Are you overlooking technology when it comes to 401(k) client prospecting? Connecting with a company rep via LinkedIn and embracing web-search tools to identify the plan administrator are savvy first steps — but novel AI-based analytics that use data from the Employee Benefits Security Administration can energize prospecting and elevate it to the next level.
First, let’s consider how performance data leads to performance improvement. A usual pathway is that of choice and competition (i.e., public transparency causes underperformers to lose market share, which ratchets up competitive pressures). In healthcare, strong arguments can be made that it’s more effective to impact reputations. We suggest AI-based analytics to easily discover underperformers in 401(k)s whose shortfalls are addressable by an adviser‘s expertise, and then borrowing from the Challenger Sale model — this approach encourages sales reps to emulate high-performing peers, also known as “challengers,” and take control of the sales process.
How can underperformance be uncovered? The answer is to completely rethink how benchmarking is done and create software that generates and stores hundreds of thousands of noteworthy, factual benchmarking insights, then query those stored insights. Search engines don’t crawl and index the web upon spotting a user’s query. Instead, the web is crawled and indexed beforehand, which then enables powerful queries and rapid response.
Read more: Small businesses can save $100,000 per employee by offering 401(k) benefits
Stored benchmarking insights can be broadened by rethinking what new types of comparison are insightful or motivating. For example, a performer might have the worst X of everyone with such a good Y where X and Y are compatible numeric measures (e.g., a 401(k) plan has the least net income of all plans with so much in total assets). Thus, software stores insights and lets users formulate search queries. But to drive improvement, insights should be informative, convincing, motivating and shareable in well-written understandable language, infused with data, salient comparison and relevant context without information overload.
Technology can catalyze prospecting via performance transparency when the following elements are in place: measures → data → analytics → expression → dissemination → transparency → action → performance improvement. The new technology referred to above involves the middle steps of analytics, expression and dissemination.
Where is the AI in all this? The technology arises from classical AI task automation, which is different from machine learning. Classical AI poses the following questions:
- What is the space of possible task solutions optionally broadened by innovation?
- What makes one solution better than another?
- What knowledge and/or data shapes those judgments?
- How can such knowledge and data be obtained and codified?
- What algorithms will search the space of solutions and report good ones in practical time?
For retirement plan advisers, embracing AI continues to be a valuable tool in assessing the market. They can use free comparison analytics engines to explore 401(k) options for their clients. When assessing the advantages and shortcomings of plans through comparative analytics engines, a user can get desired results through queries that few people would be able to examine on their own by parsing through federal 401(k) data. Each and every result is presented in a way that can be understood by all audiences.
Read more: The secret to getting employees to save more for retirement? Auto enroll them into your plans
Suppose an adviser can help plans improve on a measure X, but targets plans of a certain type, size, geography, industry or other characteristics. The adviser queries the stored insights for those within the target that manifest shortcomings in measure X, compared to a noteworthy peer group. Our name for this is discovery engine, since it finds matching insights and plans, instead of listing benchmarking insights for a chosen plan as a benchmarking engine does.
Performing a query across calendar-year 2020 401(k) plans that show a comparatively high administrative expense ratio, have at least $1 billion in assets and are within 50 miles of 11 Wall St. in Manhattan yields this example insight, which has been anonymized and excerpted: Plan has the highest total administrative expense ratio (0.840%) among all 881 plans with more than $1 billion in assets, compared to the average expense ratio of 0.059%. Reaching the nationwide average of 0.059% would imply a savings of $18,914,150 in total administrative expenses.
Following the Challenger Sale approach, advisers can bring such underperforming insights to prospects and discuss remedies. An AI-powered rethinking of benchmarking and comparative analytics can lead to innovative, user-formulated queries that deliver factual, well-written, shareable, context-rich insights that catalyze performance transparency and help plan advisers uncover and engage with prospects.
