How Trailspark turns signals into account evaluations
Connect the tools you already run and describe what a good account looks like. Trailspark resolves the evidence into one account story, evaluates it against your definition of good, explains why, and sends the result back to your stack.
From signal to action.
Product activity, marketing engagement, CRM history, warehouse data, and web behavior resolve into one account story. Trailspark evaluates that story under the lens that applies and writes the result where your team already works.
See what is happening
Your evidence, wherever it lives
- Product analytics
- Marketing tools, via webhook
- Website & forms
- CRM
- Data warehouse
Product activity arrives at full fidelity
- app.created · 09:41
- log_source.connected · 11:06
- alerting.configured · Day 3
Named events, configuration milestones, and usage breadth over time
Webhooks, Segment, or scheduled SQL from BigQuery
Know which account the activity belongs to
- MKTGPriya Nair · webinars, pricing pages
- PRODUCTSam Cole · SSO, integrations, daily usage
- JOINEDdana@brightwave.com · matched by domain, no form ever filled
Lead-to-account matching by email domain, or one shared record across CRM and product
Know what the evidence means here
A champion on pricing while two engineers evaluate. That is the account you described as worth pursuing.
The whole account story in one pass, under your definition of good, re-evaluated when the evidence meaningfully changes
Put the result to work
Where your team already works
- HubSpot / Salesforcepropensity, reason, evaluated lens & roles on the account · workflows trigger on the fields
- Slackalerts routed per ICP to the channel you choose
- Segmentlead evaluations as track events or identify traits
What happens at each step
What Trailspark does with your data, and the part your team owns, from the first event to the field your reps read.
Step 1 · Ingest
Signals in, from the stack you already run
Product events from your backend, Segment, or Heap, marketing activity by webhook, accounts and deals from your CRM, hourly or daily BigQuery syncs, and behavior on your website all arrive as signals.
What you do: Connect your CRM and send the events you already track, as any JSON. You map them to signals in Trailspark.
Step 2 · Resolve
One account story, in order
People from your product, CRM, and marketing tools are matched to one account by CRM account, product workspace, or company email domain, even when no other system joins them. Their activity lands on one account timeline, and anonymous website activity attaches once the visitor is identified.
What you do: Nothing to build. Open any account in Trailspark to read the story it resolved.
Step 3 · Evaluate
The lens that applies, and the reason
Each account is assigned the lens that applies to it and evaluated against that definition of good. Conflicting evidence lowers confidence, disqualifying criteria you wrote outweigh positive signals, and every propensity ships with a written reason.
What you do: Describe what good looks like for each motion you sell, or start from a SparkSense draft built from your won deals.
Step 4 · Activate
The result, where your team already works
Propensity, the reason, the evaluated lens, and buying-group coverage are written to your CRM, alerts go to Slack, and lead evaluations go to Segment. When the evidence changes, the account is evaluated again and the fields update.
What you do: Map the fields and choose the channels. The workflows you already run trigger on them.
Describe what good looks like.
An evaluation lens in Trailspark is a written description of the account you want and the behavior that shows it. You write it in the language you already use in your planning docs. Trailspark applies it to the account story every time it evaluates.
SparkSense · ICP setup
Connecting to your CRM...
What a lens actually says
Four definitions a team might write. Nothing here is a weight or a threshold.
“A trial worth pursuing: someone technical wiring the product into their stack in the first two weeks, and at least one more person from the same company showing up. Mid-market B2B SaaS, roughly 100 to 1,000 employees.”
“Room to grow: usage spreading past the team that bought, new workspaces or environments appearing, and someone running into the limits of their current plan.”
“A renewal I do not have to worry about: steady weekly usage across more than a couple of people, and no drop-off in the two months before the date.”
“Enterprise is a different bar. A hundred active users at a 15,000-person company is a foothold, so I want a second business unit involved before we call it adoption.”
No points to tune
The business judgment stays in business language instead of being translated into a point system somebody has to maintain. Each lens still carries an eligibility rule that decides which accounts it applies to, and priority rules settle which definition an account is evaluated under when more than one fits.
How to set eligibility rules on docs.trailspark.ai (opens in a new tab)As many as you sell
Add a definition for every motion and segment you treat differently: expansion and renewal alongside acquisition, trial users separately from free-plan users, enterprise separately from SMB.
How to manage multiple ICPs on docs.trailspark.ai (opens in a new tab)Start from what you already won
SparkSense can draft a first lens from your closed-won deals, mapping the company traits and buying roles behind them. You review and edit it before anything is evaluated against it. Or write your own from scratch.
How to draft with SparkSense on docs.trailspark.ai (opens in a new tab)Keep the business judgment in business language. Trailspark uses the definition you wrote every time it evaluates the account.
You own the judgment. Trailspark delivers the story.
The model sees a prepared account story. Everything around the model call is the part a team would otherwise build and maintain itself.
You decide
- Which sources to connect, and which events matter
- What good looks like for each motion, in your own words
- Which accounts each lens covers, and which lens wins when more than one fits
- Which CRM fields, Slack channels, and Segment traits get the result
- Where a lead evaluation got it wrong, with a written correction
Trailspark handles
- Matching people across product, marketing, and CRM to one account
- Ordering the evidence into a timeline the model can read consistently
- Assigning each account the lens that applies
- Deciding when an account's evidence has changed enough to evaluate again
- Writing the result back, with the reason attached
Each step, in more detail
AI lead scoring
How the evaluation reads the evidence
Why a point model flattens meaning, and what a reasoned evaluation returns instead.
PQL scoring
Product activity, read as part of the account
Every product user evaluated with the account they belong to, not against a threshold.
Buying Group Intelligence
The people behind the account
Which buying roles are engaged, which are latent, and which are missing.
Integrations
What connects, and what gets written back
CRM, product data, marketing, CDP, enrichment, and notifications, with a setup guide for each.
How Trailspark works: common questions
Evaluate every account for what comes next.
The Free plan is the full platform on a small set of accounts. Connect the signals you already collect, describe what good looks like, and read the reasoning behind every result in your CRM.
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