Score Leads with Intent Data

The problem

Most B2B lead scoring models are stuck in 2015. They assign points for downloading a whitepaper, visiting the pricing page, and having a VP title. The result is a queue of “MQLs” that sales doesn’t trust — because a VP who downloaded one ebook is not the same as a VP who’s actively researching solutions, reading competitor comparisons, and bringing colleagues to your site. SDRs learn to ignore the score and cherry-pick leads based on gut feeling, which defeats the purpose of having a scoring model at all.

The deeper issue is that traditional scoring only captures explicit actions on your own properties. It completely misses third-party intent signals that indicate a company is in an active buying cycle.

How GTMStack solves this

GTMStack combines first-party engagement data with third-party intent signals and firmographic fit to build a lead scoring model that actually predicts purchase likelihood.

Multi-dimensional scoring. Each lead gets scored across three axes: fit (does this person match your ICP based on enrichment data), engagement (how much have they interacted with your content, emails, and site), and intent (are they or their company showing buying signals on third-party sites). The composite score weights all three dimensions so you don’t over-index on any single factor.

Intent signal ingestion. GTMStack ingests intent data from Bombora, G2, TrustRadius, and custom sources. When an account starts researching your category on review sites, that signal boosts the score of every contact at that company. Combined with first-party signals like repeat website visits and email engagement, you get a much more accurate picture of readiness to buy.

Behavioral pattern recognition. Beyond simple point accumulation, GTMStack identifies behavioral patterns that correlate with conversion. Multiple people from the same company visiting your site within a week. A contact returning to the pricing page three times. A sequence of content consumption that mirrors your typical buyer journey. These patterns trigger score boosts that individual actions wouldn’t.

Score decay and recency weighting. Old actions count less than recent ones. A whitepaper download from six months ago doesn’t carry the same weight as a demo page visit yesterday. GTMStack automatically decays scores over time so your lead queue reflects current intent, not historical curiosity.

SDR queue prioritization. The lead generation module uses the composite score to dynamically reorder each SDR’s work queue. The highest-intent, best-fit leads always surface to the top. When a lead score spikes due to a new intent signal, the owning SDR gets an alert to act immediately.

Review detailed scoring methodology and best practices on the blog.

Results you can expect

Teams that implement intent-based scoring in GTMStack see meaningful improvements in conversion efficiency:

• 35% higher SDR-to-opportunity conversion rate by focusing effort on high-intent leads
• 50% reduction in time wasted on unqualified leads that would never convert
• 28% shorter average sales cycle because reps engage prospects who are already in a buying process
• Higher sales-marketing alignment since both teams trust the same scoring model

The real value compounds over time as GTMStack learns which signal combinations predict conversion for your specific business and continuously refines the model.