Investment Data Marketing: The 14 Profile Datasets, Explained

Investment data marketing is a deceptively difficult strategy to develop and execute.

It’s not just choosing what data to publish and which databases to publish to, but it also requires an understanding of how best to position your data in the databases in order to accomplish two things:

  1. Generate an unsolicited inquiry from a database subscriber and/or consultant, and
  2. Support and reinforce the story you’ve already been telling your clients and prospects.

When onboarding new clients or in the course of the consulting work we do on behalf of our existing clients, we have found that “data” can often lose its meaning because it means so much, yet so much of it is ill-defined.

Quantitative data? These are the numbers; the stuff that’s measurable.

Qualitative data? Those are the words; the story and the people.

Beyond these simplistic definitions, what do we mean when we say “data,” and how does each dataset contained therein fit together in a comprehensive data marketing strategy?

The 14 Datasets

Many data firms will list five main datasets. Some will even expand that to nine datasets.

We, however, see merit in further breaking down the data categories. Parsing definitions more closely enables our manager clients to keep a better handle on what we are doing, conceptually, and allows them to better understand the decisions we are making together.

We believe demarcating clearly between what is happening at the firm level, and what’s at the strategy/product/vehicle level are important distinctions that make for a truly differentiating database strategy.

Let’s take a look at them one at a time:

1. Firm Narratives

A firm’s narratives are the most important dataset. Humans are attracted to great storytelling, and the narratives are your firm’s best opportunity to tell that story. In fact, we believe that it’s the job of the quantitative to validate the qualitative, not the other way around.

You say you’re a contrarian manager? The portfolio characteristics will bear that out. That you are micro cap? Look at the holdings and we’ll see. Highly experienced with a long track record of success? OK, let’s look at your personnel. Make sure those narratives are impeccable and are substantiated by your quantitative datasets

2. Firm Personnel

We have written about this before. Who comprises the firm’s top management? What’s their experience? Who’s come and gone in recent years? A small cap contrarian strategy run by the former large cap manager from a global asset manager might be OK, but the dichotomy between their experience and the current strategy might be important.

And though it shouldn’t need to be said, a single employee who wears several hats (Portfolio Manager, CEO, and Director of Research) is still only 1 person – not three (yes, we’ve seen it).

3. Firm AUM & Breakdowns

In the past, we have highlighted the importance of AUM in a firm’s data strategy. These datasets tell prospective investors how many assets you have under management, and with whom do you work.

If you want to work with endowments, it helps to note in your database profiles how much of your AUM comes from endowments, for example. These are also important datasets because your firm AUM should substantiate the sum of your product AUM.

For example, if you have 4 products totaling $1.5 billion, but firm AUM is listed at $5 billion, with no explanation, it raises some red flags:

  • Do you have other products you aren’t marketing, and why?
  • Is this an error that calls into question your data reconciliation process?
  • What accounts for this discrepancy?

Good data strategy generates some important questions in the mind of the database subscriber, but one that potentially calls into question your data management practices isn’t one of them.

4. Product Narratives

A product’s specific process and philosophy should reflect the larger ones detailed in the Firm Narratives, but with more detail and nuance (if a firm has multiple strategies). Again, what you say in the philosophy and process must be reflected in portfolio characteristics and holdings.

The inability for a database subscriber to cross-check your narrative and quantitative elements is a big-time red flag that managers should be diligent about avoiding.

5. Product Personnel

Again, all the items we cite above remain true here, with one big addition: Is the manager/s responsible for the fund’s track record still around? If a firm touts top decile performance over a 10-year period, but the manager responsible left in December, that’s an important development about which a database subscriber should be aware.

Transparency is important.

6. Product AUM & Breakdowns

Again, these need to substantiate the firm’s topline datasets. Everything we cite above is true here, just reversed. In our example above, if you say you have $400 million from endowments, but none of your products list an endowment as an asset source, then that could be an issue.

