Client retention is the lifeblood of wealth management organizations. In fact, obtaining a new client can be as much as five times more expensive as retaining an existing client. Many factors can impact churn at these types of organizations, but often management fees, transaction fees, and advisory fees are examined as reasons why a client may choose to stay with a firm or explore other options.
If you aren’t familiar, management fees are charges paid to investment managers for overseeing a client's portfolio, typically calculated as a percentage of assets under management. Transaction fees are costs incurred each time an investment is bought or sold. Advisory fees are fees paid to financial advisors for their guidance, which can be charged as a flat fee, hourly rate, or percentage of assets under management. All of these fees are relatively standard in the wealth management industry.
For our example today, we generated a dataset from a hypothetical wealth management firm that was experiencing higher-than-usual client churn. We took a random sample of 30 accounts from the past five years, and 13 of the clients have left. We wanted to determine whether any of these three variables impacted the customer churn.
We used binary logistic regression to analyze the relationship among these variables and retention. Binary logistic regression would be used in this context to analyze how management fees, transaction fees, and advisory fees influence client retention status, allowing for data-driven insights into which factors significantly affect whether clients choose to stay with the firm.
We used stepwise regression to automatically select an appropriate model. Here are our results:
After analyzing the data, we gained many insights. One stood out:
The Significant Impact of Advisory Fees: The analysis reveals a statistically significant positive relationship between advisory fees and client retention, with a p-value of 0.012. While this was initially surprising, the data suggests that higher advisory fees are associated with an increased likelihood of clients choosing to stay with the firm, indicating a perceived value in the advisory services provided.
Neither management fees nor transaction fees had a statistically significant impact on client retention.
Further, we used Minitab to generate a binary fitted line plot focused on retention status:
The blue data points on the top of the graph show clients who were retained while the dots on the bottom represent churn. We could now visually see that, generally, as advisory fees increased, so did buy-in and retention.
The organization can now use this data to make informed decisions. For example, if the goal is to increase retention YoY to 95%, advisory fees can be set to 0.9% or higher. This approach not only enhances client satisfaction but also promotes continuous process improvement, ensuring that services align with client expectations and contribute to overall business success. And, in this case, it could help prevent mistakes, like cutting advisory fees, which would negatively impact the bottom line.
Moving forward, focusing on data to improve processes can help organizations increase retention rates and provide better value to clients over time.