One of the biggest challenges facing asset managers is how to prospect for new advisors without committing expensive resources to poor targets. The ever-increasing volume of internal, external and social media data, and the expansion in available communication methods can further compound the process, but they also represent an unprecedented opportunity to greatly improve productivity.
Asset management distribution consists of three primary focus areas:
- Retention of current assets and advisors
- Additional sales to existing advisors
- Acquisition of new advisors
Courtesy of Clyde Robinson
How to Use Big Data to Target Asset Management Advisors
Big Data and predictive analytics is a powerful combination for identifying new advisors. Data on existing advisors is analyzed to identify which factors are associated with advisors using a given fund. In effect, we ‘learn’ from advisors already doing business with a specific fund and use this information to predict which prospects would be most likely to begin using that same fund in the future. The key here is that the outcome is known for the data we are learning from, so the factors areadjusted to give the most accurate prediction. This approach also provides an accurate estimate of the targeting accuracy, which is extremely useful in campaign sizing.
To summarize, the advantages of using predictive analytics are that it is:
- Automatic: Asset managers do not have to guess which factors are useful and or not
- Optimal: The factors will be weighted to provide the best targeting performance
- Testable: The results can be ‘back-tested’, meaning that the ‘lift’ can be checked against historical data so that the targeting performance is known beforehand (i.e. if it doesn’t work for some reason asset managers will know before making a single call)
For more information on prospecting tips to attract new advisors, check out our whitepaper.