How Good Is Your Investment Sales Team?

We recently described how some asset managers are transforming their product distribution with artificial intelligence (AI).

We call this distribution analytics. The transformation requires overcoming three key challenges: inefficient prospect qualification, inconsistent sales processes, and siloed forecasting. There the focus was on target prioritization and qualification. Here, we consider the second challenge: sales performance evaluation.

Much has been written on how to separate luck from skill in investment management. But how can we tell if the sales team is doing a good job? We could, of course, simply look at their commissions, but that doesn’t seem fully satisfactory. In Principles, Ray Dalio advises us to “[Pay] more attention to the swing than the shot,” to focus more on the process than the outcome.

For instance, imagine you’re on the sales team at Bridgewater Associates. It’s April 2020, COVID-19 is raging and your flagship fund just lost 20%. Dalio admits that he was “blindsided” by the pandemic. You may not be able to attract any inflows at all in the second quarter. In fact, outflows are more likely. But what you do and what you say to clients over the coming quarter can still make a big difference.

How should your firm evaluate your performance in Q2? Surely not just by looking at your commissions.

A mix of factors drives asset flows into an investment product:

• Sales and relationship strength
• Marketing and brand strength
• Product performance
• Luck

Many asset managers struggle to separate these factors. And it’s a high-stakes struggle. Those that focus on such outcomes as commissions or assets under management (AUM) have a hard time holding teams accountable. Sales complains that marketing is delivering poor prospects. Marketing complains that product performance isn’t competitive enough. Meanwhile, portfolio managers complain they are misunderstood by the market.

By sorting out these influences, clients can evaluate which parts of their business are working and which aren’t. They can then course-correct and make improvements. At Genpact, our framework begins with the balance sheet equation: Ending AUM = Beginning AUM + Investment Return + Asset Flows.

For now, let’s ignore distributions and non-organic growth.

On the left side of the following table, we break a product’s total return down into three components: market, category, and product returns and use a concrete example: PIMCO’s Active Bond exchange-traded fund (ETF) (Ticker: BOND) as of 13 July 2020:

Source: Morningstar. Accessed 14 July 2020.

From these figures, we calculate the “Category vs. Market Return” as -0.71%. Since this is negative, Core-Plus was not the place to be in the bond market in 2020. On the other hand, the “Product vs. Category Return” is +0.17%, indicating this PIMCO portfolio management team did well within the confines of its mandate. PIMCO’s executive management should probably evaluate this team’s performance using “Product vs. Category Return” rather than “Category vs. Market Return.” After all, PIMCO is paying this team to form the best possible Core-Plus portfolio, not to pick winning categories.

We perform a similar analysis on asset flows, shown on the right side of the table below. We cannot compare them directly as with investment returns, however, because they are at different scales.

Sources: ETFdb.com, Baird, SIFMA. Category flow and AUM are placeholders. See notes below.

It helps to think in terms of market share:

• Category vs. Market Flows: In this fact set, 10% of the bond market was allocated to the Core-Plus category at the beginning of the period. If its market share had remained constant, the Core-Plus category would have suffered 10% of the market’s outflows, or $4,418 million. It actually did better than that, so its “Category vs. Market Flows” are positive: -2,345 – (-4,418) = $2,073 million.
• Product vs. Category Flows: The ETF captured 0.30% of the Core-Plus category at the beginning of the period. If its share had remained constant, the ETF would have suffered 0.30% of the category outflows or approximately $7 million. It actually had inflows of $507 million, so its “Product vs. Category Flows” were 507 – (-7) = $514 million.

The summary of our analysis for PIMCO’s ETF for the period of 1 January to 12 July 2020 is as follows:

The goal of our framework is to attribute each of these to a different team. Of course, no team is an island, but this approach helps provide some useful distinctions.

Returns are relatively easier to attribute:

• Portfolio managers are most responsible for the “Product vs. Category Return.”
• Executive leaders who set the firm’s product lineup are most responsible for the “Category vs. Market Return” metric. The better they are at entering winning categories and exiting lagging ones, the higher this metric goes.

Flows are more difficult to source:

• Sales is most responsible for the “Product vs. Category Flows” metric, but portfolio managers influence it as well. Since many investors chase performance, past returns will influence current flows.
• Marketing is most responsible for the “Category vs. Market Flows” metric because they must translate the firm’s product lineup into an attractive brand. However, firm leadership impacts this, too. Categories with good past performance are easier to sell. To use a poker metaphor, firm leadership deals the hand that marketing must play.

To isolate sales from product performance, we use the following regression:

Product vs. Category Flows in Current
Period = β * Product vs. Category Returns in Past Period + α

In this equation β is the regression coefficient and α is a measure of the value added by the sales team, similar to α in a capital asset pricing model (CAPM). Put another way, α is the actual flows vs. those that would be expected given historical product performance.

Following the same logic, we isolate marketing from category
performance using this regression:

Category vs. Market Flows in Current
Period = β * Category vs. Market Returns in Past Period + α

The equations above are simple regressions with one factor: performance in a past period, say the prior 12 months. In practice, we expand them to include:

• Multiple past periods
• Other past performance
  measures, e.g., volatility, drawdown, etc.
• More flexible model
  forms, supporting non-linear relationships

As we add factors and flexibility, we fit the data better and make the α a purer measure of sales and marketing skill, respectively. This would be similar to the various extensions of CAPM for returns, making α a purer measure of investment skill. Following that literature, we use several tests to ensure we do not overfit the data.

With these methods, clients gain
insight into how their sales teams are performing and where they might be
improved.

Notes

We are indebted to Jan Jaap Hazenberg’s “A New Framework for Analyzing Market Share Dynamics among Fund Families,” from the Financial Analysts Journal for much of the framework and analysis.

Hazenberg uses relative flows and AUM-weighted returns to decompose market share changes. We present a simplified version that replaces relative flows with dollar flows and weighted returns with simple returns. We would like to thank Hazenberg for his help in reviewing his framework and findings.

In analyzing the PIMCO ETF’s flows, we used the following sources:

• ETF flows are from ETFdb.com through 13 July 2020.
• Bond market flows are from Baird through May 2020.
• Historical ETF net asset value (NAV) is from PIMCO’s semi-annual report as of 31 December 2019.
• Bond market size is from SIFMA. We show corporate debt outstanding as of Q4 2019.
• Category flows and AUM are placeholders used to illustrate this calculation. The real figures are available from a variety of sources, such as Lipper, the Investment Company Institute (ICI), Broadridge, and MarketMetrics.

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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.

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