Predictive analytics gives asset managers an edge in distribution

Predictive analytics gives asset managers an edge in distribution

Predictive analytics gives asset managers an edge in distribution

Predictive Analytics – It’s no secret that the asset management industry is facing unprecedented pressure. Firms’ AUM continues to climb, but revenue and margins are slumping as fees plummet. The outlook for those margins is gloomy as shareholders, regulators and clients continue to pressure funds to deliver higher value at lower cost.

It’s no exaggeration to say rock-bottom fees are an existential threat to many asset managers. However, technology could still save the day. Managers who leverages predictive analytics to optimize their distribution network, allowing them to connect quickly and efficiently with the armies of advisors who can sell their investment products, will gain a valuable edge.

Until recently, sales prospecting was mostly an analog game. Managers organized their wholesalers by region and channel, whether that was wirehouses, IBDs or banks. Save the odd trusty website and sprinkling of third-party data, wholesalers determined the best sales prospects the old-fashioned way: They used open source market intelligence, leveraged their existing networks and relied on their wits and extensive industry experience to seek out qualified prospects.

Managers have been aggressively competing for advisors’ attention. The 50 largest mutual fund firms own 85% of all industry assets, which means small and medium-sized managers fight for the remaining market. They bombard advisors with communications: On average, an advisor receives over 10 messages per week from asset managers. A whopping 86% of asset managers say breaking through the noise and getting in through an advisor’s door is their biggest challenge. If they manage, they will discover advisors’ needs can include regular, pertinent updates on new and existing products, market commentary, education on investment concepts, or all three.

Most firms focus on the same cohort of advisors based on total AUM or purchases

based on a limited subset of available market data. This compounds the issue of competition, as most managers have identical top prospects, leaving the long tail of the market underserved. Some may even alienate advisors with irrelevant spam.

One thing is clear: The old approach — where asset management firms rely on a mix of patchy intelligence, street smarts and scattershot communication campaigns to connect with advisors – will no longer cut it. They risk a chunk of the distribution market if they don’t arm themselves with predictive analytics software that churns out insights about advisors through data mining, machine learning and predictive modeling.

Predictive analytics feasts on mountains of data, which, fortunately, is not in short supply. Distributors are huge data generators just like everyone else. They are constantly producing data about investors, products, assets, transactions and their appetite for digital engagement. Everything, from the net worth of their typical client, to the last time they opened an email from a wholesaler, will be recorded and stored on a company database in the cloud.

Just like a pile of 1 million jigsaw pieces, any glut of raw, unorganized data is not particularly valuable. There’s insight contained within the pieces, but they only reveal themselves when analyzed for similarities, and then arranged into meaningful patterns. Managers can use powerful computing to collect, analyze and organize the puzzle pieces if they know what picture they’re making. In other words, data almost always has an answer, as long as they know their question.

For example, an asset manager might wish to know which distributors in a specific region of the U.S. have the greatest appetite for digital engagement. The firm may well discover it’s sitting on a gold mine: millions of data points about how thousands of advisors have interacted with the hundreds of marketing emails sent out in recent years. It could train a machine learning model on that trove of historical data to identify who those advisors are, and what they have in common.

This article originally appeared on forbes.com To read the full article and see the images, click here.

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