Key Features
Convert buyer signals into intelligence you can act on.
Account buying stages
Eliminate guesswork and bias when interpreting buyer behavior. Machine learning models pattern-match daily activity against behaviors that led to won opportunities from similar accounts. Teams and AI agents get an unbiased view of where each account sits in their journey, complete with a plain-language explanation of the signals driving their stage.

Account profile fit
Proprietary algorithms evaluate target accounts against past winning deal patterns, factoring in firmographics, installed technologies, and opportunity history. Teams and AI agents gain transparent evidence behind every fit score, focusing resources on companies with a proven propensity to convert.

Persona importance
Engage the right decision-makers across the buying group. A good account can still have the wrong people engaged. Machine learning models score contacts against your past won opportunities and calculate persona importance, showing your teams and agents which roles carry the most weight so they can multi-thread the buying group.

6sense Qualified Account (6QA)
Combine account fit and buying stage into a single actionable qualification flag. When an in-profile account reaches Decision or Purchase stage, a 6sense Qualified Account (6QA) designation alerts sellers to prioritize outreach immediately while marketing delivers continuous, coordinated air cover. Prioritizing these accounts drives proven revenue impact, increasing opportunity volume, average deal value, and overall win rates.

The context you need to build a winning revenue strategy.
Comprehensive Signals
Keyword and partner intent, website visits, marketing engagement, and your own CRM activity feed every model.
Custom Predictive Models
Machine learning models analyze the behaviors and profiles behind your past winning deals to determine which accounts and contacts fit best, and where they are in their buying journey.
Coordinated Action
Match your GTM motion to the buyer journey at scale. Run awareness campaigns for early researchers, automate agentic outreach as interest grows, and focus sales reps on opportunities most likely to convert.
445%
increase in 6sense-influenced pipeline.
Bonterra
$52M
pipeline generated in year one.
Socure
82%
pipeline growth from 6QA-flagged accounts.
Flexera
Additional features.
Use Cases
5 common challenges solved.
Who it helps.
See what success looks like.
Frequently asked questions.
Predictive analytics for marketing collects data to be analyzed using predictive models that identify ideal accounts, where they are in the buyer’s journey, and what contacts make up the buying team. Marketing uses these insights to effectively target buyers at the right time in their buying stages with the right message.
Predictive analytics for sales collects data to be analyzed using predictive models that identify ideal accounts, where they are in the buyer’s journey, and which personas make up the buying team. Sales use these insights to effectively target buyers with the right message at the right time in their buying stages.
Some examples of predictive marketing analytics are ingesting intent data as a buyer researches on your website and after predictive modeling displaying the likely buying stage of that account. Another is taking historical data of how many interactions a certain buyer requires to move to the next stage in their buyers journey and using that insight to highlight buyers that likely need more marketing touches.
Some examples of predictive sales analytics are ingesting intent data as a buyer researches on your website and after predictive modeling displaying the likely buying stage of that account. Another is taking historical data of how many interactions a certain buyer requires to move to the next stage in their buyer’s journey and using that insight to highlight buyers that likely need more marketing touches.













