Survey Design and Analysis ▫ Data Analysis ▫ Secondary Data ▫ Exploratory Interviews ▫ Atomic Research ▫ Mixed-Methods Reports

Research Process

Version 1: Current Members’ Data

1.1 Member Data Analysis

We initiated this study in close collaboration with the Data Science team. We engaged Product Managers, Designers, and Engineers in brainstorming to identify relevant data points for potential segmentation. Simultaneously, we revisited our research repository to identify clues that could serve as indicators for these variables. The Data team scanned for data availability and volume to support the methods we intended to use.

We employed a K-prototype algorithm to transform the resulting dataset into groups, incorporating both categorical and numerical data. This approach allowed us to create groups where individuals within each group were as similar as possible while maintaining distinct differences between the groups.

With the segmented data, I conducted in-depth analysis and provided the team with easily digestible insights to inform daily product decisions.

1.2 Qualitative Fieldwork

By linking customer IDs to the five segments, we cross-referenced the segments with interviews conducted in the previous year. We began collecting quotes related to each segment's hypotheses, following a tagging taxonomy designed for this purpose. These quotes formed the basis of our initial segment insights.

Subsequently, we conducted qualitative interviews for each of the segments to gather additional insights and fill in the missing pieces for each hypothesis.

1.3 Segment Interviews and Reports

We organized separate interview rounds for each segment, with a set of questions common to all segments and a section specific to the hypotheses derived from the previous step. We expanded our taxonomy and compiled insights that highlighted the unique ways in which each segment approached financial thinking and behavior.

Version 2: Future Customers

As the organization shifted its strategy towards targeting the direct-to-consumer market, concerns arose that future customers might differ significantly from our existing ones. Since the current customers received the product through their employers for free, and future customers would be paying a substantial subscription fee, significant differences were expected.

Recognizing the potential for new product opportunities, I made the decision to step back and gain a deeper understanding of how the broader U.S. population manages finances and financial tools, covering a wide range of financial topics beyond personal finance management.

2.1 Secondary Data Analysis

The second version of the study focused on secondary data and market reports. We leveraged existing studies to learn from and compiled a comprehensive desk research document with links to various sources. Of particular interest were the data points used by these sources to group people together, allowing us to draw comparisons with our member data.

We identified two reports that used investable assets brackets instead of income brackets, and with guidance from our in-house Financial Planner and Product Advisor, we recognized this as an ideal lens to observe potential customers. However, limited market data using this approach necessitated conducting our analysis on public datasets from the Federal Reserve. This approach expanded our understanding of various financial topics, from income and spending to investments, insurance, and financial goals, creating a unique report despite using market data.

2.2 Segmentation Survey

The first step in this version of the study was already groundbreaking, as it generated significant momentum and inquiries beyond what public datasets could address. We compiled a document for collecting questions about the investable assets segments, including pain points and the types of financial products used. This feedback guided our data collection efforts.

The goal of the segmentation survey was to bridge the gap between our secondary data reports and the insights gathered in Version 1. We designed unbiased and coordinated questions to understand demographics and their sources of financial stress and struggles. We also included a Van Westendorp Price Sensitivity test to gauge the willingness of people to pay for our product, helping us refine pricing strategies.

Results and Impact

Establishing a Company-Wide Understanding

of Customer Segments

This project successfully addressed the challenge of finding a common language and data framework to discuss customers, moving away from confusing, hypothesis-based personas. Upon presenting the segments in company-wide meetings, team members began using segment names in their daily decision-making processes. This shift in mindset led to increased engagement with our material and a surge in inquiries, which we are prioritizing for future projects.