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How To: Explainable AI For Ideal Customer Acquisition In Wealth Management
As wealth management firms continue to face stiff competition in the market, finding and converting ideal customers has become a crucial aspect of organic growth. Historically, these organizations have relied on referrals, but cold outreach and digital marketing are increasingly becoming popular strategies for expansion. Unfortunately, these channels haven’t been delivering expected outcomes due to an insufficient number of signals used to determine a fit.
In contrast, AI-based lead scoring models can accurately predict which prospects are most likely to convert into clients and help wealth management organizations prioritize the best opportunities. However, even with AI, most models remain opaque, generating lists of leads without providing insights on why certain prospects were ranked higher than others. This opacity has led me to hear numerous times that “black box” AI leads aren’t truly usable in wealth management firms, resulting in a loss of trust in AI.
The primary challenge posed by black box AI lead scoring models is the lack of transparency surrounding their predictions, leading to:
1. Lack Of Trust: Firms cannot comprehend why AI selected specific prospects, causing distrust in AI-driven decision-making.
2. Inefficient Resource Allocation: Wealth management organizations waste resources on prospects that do not align with their ideal client profile.
To overcome these issues, I recommend leveraging explainable AI (XAI) to revolutionize the customer acquisition process in wealth management.
Step 1: Determine Your Ideal Client Profile
It is essential to start by identifying your organization’s most valuable clients. This can be achieved through attrition prediction models that enable a comprehensive understanding of client data and foster a unified organization around better knowledge sharing. By pinpointing the attributes of high-value clients, you can train a new AI model to find prospects with similar characteristics and cease investing in low-potential leads.
Step 2: Collect Relevant Data
To identify ideal leads matching your newly defined ideal client profile, you must combine both internal and external data sources. Internal data comes from your CRM and wealth management platforms, providing information such as the lead source, investment experience and interests, net worth, annual income, and engagement with marketing outreach and content. Additionally, consider leveraging market intelligence providers to access external data points like business ownership, real estate holdings, spending habits, trading activity, and social media information.
The insights generated by your ideal client profile explanations can significantly reduce the cost of acquiring new clients by focusing on the correct target audience.
Step 3: Train Your Explainable Lead Scoring AI Model
By combining these data sources with the results of your acquisition efforts, you can develop a lead scoring AI model that predicts which leads are most likely to convert. Explainable AI models utilizing decision trees or linear regression can provide valuable insights on why a lead would convert, such as:
1. Internal Signals: rapid asset accumulation (exited company with $12 million payout), deep investment expertise ($3 million in brokerage accounts) and search for advisory services (attended two exclusive webinars, multiple blog post interactions).
2. External Signals: Commented on a post about family office structures.
The benefits of explainable AI include:
1. Increased Trust: AI-driven decisions become transparent, fostering trust among stakeholders.
2. Informed Outreach Actions: By gaining insights into the factors contributing to lead conversion probability, wealth management firms can create tailored outreach actions, such as:
* Outreach by a senior advisor in wealth structuring for entrepreneurs
* Customized proposal for asset diversification
* Private client dinner with other entrepreneurs
Explainable AI, utilizing linear regression as a surrogate model, could display the impact of each factor (e.g., net worth, referral status) on the lead conversion probability. This information enables you to eliminate low-probability leads and focus efforts on high-probability prospects in your ideal client profile.
By leveraging explainable AI and internal/external data sources, wealth management firms can:
1. Focus on their ideal client profile
2. Understand why each prospect is valuable
3. Create highly personalized campaigns
In conclusion, incorporating explainable AI into the customer acquisition process is crucial for wealth management organizations seeking to increase efficiency, build trust with stakeholders, and drive sustainable growth.
By implementing this approach, you can revolutionize your organization’s lead scoring strategy, ensuring that resources are allocated effectively, and ultimately driving business results.
