Predictive Lead Scoring


Lead Scoring and Prioritization

Predictive Lead Scoring

After a business develops a healthy inbound lead flow, the next step on the path to growth is to implement a systematic way for lead prioritization.

If your business is receiving 350 inbound leads per month (nice job – pat yourself on the back!) and 35 of those convert into orders, your sales team is spending 90% of their time chasing leads that probably should not have went to them in the first place.

You need a way to filter the high quality leads that are a good match for your company from the lower quality leads that may not be ready to interact with sales or may not be a good match at all.

How GleanView Scores Your Leads

GleanView offers Predictive Lead Scoring (PLS) as a scientific and more accurate approach to solve this problem. PLS starts with the information in your CRM database about each lead that resulted in an order and each one that resulted in a loss. Each one of these historical lead profiles is then enriched with additional data gleaned from the public web and third party data sources, resulting in a comprehensive set of attributes that include:

  • Demographic

    Title of contact, email address of contact, ip address
  • Firmographic

    Size of company, age of company, location of company
  • Technographic

    Web presence and website technologies, social presence
  • Digital Behavior

    Web pages visited, time on website, number of website visits
  • Derived Attributes

    Other attributes that are derived raw data such as entity verification

All of this data is used to create a sophisticated machine learning model that statistically compares the enriched attributes of an incoming lead to those of your best customers and then generates a predictive lead score from 0.5 to 5 stars (with 5 stars being the best) in real time. Over time as more and more win/loss data is collected in your CRM, the model self-adjusts and becomes even more accurate at predicting which leads are most likely to convert.

Using PLS, the highest scoring leads can be sent directly to sales for follow-up and the lower scoring leads can be nurtured using marketing automation until they either become engaged enough to qualify for sales interaction, or drop out of the marketing funnel completely. The sales team is happy and more productive because they are only getting sales-ready leads, and the marketing team can focus their efforts on the sources that are generating the highest scoring leads.

How Other Systems Score Your Leads

Some software providers offer a rules-based solution that scores an incoming lead based on a point system. The user of the software assigns varying points for demographic and behavioral attributes of the lead. While this system may be better than no scoring system at all, it can be arbitrary and misleading.

For example... 5 points are assigned to a lead with the title of “Marketing Manager” and 3 points are assigned to a lead that visited 10 or more pages on your website.