Contact scoring is a methodology that involves ranking contacts (enter: leads and customers alike) based on criteria such as company information and how and how recently they’ve interacted with your business. It allows you to identify high-value opportunities, re-engage clients you can cross-sell or upsell to, and capture disenfranchised clients before they’re gone for good.
Data quality for lead scoring involves ensuring the data input into lead scoring models is clean, standardized, and scorable, resulting in more reliable lead scoring models and a smoother lead handoff process.
Data enrichment is a process that allows companies to gather raw data about customers and leads by matching the existing data in their existing database with third-party data. This results in updated and enriched data, giving pertinent information about contacts.
Lead scoring criteria are data points that you use to create a lead scoring model and rank leads. This can include demographic, firmographic, and engagement data.
Lead handoff is the process of handing over leads generated by marketing to sales so that the sales team can nurture, qualify and convert them into paying customers.
Firmographic data is a collection of descriptive attributes (aka key characteristics) about businesses that may be utilized to develop distinct market segments and find high-value clients.
Predictive lead scoring is a data-driven approach that applies big data and machine learning algorithms to lead scoring to find the right combination of behaviors and key attributes of existing and potential customers. Then, these attributes are automatically matched and ranked to those of new leads.