Buying prospect lists is a time-honored way to replenish the top of the funnel for wealth management firms and financial advisors. It’s a competitive business, and financial professionals are constantly seeking ways to enhance organic growth.

For years, this cost-effective approach was one of the easiest ways to get new clients in the door. Every win paid long-term dividends, especially during boom times when rising markets meant an impressive increase in assets under management for every client in the book.

Traditionally, a list was a starting point for advisors to do their own research, fill in the gaps, and prioritize who to call before painstakingly plowing through potential prospects one by one looking for that needle in the haystack. Today, however, overreliance on lists ignores advances in technology, especially artificial intelligence (AI), which can glean far greater insight and value from data of all kinds.

Understanding Goals Is Job One—And A Job For Humans
Wealth management is and always will be a blend of the right people and the right technology. The most successful firms have figured out what each half of the equation does best and  play to those strengths.

AI is useless without a goal. Goals can only be properly defined and understood by the people within a firm, who know what they want to achieve. Is it maintenance? Steady organic growth? Positioning the firm for a sale? Reaching a size where the firm can make acquisitions?

All firms need to bring in at least enough new business to replace outflows from lost clients and distributions. Purchased Prospect Lists were that starting point, but ironically take so much effort to deliver results that the human half of the equation – which excels at relationship-building, goal setting and planning—is often wasted in menial tasks.

A Eulogy For Prospect Lists: Top 10 Problems With Buying Lists For Growth
It seems only appropriate to document the problems with list-buying and the advantages of AI-powered prospecting with none other than—a top 10 list. So here it is: my Top 10 Problems With Purchasing Lists For Organic Growth.

1. Static Data: List data is static and quickly becomes outdated, often leading to inaccuracies. AI-powered solutions provide automated data updates and recurring insights, ensuring that client information is always current.

2. Lack of Personalization: Purchased lists do not include the level of detailed data  needed to properly empower advisors to tailor their messaging to individual prospects. AI will analyze vast amounts of data to provide highly personalized recommendations based on each prospect’s  unique financial situation.

3. Marginal Segmentation: Lists might have basic  segmentation based on easily compared data points, but AI delivers  highly accurate segmentation  based on behaviors, preferences, and financial goals, leading to more targeted and effective marketing strategies.

4. Limited Insights: Traditional lists lack the advanced analytics and insights that AI solutions provide, which are crucial for understanding prospect behavior. AI especially excels at identifying correlations, trends, and predictive indicators that are easily missed by humans sifting through data.

5. Limited Predictions: Lists offer a snapshot of prospects today; it’s up to advisors to decide if they have the potential to become a future client. AI modeling  draws on current and historical data to predict and prioritize which prospects look and act  like today’s best clients .

6. Inefficiency: Manually updating and verifying information from static lists is inefficient and time consuming, especially when not embedded in the advisor’s current tech stack. AI  automates routine tasks such as data entry and analysis, freeing up time for advisors to focus on building new client relationships.

7. Time-Consuming Process: Purchased lists require advisors to invest significant time into the prospecting process, diverting focus away from engaging with ideal prospects  with the highest potential for successful conversion. AI brings precision, automation, and speed to the prospecting process, drastically improving the efficiency and actionability of tasks that lead to growth outcomes.

8. Inflexibility: Traditional lists are often inflexible and do not easily adapt to changing market conditions or client circumstances. AI prospecting tools draw upon and factor in current data.

9. Scalability: Larger prospect lists mean more work for advisors to digest and make sense of the data, until eventually, they can’t handle any more. AI solutions are purpose built to  scale, handling  large volumes of data  to deliver intelligence across  firms of all sizes.

10. No Ongoing Improvement: The process for managing large prospect lists is difficult to change and plagued with risk – no one wants to accidentally make things worse. Machine learning algorithms continuously learn  from advisor actions, outcomes, and conversions, leading to more accurate predictions and highly customized models that adapt to a firm’s business.

A Synergistic Approach: Combining AI With Traditional Lists
It’s important to note that this doesn’t have to be an either-or decision. Wealth management firms can benefit from a synergistic approach that combines the strengths of both AI and traditional lead lists:

• Enhancing Purchased Lists: AI can be used to enrich and further enhance the data from traditional lists, providing additional insights and improving the accuracy of prospect information. This can help a firm pull through the full value of this investment.

• Integrating AI Capabilities: By partnering with data science experts who specialize in building AI models for organic growth, firms can integrate advanced AI capabilities into their existing systems to create a robust pipeline of organic growth opportunities.

Using yesterday’s tools to address tomorrow’s challenges is a sure-fire way to fall behind. While AI is no magic bullet and change won’t happen overnight, now is the time to start turning a list-first growth strategy into something new and better.

Jeff Koeneman is chief experience officer with TIFIN AG.