Using MaxDiff to Understand Investors’ Decision to Hire A Financial Advisor
Category: Financial Services
Methods: Maximum Difference (MaxDiff) Analysis
A financial advisory firm wanted to understand the reasoning behind investors’ decisions to hire (or to not hire) a financial advisor. Decision Analyst conducted an online quantitative survey featuring MaxDiff analysis to determine the attributes that have the most impact on potential customers’ decision to hire (or not hire) a financial advisor.
While we know that most investors understand the overall benefits that a financial advisor provides, what are the actual drivers behind their decision to actually hire one?
The client had conducted extensive qualitative ethnographic research to understand the thought processes around hiring and selecting a financial advisor. More than 40 factors were unveiled, as well as several obstacles that tended to “block” a potential investors’ likelihood to consider hiring an advisor in the future.
Our client sought to further model those inputs using quantitative research to understand their contribution to the decision to hire (or to not hire) a financial advisor. In turn, this would allow the client to be able to customize their messaging by honing in on a potential customer’s specific life stage, demographics, financial goals, and concerns.
Decision Analyst recommended testing these factors via an online survey using MaxDiff analysis.
The primary objective for this research was to understand the impact that various attributes and life events have on the likelihood to hire, or to not hire, a financial advisor
Research Design and Methods
A total of 1,000 investors completed the online survey that included multiple MaxDiff exercises.
For each exercise, respondents were shown a series of attributes across several screens and were asked to select the attribute that would have the most impact on their hiring a financial advisor, and the attribute that would have the least impact.
After the data collection was completed, a Hierarchical Bayes (HB) choice model was run to produce unique statement importance scores for each individual respondent.
In addition to the choice tasks, respondents were asked several questions about their experience with investments, their comfort level across various financial topics, and demographics.
This provided Decision Analyst with the ability to report the MaxDiff results by several key groups. For example, do the top motivators differ by age group, level of financial literacy, or by those who have used a financial advisor in the past (but do not use one currently)?
As a result of the MaxDiff analysis, our client was able to use the outcomes to:
- Prioritize messages
- Craft new messages
- Customize messages to specific groups of potential customers
- Educate their financial advisors on when to offer products and services to clients and prospects
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