For most B2B revenue teams, signals stream in from many different sources: website behavior, keyword searches, intent signals, and so forth. The problem for GTM teams isn’t having too few signals; it’s having no context to understand what they mean together.
What signals can and can’t tell you
A signal is an observable event or behavior — a page visit, a job change, a funding announcement — that suggests something may be happening with an account. Something is a very important word.
What signals alone can’t tell you is whether that event is meaningful, who in the buying group it came from, or where the account is in its buying journey.
Unfortunately, a lot of go-to-market plans use individual signals as a green light for the wrong actions like blasting buyers with irrelevant outreach or alerting reps to chase accounts that aren’t ready.
Contextual intelligence enables smarter GTM motions
Contextual intelligence allows teams to build smarter GTM motions that understand buyer context, not just individual signals. 6sense AI brings unique value by applying machine learning from 13 years of buyer journeys, incorporating data from your own CRM and other tools.
Then it uses pattern recognition to spot meaningful buyer behavior so you can target the right accounts, share the right messages based on personas and buying stages, and trigger timely handoffs for direct conversations with reps.
Contextual intelligence changes how you run GTM. Specifically:
- Target the right accounts at the right time based on where they are in their journey
- Reach the right personas with messages matched to their role and buying stage
- Coordinate marketing and sales so handoffs happen when an account is ready for a direct conversation
- Avoid over-engagement that burns attention before a buyer is ready
- Feed AI tools like Claude and ChatGPT real-time account and campaign insights to power far more advanced capabilities, outputs, and analysis
Here’s how contextual intelligence works
Contextual intelligence takes buyer signals and interprets them through a predictive model that’s trained against actual buyer journeys. The result: It learns to recognize the patterns that predict which prospects are behaving like your buyers, so you can prioritize those accounts.
Contextual intelligence combines three layers of B2B data to reveal patterns in purchases, helping you recognize meaningful buying behavior.
The three layers of contextual intelligence:
- Proprietary intent data
- Customer data integration
- Predictive buying stage modeling
Intent data
Intent data help you spot the earliest signs of a buying journey so you can spot buyers. This is important because B2B buyers are typically 60% through their buying journey before they are willing to talk to a vendor, and by the time they have that conversation, they have already formed strong preferences about who they will buy from. By the time they fill out a form, it’s too late to influence the deal. Intent signals let you spot who is in-market, and what they care about so you can use targeted marketing to aid the buying journey — and steer your way onto the consideration list.
The 6sense Signalverse™ is a data engine that processes over one trillion signals every day. It’s built on 13 years of real B2B buying behavior patterns across 200,000+ B2B properties. The depth of its historical data enhances the accuracy and reliability of predictions.
With this historical data, the Signalverse™ can:
- Compare current prospects with past closed/won accounts for better targeting
- Provide insights that rely on historical patterns
- Use a vast dataset including firmographic, technographic, and behavioral signals accumulated over years
Customer data integration
6sense provides signals and the predictive AI, but it gets smarter when it knows more about your accounts. Customer data integration connects your CRM, marketing automation platform (MAP), and other data sources with 6sense data sets.
The integrated data tells the AI which accounts matter to you by analyzing the buyer behavior patterns specific to your business. This produces account intelligence that’s specific enough to act on, such as building campaigns against your target account lists and acting on buying signals in near real-time.
Here’s what that looks like in practice:
- Better account scores
- Richer account intelligence
- Smarter RevvyAI answers
- Signal stacking across platforms
- Campaigns that know what they’re targeting
Predictive buying stage modeling
Predictive buying stage modeling helps prioritize leads, accounts, and opportunities by indicating the likelihood of an account opening or progressing within the next 90 days. It is updated daily. The 6sense predictive model categorizes accounts into distinct buying stages based on scores ranging from 0 to 100:
- Target (0-19): Accounts showing little to no activity, suitable for broad awareness messaging
- Awareness (20-49): Accounts with some activity, considered early stage; targeted for broad-based engagements and mid-funnel activities above certain thresholds
- Consideration (50-69): Accounts with significant activity and longer engagement history; targeted with banners, content syndication, and buyer’s guide content
- Decision (70-85): Accounts showing significant digital research activity; treated as mid- to low-funnel with marketing “air cover” content and sales outbound prospecting
- Purchase (86-100): Accounts at the bottom of the funnel, most likely to open an opportunity soon; targeted for high-value engagements like telephone prospecting and sales touches
This modeling unifies sales and marketing teams by providing analytics that identify high-priority accounts and guide targeted engagement strategies throughout the buyer’s journey.
The contrast: Action without context
Without context, many marketing and sales motions are over-reactive by design. Signals immediately generate follow-up actions that may not fit what buyers actually need or want. The result is spray-and-pray at machine speed. Buyers experience it as spam. Reps get flooded with low-quality alerts. The market learns to ignore you faster.
Here’s an example:
Without context: “This account spiked on two of our intent keywords last week.”
With context: “This account is in an active evaluation cycle (moved from Awareness to Consideration eight days ago). Three buying group members are researching competitors. Anonymous research aligns with declared intent on your industry benchmark. The account hasn’t been touched by sales in 47 days despite this activity. High re-engagement opportunity.”
The first is a signal. The second is intelligence.
Customer stories
Consider Duo Security (now part of Cisco). Before integrating with 6sense, the sales team relied on LinkedIn and Discover.org. These tools gave them signals: a job change, a profile update, a keyword mention.
But they had no context: Is this account in-market? Do we know how many members are in the buying group? Is the contact information up to date?
That changed when the company integrated with 6sense:
“It’s the first thing I go to when starting my day. I can see the activity that happened today, yesterday, last week, or a month ago. I can see where that account is in their engagement … and revise my messaging to be hyper-relevant and important to them,” said Kat Mills, a Duo Security Account Manager.
Looking for a better way to identify and pursue net-new business, PTC partnered with 6sense (as well as Drift and People.ai). The partners created an opportunity score report that sales leaders regularly review to determine whether the sales team is focused on the right accounts.
The result: 1,200 net-new high-intent accounts identified in the first few months, and $18M in net-new pipeline in the first four months.
Conclusion
The market is full of signal aggregators. But it’s not full of contextual intelligence to help those signals make sense and guide teams toward the actions that produce pipeline and revenue. That’s what 6sense fixes. To learn more, book a demo now.
