(Co-author: Michael Ziegler)
Lead scoring is an amazing lead management tool to decide when a lead is potentially ready to make a purchase and then gets moved from marketing to sales. Modern analytics tools can help adjust the variables that define the scoring according to success rates and individual lead behavior.
Beware the cold call
You might know this: you download a white paper about a B2B issue that you’re interested in. The download form asks not only for your email but also for your phone number. You stop for a second but then think that it doesn’t matter anyway, the topic is interesting and you simply give them your work number.
Three days later a nice sales manager calls you in the middle of your work day: you’ve downloaded the white paper and whether you are interested in the software solutions of the company that produced the paper? You decline the offer and ask yourself how successful this kind of lead nurturing really is.
Lead scoring: what turns a lead into a customer?
Lead scoring is here to save us all from cold calls like this because it defines a scoring model for leads as to when is the right time to contact them or hand them over to sales. By defining and evaluate criteria that show how close a lead is to convert or how high in quality the lead is (if the lead is a CEO, for example), the handover from marketing to sales can be defined by clear numbers and the lead’s behavior. You therefore wouldn’t get the call right after you downloaded that one white paper but maybe after you also registered for the newsletter, downloaded a couple of other white papers and clicked on an overview of the company’s products.
The variables usually differ depending on the company, industry and product/offering: lead behavior, interaction rate, click- and open rates of emails. Hierarchies, as already mentioned, also play a part in evaluating the score. Each variable will be weighed according to its impact on the final purchasing decision. If enough “weighty” scores come together to reach a benchmark that both marketing and sales agreed upon, the handover will be automatically communicated to the sales team.
Find out more about how service level agreements can help getting marketing and sales activities and responsibilities on one track.
How does predictive lead scoring optimize the process?
Predictive lead scoring enhances the process by using customer analytics. This way, the impact of each variable is measured by its actual impact on previous leads that have converted (or haven’t converted). Instead of deciding beforehand (often on a whim) which variables should have which weight within the scoring model, predictive analytics measures how often a certain behavior or property has been crucial to a conversion and defines the impact accordingly.
Furthermore, the algorithm uses data from converted customers and their behavior as leads. Then it compares the behavior of converted customers with new leads. That way, the impact of each variable is not set for all leads disregarding their behavior. Instead, different lead types can be measured differently.
For example, the behavior and actions of a lead that generally doesn’t read emails but is very interactive on social media get rated differently than those of a lead that has more interest in email marketing. The scoring adapts to the behavior of the lead which heavily influences the success rate.
Let’s look at a sports example
Imagine a football team that has one single tactical approach to every game. Even if the team is amazing and the approach successful most of the time, there always will be other teams where the tactic does not work and even hinders the team from winning.
What if the team now adapts its tactical approach to fit the tactic of the opposing team? By watching games of the other team and analyze their movements and formations, the team has a much better chance of encountering them and winning the trophy.
This is basically how predictive lead scoring works. Instead of treating and measuring all leads with one static scoring model, each lead gets scored individually according to their interests, properties and behavior.
Want to know more about the possibilities of predictive analytics? Our fact sheet gives an overview on the why and the how.