Invest With Confidence.
The goal of our product is to help investors understand the stock market. Our product should encourage users to invest like an expert, understand what they're buying, get personal finance guidance, and become financially savvy.
Our team created personas to represent the ideal users for our product. Our goal is to focus on the Comfy Carls and Diligent Danas, but also to keep the Newbie Ninas and Alex Advisors in mind for future product expansion opportunities.
Research shows that millions of people are scared to invest. When they do, they lose their money. Just ask the average investor, who earns 2.6% per year. Why?
From here, I tried to step into user's shoes, specifically the Comfy Carls and Diligent Danas, in order to figure out the kinds of emotions users feel, and the types of questions users have, when investing. Investing is a difficult skill. Users are not familiar with how to make decisions regarding investing. Some feelings associated with investing include: overwhelmed, fearful, uncertain, skeptical, lost, etc. When I reflected on our core problems from a user's point of view, two big questions came to mind:
These two questions became my projected "Big Problems", or what I thought users struggle with the most. The first part of my user experience research was to determine if our users also feel that these are their biggest problems.
Our current product allows user to:
These features help answer the projected "Big Problem" questions our potential users have (from the previous section).
As a reminder, our app is geared towards the Comfy Carls and Diligent Danas, or intermediate to experienced investors with a desire for growth.
Now, let's take a look at who our current users are, and what feature they use. I used FullStory to conduct a form of a "Customer Safari" to analyze user interactions within our app. The point of this exercise was to answer questions like:
I observed about 15% of the sessions on FullStory, which provides recordings of users' interactions with our product. From this exercise, I found a series of interesting stats:
What I learned from this exercise and calculations is the importance of onboarding, instructions, and suggestions. Users aren't sure how to use some of our features, and sometimes aren't even reaching features that we work so hard to build. Based on the interactions I observed, our users are only using a small amount of features; this means that our product is only meeting about 40% of our vision's goals.
As a result, our team has created three new goals. The first is to create a more involved Onboarding process. This will help not only to determine what features to present to the users, but also is critical in helping users actually utilize the features of our product. Onboarding should ask personal questions including investing goals and level of experience, in order to personalize the app's features to the needs of the user.
The next goal is to increase fluidity in interactions. The lack of onboarding and instructions makes the app impersonal and difficult to use. Furthermore, a lack of "Related Stocks" and "Search Suggestions" makes the user feel lost, confused, and unsupported. By providing related options and suggestions, the user has more options for navigation, and therefore is more likely to move to a new screen. This will increase fluidity between interactions as well as the number of interactions within each Session for users.
Lastly, the third goal is to figure out exactly what information people want to look at when researching a stock. This information will differ on what type of action or strategy they follow, whether they've seen the stock before, or how regularly they want to visit the stock. These questions can be answered from future research, interviews, and usability testing.
Both surveys and interviews currently focus on problem identification and solution validation. The point of the survey was to find a correlation between levels of experience and desired features or product functionalities. This method is more quantitative. On the other hand, the point of the interviews was to get more of a backstory and understand details about "why and how" people want the features they want. The point of visiting investment forums was to gather observations and behaviors of users' investing habits. These methods is more qualitative.
Our survey contained a list of problems that users come across during the investing process, based on previous research. The survey asked users to assign one of these problems as "Most Important", and another one of the problems as "Least Important". After conducting the survey, I began analyzing the results by assigning each "Problem" with an overall score across all experience levels.
Score = (# of "Most Important") - (# of "Least Important")
The two problems with the highest scores were:
The first thing I noticed is that our survey's top-rated problems match pretty closely with my own projected "Big Problems" (from the Overview section). This was good news!
Next, I categorized the responses in three catgories:
Then, I calculated the percent of survey-takers from each category that chose the top two problems:
I found that there seems to be a direct relationship between the level of experience and the top problem chosen: the more experienced the user, the more likely to choose "How do I learn to get better and gain confidence in my investments?"... why?
I looked through survey answers and interviews to find that investors without experience want simple answers, not extra information. They use words like "fast and safe" to describe their ideal solution. Comfy Carls and Diligent Danas, on the other hand, want to do research themselves. They use words like "personal strategies" and "methods". They are less trusting towards curated advice, unless they can see clear, reliable sources or quantitative proof.
Additionally, experienced investors think of more long-term investments, while newbies want short-term returns. Carls and Danas have "patience". They also know that there is not one "right answer" to buying or selling a stock. Furthermore, they even know that "gut feeling" sometimes plays a role in stock decisions.
The survey found that Comfy Carls, Diligent Danas, and Alex Advisors are familiar with the stock research process and like to be in control of their decision process, but they don't always have the time or information to research diligently.
Therefore, we redefined the core functionality of our application: to provide more efficient stock research tools for mid-experienced investors.
