Prioritizing when and why to engage a specific lead is a pain point every sales team faces. The key to turning this challenge into an opportunity lies in leveraging unique, customized data attributes to your advantage together with the power of machine learning.
With these challenges in mind, ProPair initially developed ProPair RANK, a lead conversion probability ranking system. The tool was developed to help sales teams better manage lead prioritization, guide follow-up and revisit abandoned opportunities. Built with these three core outcomes in mind, RANK leverages data-driven insights to focus sales efforts at scale and without bias which ultimately improves results and efficiency over conventional lead handling practices.
Working with our customers, we’ve spent the last year further refining the core RANK product to provide customers visibility into individual lead conversion rankings real time (and over time) by incorporating lead engagement measures. This was the inspiration behind our latest product enhancement which we’re calling Dynamic RANK. Dynamic RANK is a tool that allows sales teams to fine-tune and configure their lead management and contact strategies based on how engaged a prospect is at a specific moment in time.
ProPair’s Chief Data Science and Technology Officer, Devon Johnson PhD, shares more details on this latest, highly customized product enhancement in a recent Q&A.
What is different about this new dynamic ranking product enhancement?
Our core products were engineered primarily based on lead attributes along with the performance history of the sales team against these lead attributes. Dynamic RANK takes this a step further to overlay engagement measures like call duration in combination with lead attributes to drive real-time conversion or nurturing signals.
Instead of just coming at data from a historical context, you now have data-driven insights with each lead together with engagement metrics that predict why and when a lead is more likely to engage or convert. The model is also configured to run at any point in time and can be customized to an individual sales organization.
Why is Dynamic RANK’s ability to analyze lead engagement over time so valuable to customers?
Dynamic RANK is focused on the context of an engagement — at a specific time and how that lead’s interactions change over time. That’s the beauty of machine learning technology. Rich amounts of data that you don’t always consider at the start become increasingly valuable over time as you start seeing the data and understanding the context.
It’s extremely helpful to gain insights the day after an engagement occurs versus waiting for 10 days after the fact when the lead might no longer be valuable. It’s important to know when a lead is most valuable and most likely to engage. Dynamic RANK gives deeper visibility into lead activity to determine how valuable leads are at a specific moment in time — and how valuable they become over time.
How do you expect Dynamic RANK to evolve over time?
We’re exploring a new feature that alerts a user when they should engage or re-engage a lead. This process would determine — based on lead engagement activity — when is the right time to contact a prospect based on a probability that you could pull a prospect back into the sales process. Our machine learning algorithms can provide insights into which direction a prospect is trending.
Being able to understand lead conversion in context with lead engagement over time helps determine when and why loan officers should nudge that lead back into the sales funnel. If there are signs that a prospect has lost engagement — or has gotten more engaged — this would signal to the loan officer when the time is right to reconnect, or back off for the time being.
How do the problems that Dynamic RANK addresses fit into ProPair’s machine learning story? How is this different from other machine learning solutions on the market?
Our machine learning technology allows clients to substantially reduce the time it takes to analyze the value of their entire lead mix and adapt their contact strategy accordingly. You can get massive economies of scale by applying this technology.
Results derived from machine learning are only valuable if you have the context and direction on how to use them, which is why ProPair is a full-service solution. Anyone can create a machine learning model, but the real value of our technology is the ability to apply the technology to customized use cases and specific business processes. Our consumer direct mortgage industry focus and expertise help clients make incremental gains and apply the technology to the most impactful use cases. And, more than just static scoring or models, these are dynamic applications custom-built to address real pain points experienced across the consumer direct mortgage industry.
Ready to gain a competitive advantage? Learn more about how RANK can be dynamically utilized throughout the lead lifecycle.