Case Study for RIA Leaders
How to generate $25 million in new assets from SmartAssets leads
Over the last three years we've helped sophisticated RIAs process more than 10,000 SmartAsset leads. We put everything we learned into a case study that shows the process and investment needed to generate your first $25 million in net new assets from SmartAsset leads.*
![[MW] Graphic-2025 Marketing-02](https://pages.marketingwiz.co/hs-fs/hubfs/%5BMW%5D%20Graphic-2025%20Marketing-02.png?width=2000&height=1158&name=%5BMW%5D%20Graphic-2025%20Marketing-02.png "[MW] Graphic-2025 Marketing-02")
What does it take to generate $25 million in net new assets from SmartAssets? What is the ROI and NPV of this investment?
Page one of the case study shows the investment required and page two shows the return on that investment. We wrote this case study for busy RIA leaders who want to cut through the noise and understand what investment and process is needed to close SmartAsset leads at scale.
Download the report to learn
- The average close rate across more than 10,000 SmartAsset leads
- Best practices to see the most success
- Strategic landmines that waste time and energy of advisors
- The ROI and NPV that sophisticated RIAs can expect when launching a campaign
Download the case study
*Disclaimer
This case study is based on a synthetic data model developed to demonstrate potential outcomes of the SmartAsset program. The information provided is derived from average close ratios, deal sizes, and time-to-close metrics collected over several years of industry experience. To ensure complete anonymity and data privacy, no client data has been utilized or included in this dataset. The model was constructed using Mockaroo, employing specific formulas to simulate data which was then integrated into our HubSpot Demo Environment and visualized through a demo Databoard on Databox within our agency account. This databoard serves as a visual representation of the data model and is designed to reflect our comprehensive understanding and application of the industry metrics.
It is important to understand that while the data reflects our experience and provides a generalized representation of what could be achieved by following our processes, it remains illustrative and should be interpreted as a demonstration of potential rather than exact predictions. The methodologies and assumptions used are based on a blend of historical data and industry standards, and while they are designed to provide a realistic outlook, they do not guarantee specific results. This disclaimer aims to clarify the nature of the data and the purpose of its use within this case study.