Lead scoring that explains every score in plain language
Trailspark learns what qualified means from your closed-won data, then scores product usage, demand gen, and ICP fit together. Every score explains itself in plain language your reps will actually trust.
Most lead scoring cannot see your product users
Three common scoring patterns leave revenue on the table for modern B2B SaaS teams.
Legacy MAP scoring is blind to product usage
HubSpot, Marketo, and Pardot can only see the marketing signals they themselves collect. A trial user opening your product daily and connecting data sources looks identical to a dormant lead if they have not clicked an email.
Black-box AI scoring does not explain itself
Most AI scoring tools output a number with no narrative. Reps stop trusting the score after a few bad leads and go back to gut feel. Trust in the scoring model collapses the moment it cannot justify a decision.
ABM tools score accounts for outbound, not conversion
6sense and Demandbase rank target accounts for outbound. That is a different problem than qualifying the users already in your funnel. Running an ABM tool on inbound pipeline ranks the right accounts for the wrong job.
How Trailspark scores a lead
Four steps from raw signal to scored lead with reasoning. No black box.
Ingest signals from product, marketing, and CRM
Flexible webhooks accept any event structure. Route product events from Segment, RudderStack, Amplitude, Mixpanel, or direct from your backend. Pull marketing engagement from HubSpot or Marketo. Read CRM state from Salesforce or HubSpot. No schema lock-in.
Resolve users to their organization
Identity resolution matches users to their organization via email domain and firmographic matching. Product users who signed up with personal emails, CRM contacts at known domains, and anonymous touchpoints all connect to a single org view. This is account-level resolution, not cross-email person-level stitching.
Evaluate product usage, demand gen, and ICP fit together
Trailspark runs one LLM-driven evaluation across every signal source. SparkSense learns your ICP from closed-won deals and recommends model updates when your market shifts. Scores reflect the full context, not isolated signals weighted in isolation.
Return a score with plain-language reasoning
Every score comes with the signals that drove it, the weight applied, and a confidence percentage. Reps see why a lead is flagged hot before they pick up the phone. One-click feedback marks scores as right or wrong and the model adapts to your corrections over time.
What makes Trailspark different
Capabilities that compound into scoring your team will actually act on.
Plain-language reasoning on every score
Every score cites the specific signals, weights, and confidence that drove it. Your reps read the reasoning, not a black-box number.
Organization-level identity resolution
Product users signed up with personal emails map to the same organization as CRM contacts at known domains. One org view across product, marketing, and CRM.
SparkSense ICP modeling
Automatically builds ICP models from closed-won data, detects scoring drift, and recommends model updates when your market shifts.
Selective CRM creation
Scores at the org level first, creates CRM records only for users who meet criteria. Your HubSpot or Salesforce contact count reflects real pipeline.
Real-time webhook ingestion
Flexible webhooks accept any event structure from any source. No ETL pipelines, no managed connectors, no schema lock-in.
Privacy-respecting AI scoring
No PII is sent to LLM providers. Leads are referenced by internal IDs with anonymized data and company names only.
Common questions
Plugs into your existing stack
Webhooks accept any event structure. Native integrations for the CRMs, CDPs, and product analytics tools you already use.
See who in your pipeline is actually ready
Start free. Connect your signal sources. Watch scores land in your CRM with reasoning your reps trust. No sales call required to evaluate.
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