Lead scoring is a methodology used to rank prospects against a scale that represents the perceived value each lead represents to the organization. The resulting score is used to determine which leads a receiving function (e.g. sales, partners, teleprospecting) will engage, in order of priority. Lead scoring models incorporate both explicit and implicit data. Explicit data is provided by or about the prospect, for example - company size, industry segment, job title or geographic location. Implicit scores are derived from monitoring prospect behavior; examples of these include Web-site visits, whitepaper downloads or e-mail opens and clicks. Additionally, social scores analyze a person's presence and activities on social networks. Lead Scoring allows a business to customize a prospect's experience based on his or her buying stage and interest level and greatly improves the quality and "readiness" of leads that are delivered to sales organizations for followup.

Key Benefits

When a lead scoring model is effective, the key benefits are:

Lead Scoring Methodologies

Various lead scoring methodologies are employed: Businesses iterate on existing methodologies and change methodologies in an effort to better prioritize sales engagement. As businesses grow in headcount & the number of products they sell, predictive lead scoring methodologies are generally favored for their ability to ingest new customer data routinely and evolve its predictions.

Predictive Lead Scoring

With machine learning, lead scoring models have evolved to include components of predictive analytics, generating Predictive Lead Scoring models. Predictive Lead Scoring leverage first party data - such as internal marketing, sales & product data - as well as third party data - such as data enrichment & intent data - in order to build a machine learning model of the ideal customer profile. Predictive Lead Scoring models can also be used to identify, qualify & engage product-qualified leads based identifying statistically differentiating elements in historical user behavior which best predicts whether a user will spend above a certain threshold. Predictive Lead Scoring is particularly beneficial for SaaS businesses, which have a high Customer lifetime value & a plethora of customer data. Predictive lead scoring models enable businesses to identify high-value prospects early in the buyer journey, creating a FastLane experience for prospects predicted to be a good firmographic & behavioral fit. The success of Predictive Lead Scoring models is measured by their ability to identify a subset of prospective buyers who will account for a significant portion of sales opportunities. This is expressed in the following way: X% of leads represent Y% of conversions Optimal performance of a predictive lead scoring model sees X approaching 0, Y approaching 100 & conversions defined as a bottom-of-funnel metric such as opportunity created or opportunity won.