Before starting with the actual topic, let’s take an example case here. Consider a scenario (which could be an actual story for a Sales guy); quarter end is coming closer, you are struggling to meet your targets and you still have a huge contacts list to follow-up on. How will you prioritize from the most likely buyers with the redundant ones? Are you going to manage it as per your past experience? What if you don’t even know these contacts? Tough isn’t it!
Well, if you can connect with this, it’s time for you to start prioritizing your leads on the basis of their sales-readiness. On an ideal world, marketers tend to determine an ideal prospect on the basis of CRM/Marketing Automation data and assign scores to different metrics like location, budget, etc. Accordingly the list is finalized on the total score; and passed on to the sales for prospecting.
This is similar to the ‘Traditional lead scoring’ technique. The problem with using this is, it’s influenced by personal feelings and thus is prone to mistakes; as it somehow accepts that one of the metric is relevant to the buyer’s decision. This is exactly where data science comes as your savior with predictive lead scoring.
In my next part, I’ll elucidate more in detail about the quickly moving power of Predictive lead scoring solutions and what pointers to take care of when you’re ready to commence using one!
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