Opportunity exists today to make machine learning technology an integral and differentiating element of business development strategy in the new year. There are easy ways to leverage the technology to create improvements to lead handling decisions and conversion results, even in today’s market. We’ve gathered three core use cases demonstrating how the industry can embrace machine learning to improve business practices. These use cases are focused on lead management which is an often-overlooked area of the equation when the topic of machine learning is discussed within the industry.
Machine Learning Use Case No 1: Managing Lead Assignments Accurately and Fairly
Developing a system to go beyond common attributes to assign leads to the loan officers most likely to convert them always seemed like a powerful opportunity for the industry. However, the concept had challenges getting off the ground in the implementation and execution. Would it really be fair to the individual players? Would it only give certain loan officers all the leads? Would we risk pigeon-holing other loan officers? What attributes would prove most predictive in determining fit?
The irony is that traditional lead management processes have a lot of built-in biases. Whether done consciously, or unconsciously, decisions made based on simple automations and workflows are typically driven by human instinct and assumptions, even when backed by industry experience. These biases actually lead to suboptimal lead assignment processes. Machine learning removes these inefficiencies and biases from the lead management process by empowering sales leaders with data to make decisions that maximize team productivity.
Without automated data sets produced by machine learning technology, sales managers only have what they have experienced and personally know about their team, to inform their lead assignment decisions. They may have some performance data and trends. But, without a scientific approach to analyze these trends, interpretation is still subject to human error. Machine learning combines lead data and historical performance in a mathematical, scientific way to determine how and when to distribute leads to maximize performance.
Machine Learning Use Case No 2: Objectively Prioritize Your Best Leads
Consumer direct mortgage lenders are often overwhelmed by prioritizing and assigning leads, especially in today’s market. To further complicate matters, loan officers often call leads at random, have limited follow-up activities on leads, and waste too much time chasing bad leads.
Machine learning creates a structure around lead prioritization by objectively getting the best leads prioritized within the pipeline. Instead of chasing bad leads or leads less likely to convert over others, loan officers are empowered with data that indicates how to focus their efforts on the leads most likely to convert and when. But this benefit doesn’t just apply to lead assignment.
Our global lead data shows 28% of all closed loans originate from reassigned leads. Relying on data science to drive your lead re-assignment processes can transform middling loan officers into a population of high performers – boosting conversion rates across your team.
Sales leaders are often overwhelmed with too much data and not enough intel into how to strategically elevate lead conversion. Machine learning gives teams the tools needed to make those decisions without relying on manual analysis, spreadsheets, or static workflow rules.
Machine Learning Use Case No 3: Automate and Optimize your Lead Management Systems
Semi-automated improvements to lead handling processes and technology, such as the implementation of dialer and lead management systems, have created efficiency in lead management practices. However, these methods are often well-planned when implemented, but leave opportunity on the table over time due to lack of ongoing optimization and regular updates. These updates are difficult to manually manage, but machine learning eliminates that hurdle.
Sales leaders need to track their loan officer’s performance, but they also need tools that empower their loan officers to maximize their strengths, and minimize their weaknesses. Poorly optimized systems lead to poorly managed teams. Our data shows inefficient lead distribution can cause 65% of your loan officers to perform below expectation. Machine learning optimizes lead management systems by relying on validated data to help teams focus on the most qualified leads.
Machine learning has proven to make impacts. Knowing a task as critical as lead assignment is done in a reliable, accurate, and consistent manner makes for an incredibly strong value proposition for machine learning. If you need more proof, read how Kansas City-based BNC National Bank, a home loan and mortgage refinancing institution, used ProPair’s machine learning technology to evaluate layers of data in real time to effectuate a six-figure monthly revenue lift.
