Article
Intent Data Should Work Harder
Remember – a good lead at a bad company is ultimately a bad lead.
Everyone benefits from having a list of all the people and companies that have been actively searching for your product or service this week, right? That’s been the promise of Intent data. GTM teams rely on that Intent signal and often waste even more of their valuable time and resources chasing down what amounts to bad leads. Clearly, intent models don’t work very well – they should’ve just trusted their gut, right?
Wrong. They should’ve trusted the data. As in, all the data. Not just intent. Relying on intent data alone is the error that pushes some many sales and marketing teams away from it. You need to remember, intent alone doesn’t paint the full picture necessary to target deals in the B2B world. In fact, intent data can even point us in the wrong direction and encourage our sales people to confidently jump into a rabbit hole that goes nowhere fast. Let’s explore how to use intent data effectively and avoid diving head-first into a pit of bad leads.
The cost of getting it wrong.
Latest Articles
Article
How Do You Calculate Match Rate on B2B Data?
Your CRM has thousands of records. Your marketing automation platform is loaded with contacts. Your outbound sequences are running. But when you try to enrich those records, route leads to sales, or fire a signal-based workflow, a large portion of your data simply does not match.
Match rate is the metric that tells you how much of your data is actually usable. For revenue teams running enrichment programs, account-based campaigns, or automated scoring, this number carries real operational weight. A low match rate means your workflows are running on incomplete information, your segments are thin, and your automation is making decisions with missing context.
Understanding how to calculate b2b data match rate, and what actually drives it, is one of the most practical things a RevOps or marketing ops leader can do to improve GTM performance.
Article
What Is Signal-Based Selling?
Your CRM is full of contacts. Your marketing automation platform fires campaigns on schedule. Your sales team works the list. And yet, deals stall, outreach lands flat, and pipeline forecasts drift further from reality every quarter.
The problem is not effort. The problem is timing.
B2B sales teams have spent years optimizing how they reach buyers. Very few have focused on when buyers are actually ready to engage. Signal-based selling changes that equation entirely. It shifts your go-to-market execution from a calendar-driven model to a behavior-driven one, so your team shows up when intent is live, not when the cadence says it is time.
This post breaks down what signal-based selling is, why it matters now, and what your revenue infrastructure needs to support it at scale.
Article
Aligning marketing and sales on inbound SLAs that work
Your inbound engine breaks when marketing and sales work from different clocks, different definitions, and different routing rules. That gap shows up fast in missed follow-up, weak conversion, and low trust across teams.
If you want inbound SLAs that hold up under volume, you need more than a handoff document. You need aData Management System that keeps records clean, routes leads with context, and gives both teams the same operating view.
That is the GTM alignment moment most teams miss. Marketing says the lead hit the threshold. Sales says the lead lacked context, landed late, or reached the wrong rep. Both teams look at the same funnel and see different stories.
A working SLA removes that ambiguity. It ties response time, routing logic, ownership, and enrichment to a shared data foundation.
