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Developing Strategy With Behavioral Data

The Data Possible Podcast

Episode 3: Developing Strategy With Behavioral Data

Guests:
Duncan MacDonald-Korth, CEO, AdvisorTarget
Jeff Tripp, CDO, AdvisorTarget

Summary: In this episode of The Data Possible Podcast, we speak with AdvisorTarget CEO, Duncan MacDonald-Korth, and CDO, Jeff Tripp. Duncan and Jeff discuss how behavioral data is critical in developing strong sales, marketing, and recruiting strategies. You will learn:

  • What behavioral data is and how you can use this information
  • The advantages of working with a vertically integrated data company
  • How data and data science can help firms operate more efficiently and effectively
  • Why the partnership between Discovery Data and AdvisorTarget is beneficial to asset managers and wealth managers
  • And more!

Tune in now to learn how data can identify demand among financial advisors.

Resources: AdvisorTarget | Discovery Data

The Data Possible Podcast is produced by our partner, Advisorpedia.

Podcast Transcription:

Doug: Hello, and welcome to The Data Possible Podcast presented by Discovery Data. The Data Possible Podcast examines how data fuels your sales, marketing, and recruiting teams to achieve success. Our goal is to provide you with tools, techniques, and best practices to help you close more deals, find new opportunities, and recruit better people and partners. This is your host, Doug Heikkinen. And with me today is Duncan MacDonald and Jeff Tripp, who are the founders of AdvisorTarget. Hello, gentlemen.

Jeff: Hello.

Duncan: Hey Doug.

Doug: How are things going for you this year?

Duncan: Going pretty well. I can’t complain. Despite the craziness that’s going on in the world, business has been pretty good for us. So we feel fortunate.

Doug: So you guys created AdvisorTarget. What drove you to create AdvisorTarget and what’s the need that’s being served?

Duncan: Sure. So six or seven years ago, we actually started out as a publisher that was focused on editorially serving advisers. And back then we were all advertising driven. And as we drew in more advertising clients who were primarily asset managers and broker dealers, they started to ask us a problem. And we simultaneously basically identified two problems in the market. And on the one hand, those clients wanted to reach advisors with advertising, but they didn’t want to pay for people who weren’t interested. So they wanted to find the advisors that were really interested in their product. And the other hand we were hearing from our advisor readers, that they were getting annoyed with wholesalers calling them 35 times a week trying to sell them the same product. And when we heard all these things, the light went off, which was that we could solve this issue if we could take sort of the underlying identity lock, so to speak, that Discovery Data has on who those advisors are biographically and then overlay all our behavioral data that we were seeing in our publication at that point to figure out who’s who and what are they interested in. And we knew we could solve both problems. And so that’s really what we’re serving, and then fast forward a few years and because of the adoptions of clients and where we are, we’re now positioned to revolutionize the way marketing and distribution are done across the industry.

Doug: So what is advisor behavioral data for asset managers?

Jeff: Yeah, it’s a great question, Doug, especially given the huge range of things that people are calling behavioral data out in the marketplace. And it’s one of the things that really differentiates us. In this case, it is first party data. That’s simply profiling what advisors are reading on select B2B sites. So as advisors are out there doing their due diligence on upcoming asset purchases and staying educated so they can support their clients, they are reading. Those reading habits are simply being profiled and then advanced data science practices are surfacing what the most timely, most actionable interests are.

Doug: What are the applications for your data and how can it help asset managers and brokers with sales, marketing, and recruiting?

Duncan: Absolutely. So we see ourselves really filling a void in the market and something that’s been sorely needed. So if you think of the industry and how most marketers or distributors do business, whatever particular company they’re at, there’s sort of three things that go into it. One is you have to identify the advisor. So call that the biographical pillar of the three pillars. Then you have sort of transaction data, asset data, whatever you want to call it. And then you have behavioral data. And those first two, they’re very useful and they’re must-have’s in most cases, as we know from Discovery Data and how it dominates the space. But the problem with them is all the information. So transactions and biographical, it’s fundamentally backward looking. It’s trying to take what someone’s done in the past and project, what they may do in the future. But what’s really been missing is a timely, actionable piece of, that’s predictive showing what an advisor may do next. And that’s where we come in with our third pillar of data. So that’s the whole idea, to help give a product provider or broker-dealer an advantage in being able to identify an adviser interested in their product and then give them a timely, actual point to reach out to that advisor to help close the sale.

