Marketing Automation Requirements for Predictive Lead Scoring
Designed to be used by the professional that needs to include multiple types of data in their lead score models. Spanning marketing, sales and service types of activity.
Include engagement, sentiment and platform behavioral data in process to complete the lead scoring solution
Dynamicially adjust the input parameters to predict which lead nurturing activity is going to produce an effective result
Work directly with predictive lead scoring analytics models or the data scientists on your team to define which criteria the machine learning should consider.
Combine lead scoring with propensity models to direct your customer to the right solution everytime
View in the context of activity as it's embedded into other applications and points of use with Put It Forward Foresight
AI data analytics and predictive insights embedded into the places where your team does the most valuable work - in real time with the customer. Include sentiment analysis right into active decision making process.
Every AI data analytics project has 7 core steps that need to be in place to ensure predictive success. Find them here with the detail needed to validate conditions for success are present.
Repeatable patterns for success to scale by automation and AI driven processes. Learn from how we’ve managed to leverage these two concepts for scale.