Neotask scores every lead the moment new signal arrives — a form fill, a pricing page visit, an email open — instead of running a nightly batch job that's already stale by morning. It combines firmographic fit, behavioral engagement from Segment, and enrichment data to produce a live score in HubSpot, and re-scores automatically as new activity comes in, so sales sees who's actually heating up right now, not who was hot at midnight.
Most lead scoring models are accurate in theory and useless in practice because they run on a schedule instead of on signal — a lead can go from cold to actively evaluating pricing in an afternoon, and a nightly batch score won't reflect that until the next morning, by which point a competitor may have already had the call. The other common failure is scoring on firmographics alone, treating a large enterprise account the same whether they've visited the site once or twelve times this week. Neotask treats scoring as event-driven rather than scheduled: it re-computes the score whenever new behavioral signal arrives from Segment, blends it with firmographic fit and enrichment data, and only surfaces the change to sales when it crosses a meaningful threshold — so reps get a timely nudge instead of noisy re-notifications for every minor point shift.
On lead creation, Neotask pulls company size, industry, and tech stack signals via Clay enrichment to establish a baseline fit score independent of behavior.
Integration: clay
It subscribes to Segment events — page views, email opens, pricing page visits, product trial actions — and updates engagement score continuously rather than in a batch.
Integration: segment
Fit and engagement are combined into a single score using a weighting your team defines (commonly weighted toward recent behavior for PLG motions, toward firmographic fit for enterprise sales).
The composite score, and the specific signals driving it, are written back to the HubSpot lead record so reps see not just a number but why it moved.
Integration: hubspot
Sales gets a Slack alert only when a lead crosses a defined threshold (e.g. cold-to-warm, warm-to-hot) rather than on every score tick, avoiding alert fatigue.
Integration: slack
Closed-won and closed-lost outcomes are fed back to periodically recalibrate which signals actually predicted conversion, so the model doesn't stay static as your buyer behavior evolves.
Within minutes of new signal arriving — it's event-driven, not on a fixed schedule, so an active session today shows up today, not tomorrow.
Yes — MQL and SQL thresholds can use different weight profiles on the same underlying signal set, so marketing and sales don't have to argue over one number meaning two things.
The model applies decay and smoothing so one anomalous session doesn't permanently inflate a score; sustained engagement is weighted higher than a one-off spike.
The feedback loop from closed-won/lost outcomes is reviewed periodically (commonly quarterly) to check the score's correlation with actual conversion and re-weight if it has drifted.
$0/mo
Download without a card and start for free.
$50/mo
The full personal agent platform for one person.
$100/mo
One company workspace with room to add your team.
$200/mo
Multiple workspaces and capacity for larger teams.