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The key to successfully identifying sales-ready leads is having an effective lead scoring system in place.Â
However, over time lead scores can degrade as buyer behaviors change.Â
Therefore, it’s critical to consistently monitor changes in lead scores to identify downward trends and prevent revenue leakage.
In this article, we discuss best practices for identifying and addressing degrading lead scores to keep your sales pipeline healthy.
Lead scores naturally go up and down during the buyer’s journey.Â
A sharp drop in lead score over an extended time can indicate low buyer intent or engagement.
But what’s the best way to identify such a drop in a timely manner?Â
These are just some of the data points you can analyze regularly to spot degrading scores:
If you notice any of the above with some of your leads, it likely signals that their scores should be degraded.
A data scientist can help you analyze and interpret your data on a daily basis, so you can make adjustments to scores and inform your sales reps quickly.
But if you don’t have a data scientist on your team, trying to manually identify such changes in lead quality can be anywhere from challenging to impossible.
Rather than manually trying to identify lead scores that trend down, Savvy marketing operations teams implement predictive lead scoring.Â
Self-learning predictive scoring models automatically surface leads trending down (or up!) and notify sales reps instantly via tool they use daily like Salesforce or Slack.
Instead of trying to sift through data hopelessly, or using the help of expensive data teams, the latest innovations predictive scoring solutions:Â Â
Re-scoring leads based on real-time data allows you to feed sales reps with the right leads and accounts while reducing wasted efforts.
The accuracy of outdated scoring methods, like rules-based scoring systems bundled with HubSpot and Marketo, depends on a human arbitrarily making all the decisions and assumptions.Â
In a perfect world you would be able to build the degradation that is bias-free, using a model that is based on correlation with success in the past.Â
A machine learning-based lead score can do that automatically, but it's nearly impossible to do that manually for each scoring rule you implement.
If you can’t afford an AI-based, automated solution like Forwrd and already have data scientists on the payroll, then do that!
Your best chance is to get data scientists involved and have them help you understand the true likelihood of conversion for the various stages in your acquisition funnel.
This will ensure you eliminate bias and human error and put you in a better position to score prospects accurately and identify positive or negative change in said scores.
Degrading lead scores that are left unaddressed can directly reduce sales productivity and pipeline health.
The key is taking a proactive approach by implementing predictive scoring and consistent model optimization.Â
This will enable you to instantly detect deteriorating lead engagement so sales can pivot resources accordingly.
