How intelligent lead assignment gets the most out of loan officers.
By Ethan Ewing
Loan officers have a lot to do and not much time to do it. They’re making calls, sending emails and following up with new and existing leads all day, every day. Ideally, they have strong customer service skills, sales expertise and in-depth knowledge to gain the confidence of their prospects. But not all loan officers are equally competent in all areas, which becomes painfully obvious when they reach a prospect whose needs or personality pair unfavorably with the loan officer’s weaknesses. This can kill a prospective loan before it ever has a chance.
Some prospective customers just want to talk about their lives and why they need the loan, while others need to know every single detail about a plan before they sign anything. The problem is that when the initial lead assignment decision is made at random or using fixed rules, the opportunity to match the prospect with a loan officer who is more likely to match their specific preferences is wasted. But what if you could rely on hard data as a basis for lead distribution to maximize your chance of success?
As an example, let’s look at Amy, a loan officer at Emerald Lending. Amy is a productive employee, always on time and working diligently to improve herself. She’s up-to-date on the latest strategies, reads trade publications to fine-tune her technique and has good numbers to show for it. However, she seems to have hit a ceiling. Her close rate is flat and has been for many months. As her manager, you want to help. Both of you have the same goal and both of you are motivated to reach it. She seems to be doing everything she can, but the random assortment of leads that come in the door don’t seem to be doing her — or anyone else in the office — any favors. That’s where Prospect Matching comes in.
Prospect Matching is a turnkey solution that optimizes lead assignments based on each team member’s proven strengths and tendencies, replacing random or fixed rule assignments. Strengths — and weaknesses — are identified using the best indicators of future performance: data from employees’ past successes and failures. Prospect Matching software uses machine learning to catch the trends and patterns accessible in the data, like the way Amy sends more enthusiastic emails first thing in the morning and right after lunch. The solution might also identify that her calls get better responses around mid-morning — just after she takes her morning jog and is brimming with confidence. Then Prospect Matching automates the lead assignment decision, replacing a random or fixed rule decision with a predictive, automated decision.
This strategic assignment of leads maintains equitable distribution of opportunities, built on the unique strengths of all your loan officers. And with all the data Prospect Matching collects, loan officers can learn both how to take advantage of their best moments and strive to improve the situations where they aren’t as effective. This information improves the close rates of individual loan officers and limits prospects getting assigned to loan officers with whom they are unlikely to develop confidence.
Leading lenders are already using Prospect Matching to optimize lead performance and take advantage of the full potential of their loan officers, improving close rates by more than 10 percent in the process. Offering their loan officers random leads and prospects only goes so far, and in a business environment in which data-driven decision-making reigns, lenders using matching platforms are gaining on those using traditional assignment methods. The artificial intelligence engine at the heart of Prospect Matching helps leaders create a hyper-functional team and gives them more time to provide the support that loan officers need. Knowing lead assignments are being managed efficiently, managers can work with loan officers to improve the strengths they already have — and build new ones — to help make them their most efficient selves.