades every month)

3) Regular traders (typically make stock trades every week and may trade more advanced financial instruments such as options)

I also brought in key advisors that had either built institutional-grade investment tools or worked for established brokerages to provide their related know-how.

For the app build, we vetted and hired two engineers in Ukraine that specialized in React Native. The build was broken up into 3 key milestones with approximately 2 weeks allotted for each. At the same time I worked with a UX designer in San Francisco to develop and hone the app UX in Figma (which I had initially mapped out as simple wireframes using Balsamiq). A great deal of time was spent at this stage mapping out user flows and gauging how best to package and present relevant investment insights to users. Prototype mock-ups were regularly shown to our user group and key advisors in order to solicit constant feedback that helped guide development direction. In addition to giving users the ability to build their own stock watch list, we also enabled them to link with their existing brokerage account(s) using Quovo (later acquired by Plaid) on the backend.

The market segment I ultimately decided to pursue was between 2) Investors and 3) Regular traders as these users were already more engaged with their investments, had typically moved beyond investing basics and were largely seeking some level of direction in their investing. This helped the core focus of the app remain on investment insights rather than moving too far into investor education.

Outcome:

We were accepted into the Boston-based FinTech Sandbox financial data incubator, which gave us the ability to utilize expensive institutional-grade market data feeds for free while we developed the MVP up until launch.

However, ultimately there was a disconnect between the underserved needs (investment insights) and the value proposition presented. It turned out that potential customers would pay for an app that outright indicated what financial instruments they should buy or sell rather than one that would improve their existing decision-making process. Going in this direction would get into sticky regulatory issues around financial recommendations as well as potentially poor calls due to a myriad of reasons out of our control (macro environment shifts, surprise announcements, etc.) A clear path to monetization was just not there despite the perceived product market fit.

Post-Mortem:

If I did this project again, I would:

A) Hold off on building an app until there was an ironclad monetization strategy in place that had been properly vetted by end-user research.

B) Investigate potentially using smaller but more lucrative cohorts of customers (i.e. professional traders, family offices, etc.) as an early adopter beachhead from which to expand.