How good is your investment sales team?

We recently described how some asset managers are changing the distribution of their products with artificial intelligence (AI).

We call this distribution analysis. Conversion requires overcoming three key challenges: qualifying for inefficient prospects, inconsistent sales processes and sidelined forecasting. The focus there was on priorities and qualifications. Here, we consider the second challenge: evaluating sales performance.

Much has been written about how to differentiate fortune from investment management skills. But how can we tell if the sales team is doing well? We can certainly look at their commissions, but that doesn’t seem to be entirely satisfactory. Inside Policy, Ray Dalio advises us “[Pay] Focusing more on the swing than the shot, ”focusing more on the process than the result.

For example, imagine you are on the sales team of Bridgewater Associates. This is April 2020, Covid-1ra is running and you have lost only 20% of your flagship funds. Dalio admits he was “blinded” by the epidemic. You may not be able to attract any flow in the second quarter. In fact, outflows are more likely. But what you do and what you say to clients in the coming quarters can still make a big difference.

How should your firm evaluate your performance in Q2? Of course not by looking at your commission.

The combination of factors drives the flow of assets into an investment product:

  • The power of sales and relationships
  • Marketing and brand strength
  • Product performance
  • Fate
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Many asset managers struggle to separate these issues. And it’s a high-stack struggle. Those who focus on outcomes such as commission or asset management (AUM) have a hard time holding their parties accountable. Sales complain that marketing is offering weak prospects. Marketing complains that product performance is not competitive enough. Meanwhile, portfolio managers complain that they are being misunderstood by the market.

By sorting out these effects, clients can evaluate which parts of their business are working and which are not. They can then course-modify and improve. In Genpact, our structure begins with the balance sheet equation: AUM ending = AUM + return on investment + asset flow starting.

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

On the left of the table below, we divide the total return of a product into three parts: market, division and product return and use a concrete example: PIMCO’s Active Bond Exchange-Trading Fund (ETF) 13 July 2020:

Entity YTD returns
Market Bloomberg / Barclays Total Return USD 5.82%
Department Intermediate Core-Plus Bonds 5.11%
Products Pimco Active Bond ETF 5.28%

Source: Morningstar. Accessed 14 July 2020.

From these figures, we calculate “Division vs. Market Return” as -0.71%. Since this is negative, there was no place to stay in the core-plus bond market in 2020. On the other hand, “product vs. category return” + 0.17%, indicates that this PIMCO portfolio management team has done well within the range of its orders. Pimco’s executive management should probably evaluate the performance of this team using “product vs. class return” instead of “division vs. market return”. After all, Pimco is paying the team to build the best possible core-plus portfolio, not to choose the winning categories.

We do a similar analysis of asset flows, shown on the right side of the table below. We cannot compare them with direct investment returns, as they are on different scales.

Entity YTD flows until 13 July 2020 AUM as of 1 January 2020
Market Bloomberg / Barclays Total Return USD – $ 44,183 m $ 9,597,750 m
Department Intermediate Core-Plus Bonds – $ 2,345 m $ 959,775 m
Products Pimco Active Bond ETF $ 507 m $ 2,925 m

Sources:, Baird, Sifma. Class flow and AUM placeholders. See note below.

It helps to think in terms of market share:

  • Category vs. Market Flow: In this fact set, 10% of the bond market was allocated to the core-plus segment at the beginning of the period. If its market share had remained stable, the core-plus category would have lost 10% of the market or, 4,418 million. It actually did better than that, so its “division vs. market flow” is positive: -2,345 – (- 4,418) = $ 2,073 million.
  • Product vs. Division Flow: At the beginning of the period ETF occupied 0.30% of the core-plus segment. If its shares had remained stable, the ETF category would have lost 0.30% of its outflow, or about 7 7 million. It actually had a flow of 7 507 million, so its “product vs. class flow” was 507 – (- 7) = $ 514 million.

The summary of our analysis for Pimco ETF for the period January 1 to July 12, 2020 is as follows:

Division vs. Market Products vs. Division
Return -0.71% 0.17%
Is flowing $ 2,073 m $ 514 m

The goal of our structure is to multiply each of these to a different group. Of course, no team is an island, but this method helps to provide some useful differences.

Division vs. Market Products vs. Division
Return Strong Leadership Leadership Portfolio management
Is flowing Marketing + Leadership Leadership Sales + Portfolio Management

Returns are relatively easy to qualitatively:

  • Portfolio managers are most responsible for “product vs. category returns”.
  • The executive leaders who set up the firm’s product lineup are most responsible for the “category vs. market return” metric. The better they are to get into the winning section and get out of the backward sections, the more this metric.

More difficult for flow source:

  • Sales are most responsible for the “Product vs. Category Flow” metric, but portfolio managers also influence it. As many investors follow performance, past earnings will affect current flows.
  • Marketing is most responsible for the “division vs. market flow” metric because they must translate the firm’s product lineup into an attractive brand. However, strong leadership also affects it. Easy to sell categories with good past performance. To use a poker metaphor, strong leadership marketing must play.
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To separate sales from product performance, we use the following regression:

Product vs. class flowing in the present tense

In this equation B Regression coefficient and A A measure of value added by the sales team, similar A A Capital Asset Value Model (CAPM). Say another way, A Actual flow vs. those historical product performance will be expected given.

Following the same logic, we use this regression to differentiate marketing from category performance:

Category vs. Market Flow Current Time = β * Category vs. Market Returns Past +

The above equations are simple regression with a factor: performance over the past period, say the previous 12 months. In practice, we extend to include:

  • Multiple past tenses
  • Other past performance measures, e.g., instability, withdrawal, etc.
  • More flexible model forms, support non-linear relationships

As we add factor and flexibility, we fit the data better A A pure measure of sales and marketing efficiency, respectively. This will be similar to the various extensions of CAPM for creating returns A A pure measure of investment efficiency. Following that literature, we use various tests to make sure we don’t over-enjoy the data.

Through these methods, clients gain insight into how their sales teams are working and where they can be improved.


We are indebted to Jan Jap Heisenberg for “a new framework for analyzing market share dynamics within the fund family” Financial Analyst Journal For many structures and analysis.

Heisenberg decomposes market share changes using relative flows and AUM-weighted returns. We present a simplified version that replaces relative flows with dollar flows and weighted earnings with easy returns. We would like to thank Hazenberg for his assistance in reviewing its structure and results.

To analyze the flow of PIMCO ETF, we used the following sources:

  • ETF flows from until 13 July 2020.
  • Bond market flow from Baird until May 2020.
  • December Historical ETF Net Asset Value (NAV) from Pimco’s half-yearly report to 31 December 2019.
  • Bond market size from SIFMA. We show corporate debt arrears as of Q4 2019.
  • Class flow and AUM are the placeholders used to explain this calculation. The actual figures are available from various sources, such as Lipper, Investment Company Institute (ICI), Broadridge and Marketmetrics.

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All posts are the author’s opinion. As such, they should not be construed as investment advice, or the opinions expressed must not reflect the views of the CFA Institute or the author’s employer.

Photo Credit: © Getty Images / Vipopovic

Alan Bochman, CFA

Alan Bochman, CFA, New York-based Genpacts (NYSE: G) is a partner in capital market consulting practice. He works with asset managers and banks to help them make better decisions with data. Prior to that, he spent two years managing an equity portfolio for SC Fundamentals. Bochman began his career as a programmer by co-founding a social networking software firm acquired under Thomson-Reuters. He holds an MBA from Columbia Business School and a BA from Albany University.

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