I followed the same steps as above to validate our solutions. Our survey contained a list of solutions that we came up with, based on previous research, interviews, and experience. The survey asked users to assign one of these solutions as "Most Important", and another one of the solutions as "Least Important". After conducting the survey, I began analyzing the results by assigning each "Solution" with an overall score across all experience levels.
Score = (# of "Most Important") - (# of "Least Important")
The three solutions with the highest scores were:
Next, I categorized the responses in three catgories:
Then, I calculated the percent of survey-takers from each category that chose the top three solutions:
Once again, I found that there seems to be a direct relationship between the level of experience and the top solution chosen: the more experienced the user, the more likely to choose "Quickly understand a company's financial statements". I suspect that the same reasons that affected relationship betwen level of experience and problem identification, also apply to the relationship between level of experience and solution validation.
I also considered that the top solution for Comfy Carls and Diligent Danas contain the word "quickly", which ties to their main concern of not having enough time to diligently research stocks. This reaffirmed our redefined product functionality: to provide more efficient stock research tools for mid-experienced investors.
Next, I conducted interviews to get a better understanding of why our users are facing problems in their stock research process, and how they envision a solution. I got great feedback from several interviewees.
“I look up different stats for different strategies of investing.”
“Curated news is great. I don’t have time to diligently do all my research.”
“Best investors correlate research with results.”
“I don't trust apps that tell me to buy or sell. I would rather have technical indicators.”
“I'm too busy to do quantitative research.”
“Do news summaries look at the right articles and sources? Do their summaries filter information in a way that is right for me?”
From the interviews, I found some contradictions in the users' solutions. Although most of the users agreed that they lack the time to meticulously research while investing, not all want financial document or news summaries. Some experienced investors have a difficult time trusting what they are reading, and have their own preferences when it comes to news sources. They feel that summaries do not always do the entirety of the document justice, and may skip out important details that the investor thinks is vital for his own personal stock decisions and choices. They also would like to verify any sources and make sure that the summaries convey the same information.
Therefore, we decided to add a new clause of trust to our new product functionality definition: to provide more efficient, yet clearly trustworthy, stock research tools for mid-experienced investors.
I also visited a lot of online investment forums to figure out investing habits from observing and noting interactions between different kinds of investors. One thing I noticed was that newbies repeatedly asked the same question over and over again:
"If you had X amount of money to invest right now, what would it be, and why? ... What should I invest in?"
In response, a lot of experienced investors gave insightful answers.
“Disney and AT&T. They have solid futures and are solid companies.”
“Where do you think the world will be in three years?”
“Picking a good stock is as simple as recognizing a good company.”
“Learn how to guess with charts. Or just invest by fundamentals and hold long term. Nobody can give you an answer.”
“Investing depends on strategy. Long term? Dat trade? Dividends?”
“Look at graphs of what a company has done market wise for the last couple years and then look for reasons why you think they might go up.”
Experienced investors recognize that a good company indicates a good investment. They also note that there are different strategies to investing, or different aspects to think about when choosing a stock, depending on what the investor's goal is. Experienced investors use charts and graphs, but they also have a gut feeling. They think about why a price is rising or dropping, and how the current news will affect a stock. They try to predict the market trends and future growth sectors. In short, experienced investors know that there is no one right answer.
To synthesize all my research and put the findings to use, I followed a set of steps. First I established empathy with the users by creating an Empathy Map. I used the Empathy Map to define the challenges users currently face, or the specific problem I will address. Next, in the "ideate" step, I conducted a Brainstorming session where I came up with a plethora of functionalities of our product that would address the user's problem. I, then, created a 2x2 Prioritization Matrix, experimenting with axes, in order to find the best idea from my Brainstorming session. Finally, I created a Storyboard for this idea to present it to the team. After this presentation, the group approved of the new product functionality, and created a clear roadmap to begin prototyping and testing the feature.
I use Empathy Maps when I have research findings and want to understant what they mean at a deeper level. It helps me sink into a customer's perpsective and relate to his or her emotions. It also helps me uncover underlying motivations and beliefs that drive behaviors and words. I begin by unpacking the field research. I make notes of what was surprising, interesting, or major from each user interview. I write these down on post-its to capture observations, quotes, and inferences. Next, I categorize each post-it in 4 different areas...
What did each person:
Next, I identified some contradictions in the interviews. One thing I noticed was that Some users felt like they spent enough time investing (per day/week), but thought that their research process was inadequate because not enough time was spent researching stocks. This may be because their busy schedule does not permit any more time spent (per day/week) on investing, but their research process is really lacking. This may also mean that they are spending adequate time on investing, but a lot of time is wasted in the research process due to navigating between different sources (Seeking Alpha, Yahoo Finance, Google News, etc.) and actively searching for information. This may indicate a lack of clear access to the desired information. Nonetheless, most users felt that they do not have enough time to research stocks diligently.