Doug: Are there differences between your data? And other behavioral data?

Jeff: Yep. Absolutely. And it’s another important point to make in that the technological perspective on that is that we are not reliant on cookies where a huge part of the ecosystem, the MarTech ecosystem, and many of the behavioral data products in the marketplace are essentially reliant on cookies and, they’re taking cookies and trying to figure out who people are: probabilistic matching. So our solution actually starts with the identity and it’s not reliant on cookies. So you can think of it as a loop. Starting with the advisor’s CRD level identity, we’re simply profiling what they’re reading on very specific B2B sites. So as the entire industry, even Google and Apple’s already well into this, is phasing out third party cookies and many of the connections that a lot of the MarTech platforms have been essentially reliant on for years, we are out ahead of that in that we are not reliant on cookies. So then the actual data that’s being captured is differentiated as well in that it’s not hundreds of sources that we’re trying to make some use of. It’s simply profiling advisors’ reading habits on very select B2B sites. So it’s very targeted. It’s very straight forward. And again, from that, we’re able to make it actionable for clients.

Doug: So Jeff, I have a follow-up: does a publisher have to give you guys permission to get the data?

Jeff: You mean the source of the data, the collection point?

Doug: Yes.

Jeff: Yeah, absolutely. It’s very specific sites. We actually have an editorial property ourselves that is really the main anchor here at this point. Other publishers that are highly endemic to the advisor space will be brought on over time as well as it makes sense, but it’s absolutely specific sites that are transparent and very well-lit and clients can go and read the content and kick the tires and really understand that perspective as well, which is important.

Doug: Yeah. Duncan what’s what does it mean to be a vertically integrated data company?

Duncan: So that’s picking up basically on what Jeff was just saying, which is, we have a proprietary technology that allows us to do all the tracking that underpins this behavioral data identification that we’ve been talking about. And so what we do is basically we’ve partnered with various media properties, including ones that we own and run to apply this technology in different places. And what that means is, we’re basically owning, and can identify the advisors at the inception point across all these different properties. So it’s a very important differentiation for us. The general model when you hear behavioral data is, a company that specializes in buying thousands of different datasets, scraping information across the internet, and trying to repurpose it, sort of like an alchemist trying to create gold out of thin air. And that’s completely different than what we do. And how we’re able to maintain quality is that we have a very consistent protocol for how we can identify and capture data across different properties using the same technology and all feeding into the same database. So we refer to that as vertical integration. That’s what ensures the great results you get with AdvisorTarget.

Doug: So how can your data and data science help firms operate more efficiently and effectively?

Duncan: Yes, absolutely. There’s many ways that it can, I would say. The first thing to always focus on is, and how we can help is, AdvisorTarget. When you get down to it, it’s about boosting ROI. Whether you’re a marketing or distribution team, the idea here is, you’re inevitably going to have large lists of prospective clients. And if you apply our behavioral data lens, that allows you to quickly cut through from a list of 500 people down to the list of 20, that really are the ones who are likely to be buyers. So that’s the first thing to say, but getting more into the data science side, a lot of companies now have built out their own data warehouses and have more sophisticated protocols that when that occurs, what we can do is, our data can act as an additional layer that goes into their own targeting to help identify who’s who, and just add on another parameter. And finally, one thing that we could do is help close the loop, so to speak, which is, an asset manager, for instance, can see which transactions happen in certain funds of theirs. But what they don’t really know is what preempted that transaction. And so what we can do is, if we give them behavioral data, they can then see if that did end up in a transaction, which that information can help smarten their data protocols and their own algorithms.

Doug: Guys, have you always been such data geeks?

Duncan: I certainly have. So I did a PhD concurrently, but also before doing this, so I’ve always been super wrapped up in this kind of stuff. So I don’t think it’s a coincidence. I ended up having a data company.

Doug: And what’s next for AdvisorTarget?

Duncan: Well, you know, right now the big focus for us is on this partnership. This is a big opportunity for us. We’ve been operating on our own for a couple of years. And now we feel like this opportunity with Discovery Data is a big thing because Discovery Data brings so much in terms of identity lock and being able to identify the advisor. And then we bring the timely, actionable aspects. So we think this is a really dynamite proposition for clients. And so we’re really focused on that.

Doug: Duncan, Jeff, this has been absolutely fascinating. Thank you so much for talking to us today.