Using 5500 Data for Offensive and Defense Business Development (Part 1)
This article is the first in a five-part series devoted to helping you more effectively use the 5500 for prospecting. By the end of this series, you will learn:
- Steps to locate viable new business
- Defense strategies for your book of business
- How to approach your first meeting with a potential client
Identify your Clients
There are over 1 million ERISA qualified plans active nationwide each year. Nearly 800,000 plans in the retirement space alone. Finding the plans that fit well into your business model can seem like a daunting task. The first step in business development is often research. Here are 3 questions to ask yourself:
Who Are My Current Clients?
Knowing who you currently work with is a great indicator of where you should be looking for new business. Consequently, as no one knows your client’s as well as you, it is always a good idea to dig deeper and see if there are unifying traits that you may not be aware of. The 5500 form provides a ton of information that, when analyzed, can reveal trends and traits that may not have been uncovered by your face-to-face interactions.
What Are My Strengths?
Properly identifying your skill set is imperative when planning your prospecting. Perhaps you are a good educator and skilled at letting employees know the value of their plan. Maybe you are a data wonk and are skilled at identifying trends in large pools of data. You may have spent years in the 401(k) market and are known in your region for strong 401(k) performance.
Who Looks Like My Current Customers?
A good place to start are those categories of sponsors that are similar to what you work with and blend well with your strengths. Therefore, if you can find sponsors who resemble your client base, sponsors that also may have an unhealthy plan, you have yourself a beginning of a great lead list.
THE JDA ADVANTAGE:
The Retirement Plan Prospector database was designed not only as a lead generation service but as a tool to help you identify trends in large datasets.
Find the second post in this series Here!
