Big Data for Asset Management Distribution

By Ian J. Scott April 13, 2012

Big Data is changing the world of asset management.

Not too long ago, anyone wanting to develop a deeper understanding of their customer’s behavior was largely constrained by the availability of data and the ability of their organization to put analytics into action. The advent of the likes of LinkedIn, Google, and Facebook has changed the equation. Now the value of data and analytics is no longer in question, and Asset Management firms hoping to compete in this brave new world would be unwise to ignore this trend.

Which begets one of the most common questions I get asked by asset managers looking to compete in this brave new world: “What data do I need?” Generally speaking, the bar is not set as high as most people think. Organizations often have most, if not all, of the data they need to get stated. Furthermore, the common perception is that any data they have is dirty and cleaning it up is a necessary precursor to using to drive sales behavior. While this is true in principle, the reality is that a lot of value can be derived with only minimal cleanup.

Transactional Data

The most important data is transactional history for the financial advisors. This includes items such as Purchases, Redemptions and Assets.  It is foundational for all types of analysis, from profiling and targeting current advisors, to prospecting for new advisors, as well as for measuring the effectiveness of sales and marketing efforts. As a rule, 24 months of history is a good place to start, but shorter timespans are also workable. Very often this data will have issues related to omnibus accounts, teams etc. but these are far less important than is commonly believed, and the vast majority of asset managers will have sufficient data quality to support analysis.

CRM Data

CRM Data captures important interactions with advisors. Typically these are calls and meeting between internal and external wholesalers and financial advisors, but can be extended to include other items (see Marketing Data).

Fund Performance and Ratings

Fund performance and ratings are obvious drivers of advisor behavior. It can also be used to profile advisors to understand which advisors are most sensitive to fund over or under-performance to proactive action can be taken under these circumstances.

Third Party Data

Third Party Data is primarily used in prospecting and profiling, and is sourced via regulatory filings (e.g. licenses held by the advisor) or obtained directly by the third party often through direct calling:

• Advisor Demographics: Age, location, licenses help, years in business, estimated book size, products used, etc.
  • Providers: Discovery, Meridian IQ, RIA Database
• Wallet share: Information about holdings and or transactions across all asset managers. These can be supplied at the individual advisor level but are often rolled up to the branch or firm/zip code.
  • Providers: Market Metrics, DST, Coates, IXI

Marketing Data

As distribution adopts some of the methods more traditionally used in e-marketing applications, newer data sources are becoming available. Examples include:

• Email opens
• Website attendance and registration
• Promotion history and responder data for e-marketing campaigns

Social Media

Social networking sites are an emerging trend in asset management, and are being increasingly used by financial advisors to market themselves, ostensibly to younger demographics. Although compliance challenges will hinder growth, it seems inevitable that this channel will increase in importance. For the relatively small number of advisors that currently are actively using social networking, however, it can be invaluable given that it is volunteered and can provide insight into relationships to their clients as well as to asset managers. The challenge is to gather and structure this data in a way that it can be used, but that is a subject of a future blog…

Are you an asset management firm leveraging big data in your efforts? Share your insights below.

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Related:

• Using Compensation to Retain Assets (Part 3)
• Whitepaper – Analytics in Action: Winning in Asset Management Distribution with Predictive Sales Intelligence
• Le Tour de Finance

Ian is responsible for bringing Lattice Engines’ market-proven software solutions to new verticals, drawing on his extensive experience in financial services and analytics to develop new application areas based on customer needs. Prior to joining Lattice Engines, Ian served as CTO for Angoss Software. During his career, Dr. Scott has conducted quantitative risk assessment for UBS and also worked for CFM, a Paris-based hedge fund. During his academic career he was a faculty member at the University of Wisconsin, Madison. He also held research positions at the CERN particle accelerator (Switzerland), and at the Stanford Linear Accelerator Facility (California). Dr. Scott holds a Ph.D. in Physics from Harvard and a B.Sc. from McGill.

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