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.
Scoring that changes what your team does next
Not the same signals with AI on top. Better information to act on. Here's the design that gets you there.
Legacy lead scoring · counts actions
- Downloaded the "Architecting for scale" guide+10
- Attended the "Scaling event pipelines" webinar+15
- Viewed the pricing page 6 times+25
Routed to a rep as a lone contact.
Never sees her product usage, the engineer in the trial, or that no Economic Buyer has surfaced.
With Trailspark · reads the whole timeline
One account. Marketing and product activity, in sequence.
- Jun 22Priya Nair read the "Architecting for scale" guidemktg
- Jun 23Priya Nair attended the "Scaling event pipelines" webinarmktg
- Jun 24Priya Nair viewed the pricing page 6 timesmktg
- Jun 25Priya Nair heaviest product usage on the account, active 6 of 7 daysproduct
- Jun 27Sam Cole configured SSOproduct
- Jun 28Sam Cole connected the integrationproduct
resolves into one buying group
3 of 4 roles identified · 2 engaged · Economic Buyer missing · 86% confidence
- EngagedChampionPriya Nair · VP Platform
- EngagedTechnical EvaluatorSam Cole · Staff Engineer
- IdentifiedDecision MakerJ. Chen · VP Engineering · in CRM
- NeededEconomic Buyerno one on the committee yet
An exec sponsor engaging your scaling and pricing content while an engineer runs a live technical evaluation. Read together, this is an account with real propensity to buy.
High propensity, missing a role. Trailspark writes the gap to your CRM so sales and marketing can launch a play to bring Finance into the deal.
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 scoring reads different inputs entirely
6sense and Demandbase score account firmographics plus modeled third-party intent: who a company is, and research signals observed outside your properties. That tells you who might be a buyer somewhere. It can't tell you which people at the account are doing the things your closed-won buyers did.
How Trailspark evaluates an account
Four steps from raw signal to an evaluated account with reasoning. No black box.
Ingest signals from product, marketing, CRM, and warehouse
Flexible webhooks accept any event structure. Route product events from Segment, RudderStack, Amplitude, Mixpanel, or direct from your backend, or sync straight from BigQuery on a schedule with SQL you already trust. 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. You describe your ICP in plain language, and SparkSense can draft it from your closed-won deals so you start from real data instead of a blank page. 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. When a score is wrong, your team submits a written correction, and model refinement folds those corrections into the next model version you review and apply.
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 drafting
Drafts your acquisition ICP from closed-won deals: the company traits, titles, and buying roles that show up in deals you've won. You keep what fits and edit the rest.
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, and the model sees anonymized behavioral and firmographic context rather than names or emails.
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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