How are predictive scores different from traditional lead scores?
Predictive scores are far more accurate because the models we’re built on access large external datasets and use machine-learning.
The tip of the iceberg is made up of your traditional buying signals. This usually includes signals from your CRM and marketing systems that offer a great initial, albeit limited, view of your leads.
Predictive platforms, on the other hand, track and correlate all the buying signals that are “submerged”. They provide added context to your leads and help drive more informed marketing decisions.
These predictive signals and the scores themselves are augmented with data from a plethora of sources, including social media, websites, public and proprietary databases, hiring trends, news and event sources, and other means.
- WHY ARE PREDICTIVE SCORES IMPORTANT?
Most marketing processes are muddled with irrelevant leads, which are passed on to sales and result in a significant amount of wasted time for the sales team. Only 30% of MQLs turn into SQLs. Using predictive models, marketers can reduce the required number of leads their sales team must hit to reach their goals while also ensuring that the right campaigns are created to help support their targeted efforts. Reducing the required number of leads, but rising their significance, the sales team will engage the right audience and deliver better results.
- WHY CHOOSE PREDICTIVE SCORING
B2B companies are increasingly using outsourced predictive models, along with the built-in machine learning and large external datasets, to identify accounts with the highest potential to convert. In such way, marketers are able to use scores and grades to build prequalified lists and segment accounts to gain insights on common attributes of ideal prospects. Marketers look to predictive scores when the traditional “two dimensional” demographic data scoring solutions are insufficient to identify the best leads that need to be prioritized for the sales team.
