If you are a financial advisor or a wealth manager, then you know that the financial services landscape is highly dynamic. Financial services trends and regulations change frequently. Marketing in financial services generally has a long conversion process due to the sensitive nature of the information that clients have to share with their wealth managers. Financial advisor prospecting ideas and activities, therefore, have to constantly be refreshed.
Think about how you typically market a financial services company. Prospecting ideas for financial advisors generally means cold calling or relying on referrals. These approaches are not without their challenges or issues. Referrals, of course, can be an effective way to find new clients. However, even your referral strategy can become more precise when driven by data.
In fact, it can be even more beneficial to begin with data. You may already have plenty of it. But, it may be fragmented and uninsightful in its present state.
Your data needs to be housed in a centralized database, collected and organized into wealth profiles. Even if you are an individual financial advisor or wealth manager, organizing your data can be the first step in gaining actionable insights.
Take wealth indicators for example. They can be a great starting point in understanding where the greatest potential lies in your database. Furthermore, you can combine wealth indicators with demographic, lifestyle, interest, and affinity data. Doing this gives you a holistic picture of who your contacts are.
For instance, you may find two contacts who are both 55 and have a net worth of $8M. When you perform a wealth screening, you might see that prospect A is interested in golf, whereas prospect B is interested in opera. Imagine you now approach them with a richer image of who they are. You will be more likely to have an engaging conversation with them.
Other data-driven, prospecting activities for financial services involve deeper analysis. Analysis and modeling can further elevate your prospecting ideas. Let’s explore how.
You can leverage your existing contacts through modeling. A look-alike model can analyze your best customers and look for patterns among them. These patterns can then drive your prospecting activities.
For example, the model may tell you that all your top clients have a net worth between $1-3M. That a large majority of them do not have children and that they prefer adventure travel. Geared with this persona of an ideal customer, you can tailor your prospecting ideas to really speak to someone like this.
Although this technically is not a model, WealthEngine’s inner circle feature can make a big difference to your referral marketing. Basing your financial services prospecting on data from this feature can give you a sharper focus. Your current customers can again be a great source for you to meet great new customers. A warm introduction from your client to his colleague or HNW friend can automatically generate trust.
You can use wealth models to predict specific outcomes. Predictive models build a specific formula for your client base. The model then uses this formula to analyze your database. Everyone on it will be scored against the formula and divided into segments. These segments will, therefore, help you prioritize your prospecting activities. A formulaic analysis, like this, can help you with the following:
This model can help you identify a completely new customer group. The group will be a high-potential segment as it will have properties that are similar to your existing customers.
In this case, you can identify customers who were successfully converted to higher spending. We will build a model that can understand these customers. The model can then pick out other customers who can be upgraded.
The upsell model builds a formula to predict upgrades or complimentary services. Through this, you can identify customers who can be approached with other offers.
For any financial advisor or wealth manager, customer churn can be a major concern. Using the retention model helps you predict whether a customer will churn or not.
This model can predict the lifetime value of your customers. This way, you can focus your attention on those relationships that have long-term potential. How does this work?
The model uses your customers’ transaction history. Doing this enables it to predict churn. Further, you can also forecast the number of future transactions, the value of future transactions and even the next transaction amount. This type of analysis is possible using the latest machine learning techniques.
Therefore, data-driven, financial advisor prospecting ideas lead to more efficient prospecting activities.
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