Another contradiction I noticed was time versus trust. Most customers want clear access and ease of information, but say no to expert opinions, analysis, news summaries, etc. Why? They don’t think these sources are personalized to their own interests. They also don’t think these sources are trustworthy. Some users mention they don’t trust Glassdoor. How do we provide information in an easy and accessible way while maintaining trust? One way is to use a lot of numbers and stats. People trust numbers. Another way is to clearly list sources. We should brainstorm about how else to gain trust.
The next contradiction I found was that investing techniques coexist with a gut feeling. Users list many stats and things to look at when deciding to buy or sell stocks: news, personal strategy, goals, techniques, financial statements, company history, company predictions, market predictions, etc. But they also repeatedly list something called “gut feeling”. Is that just another name for experience/practice? Is it confidence in company’s future/stock’s ability to grow? How do we help users develop a “gut feeling”?
Another contradiction I found was discerning between emotions and familiarity. Users agree that values and emotions are the least of their concerns when investing. But they mostly invest in tech and energy. They state that they would rather not invest in sectors that they are not familiar with. Feedback shows that people are warned about the lack of their diversity in their portfolios (Merrill Edge), but they don’t want to do anything about it. They don’t care that they are overweight in technology. How do we change familiarity of sectors for our users? How do we ask people how comfortable they are investing in a certain sector, without including emotions in the question?
Next, I found a key difference between the fear of losing money and the fear of market ceash. Some users are afraid of market crash. These seem to be the less experienced investors. Experienced investors are afraid of losing money due to a lack of research. Some say that they don’t invest in small companies because they’ve been burned too many times. Instead, they only invest in mid-to-large size companies.
Lastly, I found a distinction between users that believe in market predictions and users that believe in creating a holistic portfolio. Users that say their top solution would be a guide to make a holistic portfolio, also say that market stats/sentiment (greed, fear, news sentiment, etc.) is less important. People that say market predictions is most important, say that diversification or exploring other sectors is not a priority.
As a last step of this UX exercise, I found some shared observations amongst users:
From these contradictions and key observations, I was able to define a series of challenges investors face. I used these challenges or problems to create brainstorming questions, which leads me to the next part of the UX process.
I conduct brainstorming sessions when I want to quickly generate a bunch of ideas from a variety of perspectives. It helps me probe more deeply into a problem or opportunity area.
First I set context by defining the problems that users are currently facing, using interviews, empathy maps, personas, and insights. Next, I come up with a series of provocative questions. I rapidly come up with ideas to answer these questions, and when ideas start slowing down, I build off of old ideas. Finally, I cluster the ideas into themes.
Below are some questions I asked myself during the Brainstorming session:
For each question, I came up with a plethora of ideas. After clustering these ideas into themes, I moved on to the next UX exercise: the 2x2 Prioritization Matrix.
I use the 2x2 Prioritization Matrix when I have a large number of ideas and need to evaluate their effectiveness and narrow my focus. It helps me explore the relationships and tensions between two goals, values, or motivations that are important to my customers.
I begin by experimenting with word pairs that serve as axes on the matrix. Then, I place the idea post-its in the appropriate quadrants, using the x- and y-axis as relative locations for how well the idea achieves the two goals. Finally, I iterate this process until I am able to find the best idea from my brainstorming session.
Some axes I used for the 2x2 Matrices are:
From this Brainstorming and 2x2 Matrix exercise, I came up with a best idea to propose as a new functionality for our product.
The idea proposes a new stock research tool that maximizes time-saving as well as trustworthiness. This feature correlates News Headlines with significant inflection points (significant peaks and dips) on the stock's graph. Then, it shows related current news on the stock. This feature is time-saving because it minimizes the user's search for articles. It also minimizes the user's need to guess why significant price changes occur. This feature is trustworthy because the headlines and related news all come from reputable news sources that users are already familiar with or already use for their current stock research process.
To pitch this idea to the rest of the team, I used another UX exercise: Storyboarding.
I use Storyboarding when I have an idea and want feedback on how well it solves the customer's problem, meets the customer's criteria, or delivers a big customer benefit. It helps enable my team to iterate quickly on new concepts before spending time designing or building mockups. It also helps gain deeper insight on customer experience.
I selected my best idea, described in the last section, to storyboard. I created a script, from the shoes of the user who faces an investing problem or challenge. I, then, pitched the new idea and showed how this would benefit the customer. I asked a potential customer to act out each scene of the storyboard to show my team how this idea would help solve user's problems.
After pitching the new idea to the rest of the team, we discussed its effectiveness in solving our customers' problems, as well as its feasibility from an engineering perspective. Finally, the team approved the idea. We have now begun the process of designing prototypes of the feature, which we will then test with users, iterate on, and eventually implement in our product.
Meanwhile, I will begin collecting new research in order to iterate through the UX process once again and help identify new customer needs and potential solutions.
Download our app from the Play Store or the App Store!
I am really thankful to Bloom's founder, designer, and engineer for giving me the opportunity to work with them. They have offered me tons of guidance and advice in how to learn more about the user experience field. I am thankful for the opportunity and I hope to continue to learn from them!