How to Use Data as a Financial Advisor and Improve Outcomes

Betterment’s mission is to help people become smarter investors by using smarter technology. To do that, we have embraced data, helping us to make decisions that drive product development and produce meaningful results.

One way we help investors is by pursuing behavior gap zero—that is, closing the gap between what an investor should take home and what he does take home.

Our product feature Tax Impact Preview is already showing to be an effective tool in this pursuit. Based on initial data, customers who used the tool, which shows the tax cost for the sale of shares, are 62% less likely to change their portfolio allocation when the cost was greater than $5. For us, this is a sign that we’re measurably closing the gap through better design.

Some financial advisors calculate tax impacts for their clients before transactions, because this information is essential in assessing the cost of a trade. We are the only smart investing service to build this into an automatically available feature and to measure the effect it has on individual investors.

If you want to bring a more data-driven approach to your work as a financial advisor, consider implementing some of the tools and processes that we use at Betterment (or learn more about Betterment Institutional, our platform for advisors).

1. Create your own in-house data team.

Last year, our engineering leadership approached me with a proposal to form an in-house data team. For a growing company, that’s a lot of resources to go into one standalone team.

But they convinced me that it would create the tightest possible feedback loop between what we could do for our customers and our technology. Traditionally, business intelligence tooling has been something that comes at a great upfront cost to an organization (it can reach into the millions of dollars). But I entrusted them to create a team in the most pragmatic way possible, and the result is our dedicated data group at Betterment called Polaris. (You can read more about Polaris’ work here and here.)

Today, the team maintains a robust data source and analytics system for the company, allowing our engineers, product managers, and investment team to test hypotheses and iterate quickly.

2. Don’t rely on found data.

Too many companies just try to mine the data that they already have: data that is a byproduct of other activities. This might be termed ‘found data.’

For example, that could be data from server logs about when customers log in. The problem with found data is that too often its very convenience is what drives the types of questions that your company asks and answers.

However, a better way to think about data is to start with the questions, not the data. Good analyses start with an important question and consider whether there is evidence for a conclusion.

Searching the most convenient data for insights offers many tempting opportunities to see interesting or unanticipated patterns, but these are not as likely to hold up in the future.

3. Define the questions and generate the data you want to analyze.

So, now you know you should start with questions, rather than found data. What kinds of questions do you want to answer?

As we’re building a feature, we ask ourselves: What is worth measuring and tracking when the customer interacts with this? What kind of behavior do we want to know more about? If we do X, will we get Y? With every product and improvement we add to our service, we create a way to track and record actionable data.

4. Improve your customers’ outcomes.

Using the robust measuring analytics and data collection system set up by our in-house team, we can see where customers are starting to behave in ways that could hurt their chances of reaching their financial goals.

Let’s return to our new Tax Impact Preview feature and how the process worked.

We started with a hypothesis: Customers would be less likely to make an allocation change if they saw in advance the actual tax hit they would incur with short-term capital gains. Next, we created a demo and user-tested it. We incorporated feedback and then we built it, complete with data-tracking features so that we could measure results in the field.

After three weeks, our data showed that customers who considered making an allocation change—but who used the tool and saw that their estimated taxes would be greater than $5—were 62% less likely to follow through on the change. Statistically speaking, that is a huge improvement in behavior, and we’re proud that we’re able to provide a smart tool that helps customers make smarter decisions. It’s just one example of how we’re implementing data into our work as a financial advisor.

There are many ways to use data as a financial advisor—this is simply one example. But the guidelines are the same: Dedicate resources, don’t rely on convenience, ask the right questions, and put the data to work for your customers.

A version of this article originally appeared on Investment News and Betterment.

More from Betterment:

• Building a Data Team from the Group Up (Video)
• SmartDeposit: Auto-Deposit, But Smarter
• How to Earn Returns Through Volatile Times

Jon Stein is the CEO and founder of Betterment, the largest and fastest-growing automated investing service. Passionate about making life better, and with his experience from his career of advising banks and brokers on risk and products, he founded Betterment in 2008. Jon is a graduate of Harvard University and Columbia Business School, and he holds Series 7, 24, 63, and is a CFA charterholder. His interests lie at the intersection of behavior, psychology, and economics. What excites him most about his work is making everyday activities and products more efficient, accessible, and easy to use. Learn more about Betterment here.