There is a giant gap in how RIAs use data to make informed decisions. Some parts of the business are highly sophisticated while other aspects of the business remain in the proverbial stone age. The fastest growing firms have closed this gap while the laggards have yet to solve for it.
At most RIAs, business activities can be sorted into two buckets:
The “sophistication gap” exists between how these core business functions collect and analyze data and how the business functions of organic growth utilize data. The chart below shows this gap visually.
Why the sophistication gap?
That’s a question we’ve thought about a lot over the last several years, and there are two key drivers:
We don’t advocate for changing your team’s priorities. In the RIA business, the priority must be managing wealth. And that is why we have spent much of the last several years working on the first driver of the sophistication gap: the infrastructure required to capture critical organic growth data, analyze it, and drive more informed decisions.
The opportunity, and our vision for the industry, is to up-level the sophistication of organic growth so it is in-line with the sophistication of the core functions of wealth management. The rest of this article shares the specifics of how firms can bring their organic growth infrastructure in-line with the rest of their business.
Build a great organic growth dashboard
If we think about the most sophisticated platforms used by RIAs, there is a common denominator: these platforms allow RIAs to aggregate, filter, and visualize huge data sets. This is what your organic growth dashboard should do too.
The dashboard below is just one example, and shows the performance of a $1.5 billion RIA purchasing and processing leads from SmartAsset. As you can see, the number of deals created, closed deals, and AUM gained from the campaign can be tracked over time and quickly referenced. If you want a closer look at how to build a campaign targeting $25 million in net new assets from SmartAsset, you can download our case study. (see disclaimer below)
The data presented in this case study is entirely simulated and has been developed using synthetic models that reflect average industry metrics. No actual client data was used in the creation of this model. See full disclaimer below.
Just as important, this dashboard allows the firm to drill down and view key performance metrics by advisor and by advisory team. In this way, a good reporting dashboard helps to drive accountability and engagement across the organization. The example below shows how this data can be filtered and reported on by team member.
Deploy AI thoughtfully, and strategically
There is a saying that “AI won’t replace lawyers… but lawyers who use AI will replace lawyers who don’t”. This same logic applies to wealth advisors. We don’t believe that AI or roboadvisors can replace the solutions provided by sophisticated RIAs and family offices, but we are already seeing firms who learn and utilize AI outperform the firms who don’t.
And before we go further, let’s make it clear: having any AI interact directly with sensitive client data is a non-starter. This is because it isn’t clear how AIs are treating this data and what the negative repercussions might be down the road. So first and foremost, it’s critical to build a conservative process that creates a firewall between AI and sensitive client data.
With all of that said, we are seeing benefits from AI in three key ways: data synthesis, content creation, and content to support content.
Data synthesis and enrichment
In this article you’ve already seen AI parsing massive data sets to provide executive summaries. If you scroll up, the first dashboard in this article includes an AI generated summary in the upper left corner… the text beginning with “The Deals Closed Won…”. AI is working to help make sense of this data, and AI is quite good at identifying variables within the sales pipeline that are bottlenecking the entire system. It is also quite good at providing ideas for how to eliminate those bottlenecks within your sales process.
The second use case for AI is data enrichment: when a new lead is entered into your pipeline, you will likely only know their name and email address. Using AI-powered tools we can now “enrich” this data. Data enrichment simply means using baseline data (like a name or an email address) and then applying an automation to append that data using a number of available data enrichment solutions to augment what we know about that person.
From a simple name we can many times find a home zip code, and from that zip code we can estimate their wealth level. And if we have a name and a zip code, we can also find that person on LinkedIn, which allows us to add their job title, seniority level, and estimated annual compensation. As you can see, all of this information can really help your advisors identify the highest-value leads within your sales pipeline.
Content creation
Using AI for content creation is all about expectations. If you put in a prompt such as “write me a 900-word article on estate planning for high-net-worth families managing a wealth transfer in 10 years” and expect any AI to deliver a client-ready article, you will be really disappointed and may give up on AI altogether. If you put in that same prompt and expect the AI to deliver a good draft that can be edited, fact-checked, and updated to reflect your unique world view, then you are going to be pretty impressed AND save a lot of time creating the article.
Going back to our lawyer analogy: content creation is one area where the firms that embrace AI are starting to measurably outperform firms that don’t.
Content to support content
When you create a piece of content like the blog on estate planning referenced above, there is a huge amount of content that is needed to support that content. You need an email that goes to clients announcing the blog post, a social media post sharing the insights, a description of the article for your newsletter, a description of the article for a podcast on it, etc.
AI does a great job creating this kind of content when you provide the finished blog post as a starting point, and it’s one of the reasons why HubSpot has had so much success with their Content Remix feature. Again, this is one area where firms that deploy AI are seeing it measurably up-level their content strategy, which is a big part of closing the sophistication gap referenced earlier.
People bring it all together
Technology is great and we’ve put a lot of energy into building these platforms, but the RIA business is still a relationship business. And that’s why bringing this data to your team is so critical. We’ve seen firms successfully implement monthly meetings that are one hour long to review dashboards and their content strategy.
These structured 60-minute sessions dedicate 20 minutes to reviewing key performance data, ensuring that everyone is on the same page regarding progress and areas for improvement. Another 20 minutes are spent sharing wins, which not only boosts morale but also highlights successful strategies that can be replicated across the team. The final 20 minutes focus on discussing opportunities for the upcoming month, allowing the team to strategize collectively and set clear objectives.
Closing thoughts
If you’d like help matching the sophistication of your organic growth strategy with the rest of your business, we can help. Connect with our team to schedule a confidential meeting with our Founder, Craig Hall. On that first call we want to learn about your growth objectives and share how we help RIAs reach theirs.
*Disclaimer
The case study and data referenced in this article 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.