Data-Driven Techniques to Anticipate and Mitigate Asset Outflows (Part 2)


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In Part 1 of this series, we discussed why firms need to rebalance their focus on both acquiring net new assets and minimizing redemptions. In this installment, we address new data-driven techniques to anticipate and mitigate asset outflows. In Part 3, Steven Miyao of kasina will discuss changes to wholesaler compensation that align individual incentives with overall firm goals.

Today, the standard response to redemptions is largely reactionary: Marketing and sales activities are triggered once redemptions occur. In many cases, the firm and its wholesalers will fail to identify and contact at-risk advisors in-time, even when the advisors have already exhibited a clearly discernible pattern of redemption activity. When the outreach finally occurs – – perhaps weeks after the advisor has already decided to move out of the fund – – it often lacks a well-defined alternative ‘play’ to keep the assets. Not surprisingly, speaking to advisors long after they have psychologically fired your firm, or at least decided to jettison one of your funds, can serve to accelerate the redemptions. Advisors cannot be expected to suddenly stop executing their plan because you kindly ask them not to.

A more dynamic and data-driven approach to redemptions incorporates three lines of defense.

1. Avoid: Eliminate the advisor’s intention to redeem your funds in the first place

2. Anticipate: Alert your wholesalers to probable redemption activity while there is still time to influence the advisor

3. Target: Engage the at-risk advisor as early as possible with a tailored conversation that aims to keep the assets at your firm

Predictive Analytics plays a key role on all three fronts. Predictive Analytics encompasses statistical techniques to analyze historical data and make predictions about future events. Consumer companies like Amazon and Netflix have popularized predictive analytics by offering targeted product recommendations on books and movies based on historical shopping patterns. Increasingly, many B2B companies are applying these techniques to boost sales and marketing productivity. The insight is that the explosive growth of data about companies and purchasing decision makers contains hidden but invaluable intelligence for frontline sales teams. In any business with high distribution channel costs, predictive sales intelligence enables managers to focus scarce sales and marketing resources on the most attractive market opportunities.

How does Predictive Analytics drive the three lines of defense? Avoiding redemptions can be achieved by acquiring more loyal advisors in the first place, and making the advisor relationships you have stickier. Anticipating redemptions can be achieved by understanding early triggers or signs that an advisor’s interest may be waning. These signs are typically a combination of external factors such as fund performance, coupled with tell-tale patterns of advisor transactional behavior. Finally, when you believe that assets may be in peril, engaging with the advisor with customized messaging can significantly improve the odds of retaining the assets with your firm.

The figure below gives specific examples of Predictive Analytics Plays:

Your firm can operationalize a data-driven approach to redemptions by following the four steps below: (click here to read the expanded version of these steps in the Whitepaper: ‘Analytics in Action: Winning in Asset Management Distribution with Predictive Sales Intelligence):

1. Assess your distribution team’s current practice and needs: Analytics do not work if they cannot be incorporated into current practice without undue turmoil.

2. Conduct a Data Audit: None of this can be achieved without the necessary data. Much of this data is transactional in nature, but other sources related to advisor demographics, size of book, and fund performance can be very powerful.

3. Build an Analytics Foundation: Analytics is done from the ground up. Developing the advisor’s profile and current value is the foundation of predictive analytics. With this foundation in place, consider straightforward, strategic segmentations of the advisor base. After this step, move on to predictive modeling. The final step in this analytics progression is implementing an operational solution that makes ongoing recommendations to wholesalers on which advisors to contact, at what frequency, and with what messaging.

4. Test, Iterate and Improve: Data and predictive sales intelligence can deliver immediate benefits, but winning over the long term requires a commitment to testing and continuous iteration.

Do you have cutting-edge advice on managing redemptions? We’d love to hear from you.

Republished with author's permission from original post.

Ian J. Scott
Dr. Ian J. Scott is the VP of Customer Solutions for Lattice Engines. Prior to that, Ian served as CTO for Angoss. During his career, he has conducted quantitative risk assessment for UBS and also worked for CFM, a Paris-based hedge fund. Dr. Scott holds a Ph.D. in Physics from Harvard and a B.Sc. from McGill.


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