As account based strategies grow in importance, sales and marketing teams increasingly rely on artificial intelligence to help them decide the accounts on which to focus their efforts. But there are a couple of key challenges that any account based approach ultimately depends on for success a robust data foundation and a flexible configuration to fit an established strategy.

Account Scoring is the latest CRM product to emerge from Data Fox. It has been designed to help businesses to work out quickly and easily which clients to prioritize. Data Fox already has information on more than two million companies to help firms evaluate which companies to add to their CRM database and which to prioritize. 

Data Fox has been harvesting company data based on a range of criteria, including firmographic, technographic and signals data. 

Customers scores apply to all of the two million plus companies which Data Fox is following. This extends beyond AI scoring, adding an additional human element and helping surface new, potentially hidden prospects. 

Data Fox increases the number of deals sourced by helping to identify and prioritize business opportunities that would otherwise be missed.

The scoring piece is an important element of the entire offering by empowering the entire team from the head of operations to individual contributors. Every member can see the underlying criteria and understand the rationale for the score. 

Key Takeaways: 

  • Data Fox’s Account Scoring feature is designed to eliminate the grunt work in an enterprise, allowing personnel to focus on smarter, more strategic activities. 
  • The data can form the backbone of account-based strategies with a synthesis layer that leverages both Data Fox and client CRM data to deliver a unified view of best fit accounts. 
  • While companies CRM strategies evolve regularly, it is difficult and expensive to gather all the necessary data companies need to keep company rankings relevant. 
  • Data Fox enables users to incorporate new datasets automatically and iterate on the scoring model.