Let’s say you work on the business development team at ChairDB, a database company which follows a bottoms-up sales model. Your users can create accounts and use your software for a small monthly subscription fee. However, the majority of the revenue comes from a small number of customers that pay large subscription fees, thanks to their usage volumes and use of premium features.
As a member of the business development team, your role is to identify customers that are currently on a small subscription, but that likely can be upsold to higher tiers. Traditionally, you have done it by eye-balling your customers’ usage data, or by proactively reaching out to users that could use premium features based on the companies they work at. You are frustrated because it’s always a pain to identify which customers to reach out to. You reach out to too many users, and suddenly you annoy some users (while wasting your valuable time); alternatively, you reach out to too few users, potentially missing users that could have been upsold.
You always wished you had some way of prioritizing which users to reach out to.
Manual lead scoring is often an error prone, imprecise process that may produce unreliable results. This may result in poor follow-up with your most valuable customers. In fact, 70% of leads and sales are lost because of this.
Fortunately, machine learning has advanced to the point where there is no need to do this the old way. According to the Harvard Business Review, sales teams that adopt machine learning see a 50% increase in leads and appointments, and a 40-60% reduction in costs.
Machine learning is the process by which a computer can learn patterns from data and apply those patterns to generate output in new data.
By now, you already know how machine learning can be used to identify sweet mangoes. It’s not a big leap to suggest that the sweet-mango-identification problem is not that different from the best-lead-identification problem.
At ChairDB, you have access to all kinds of historical information about your users:
You could throw all this data at the computer to build a machine learning model that could classify users into two categories going forward: Upgraded, Did not upgrade. This is possible because the machine would learn the patterns between all other columns and the “Status” column to come up with a “calculator” to output predicted status.
Even better: if setup correctly, the model could also output the likelihood that a user could be upsold. In other words, instead of just predicting whether a user could be upsold, your model could tell you the degree of certainty with which a user could be upsold. This will help you prioritize even better and in turn, increase revenue.
Most importantly, you can do all of this as your leads come in, without the need to wait for months to analyze their data.
Intersect Labs makes it incredibly easy to build a machine learning model to help you prioritize your leads. No prior coding experience or mathematical background necessary! Book a call today with one of our expert machine learning engineers and see how a few minutes is all it would take for you to be on your way to always talking to the highest value leads.