Both firm & vehicle breakdowns help to demonstrate that you have the capacity and the ability to work with the types of clients with whom you claim to work.

7. Product Personnel Key Biographies

It is one thing to have a list of portfolio managers and executives, alongside their tenure dates. It is quite another to craft an eloquent narrative describing the details of the executive team’s education, experience, and accomplishments. Providing a bio helps to establish connections and foster transparency. If the purpose of due diligence is to empower an investor or consultant to develop familiarity with a firm before reaching out, narrative-based biographies of the key decision makers in the product is essential.

8. Vehcle Performance

This dataset is deceptively complex, for investment data marketing purposes. Because, as the name suggests, it’s not simply the performance of a specific product, but each of the underlying vehicles. Should a manager publish each vehicle in its entirety? If not, why? And can a manager get away with publishing performance-only? If so, under what circumstances and why?

Without adequate advice and consulting, managers can easily fall into a common trap: doing what the database tells them to do, which is often is not in the manager’s best interests.

9. Vehicle Holdings

Assessing whether to publish your holdings is a decision that has vexed many investment managers over the years. After all, if the holdings are the net result of the investment management process, doesn’t that yield to your competition some competitive advantage?

In our view, publishing holdings is a key component to one’s investment data strategy, though under certain circumstances we have found workarounds when a firm’s holdings are, truly, a trade secret.

10. Vehicle Portfolio Characteristics

The vehicle’s portfolio characteristics dataset offers managers some cover when they decline to publish their holdings, but it requires precise strategic work that is quite nuanced. More broadly, however, portfolio characteristics are extremely important when validating the other datasets.

For example, if you say you are a small cap contrarian manager, then the portfolio characteristics (and holdings, obviously), will either confirm or repudiate your assertion. Weightings and allocations tell an important part of your story- these need to be thoughtfully considered as well.

11. Vehicle AUM

Similar to Product & Firm AUM, by providing database subscribers and consultants with increasingly granular detail on which investor types are invested in a given vehicle, along with the total amounts, managers can offer potential clients with transparency, as well as reassurance that you do what you say and others have found efficacy to your approach.

12. Behnchmark Returns

Of course, context matters. 1.3% returns can be fantastic, gut-wrenching, or par for the course, so it is important to provide database subscribers and consultants an appropriate benchmark for comparison.

Importantly, a manager’s choice of benchmark (if it’s interesting or counter-intuitive in some way) can offer an important area of discussion between a manager and a database subscriber or consultant. Good data strategy is one that prompts an unsolicited inquiry – the more things that highlight the uniqueness of your approach and outlook, the better.

13. Vehicle NAV

The relevance of the NAV depends upon the type of product that is being marketed. If it’s a mutual find, closed end fund, or a hedge fund, the NAV is a customary datapoint that any interested investor will need to know, so it makes sense to publish it. As we’ve noted, sometimes it is a good idea to hold specific details for a few strategic datasets in order to prompt an unsolicited inquiry. NAV isn’t one of them. Provide it up front.

14. Vehicle Fees

While there’s the old Warren Buffet line about, “price is what you pay, value is what you get,” it’s the price of your services that can make or break the final decision about whether or not a relationship gets formalized. This is why your fees might be the simplest dataset to understand and to publish, but also arguably one of the most important. For decades now, fee compression has been one the industry’s most stubborn headwinds.

(By the way, one of the databases we publish with, Investment Metrics, has a great tool, the Fee Analyzer, that helps managers to better understand what’s happening with regard to fees in the industry. It’s an important piece of intelligence managers can use to better position themselves.)

In the end…

The name of the game is substantiation and enticement. It’s far too easy for managers to either take a minimalist approach to database publishing, which restrains effective due diligence, or to follow the advice of the databases themselves and publish everything under the sun, which leaves no questions to be asked (and thus no reason for that all-important unsolicited inquiry).

The only way to strike the right balance is through effective investment database marketing.