what Unstuck Engine does

Most B2B tools give you single-dimension scoring. "This lead is qualified" or "this lead scored 73/100." Then you're supposed to figure out what that means and what to do about it.

That's not how buying works.

Buying happens across 3 dimensions simultaneously: company fit, persona match, and engagement level. A perfect-fit company with zero engagement isn't ready. A highly engaged person at a terrible-fit company won't close. A decision maker who's just browsing shouldn't get a sales call.

Single-dimension scoring forces you to guess which dimension matters most. We don't make you guess.

Before we explain the philosophy, just watch it work:

What you'll see is the product doing exactly what we're about to explain: taking messy, multi-source data and turning it into clear prioritization across company fit, persona match, and engagement timing.

Now let's talk about why we built it this way.

Here's what happens when you collapse 3 dimensions into 1 score:

You import 10,000 contacts from Sales Navigator. Your scoring tool gives you a ranked list: 87/100, 85/100, 82/100, 79/100...

You call the top 50. Some conversations go nowhere. Some people say "not interested." Some say "call back in 6 months." A few actually book meetings.

You have no idea why. The score told you they were qualified. But qualified for what? Are they the right company? The right person? The right timing?

You're flying blind with a single number.

We started Unstuck Engine because we kept seeing this pattern. Companies invest in lead scoring, get a ranked list, then still waste time on bad-fit leads or miss opportunities because they can't tell the difference between "high score because perfect fit but cold" and "high score because terrible fit but very engaged."

The solution isn't better single-dimension scoring. It's separate scoring for each dimension, then intelligent combination.

In who we build for, we explained that you can run up to 8 ICPs and up to 8 Personas simultaneously. What we didn't explain is what happens when those definitions meet real data.

Here's the flow:

Step 1: Someone enters your system - imported from Apollo, engaged with your LinkedIn post, visited your website, attended your webinar, whatever. They become a Record.

Step 2: The system immediately scores them against ALL of your ICPs. Not just "do they fit?" but "which ICP do they fit best?" A company might score 87/100 for your Enterprise ABM ICP and 23/100 for your PLG ICP. That tells you which playbook to run.

Step 3: The system scores them against ALL of your Personas. Champion, Decision Maker, Buying Committee member, Blocker. Same person, different companies might put them in different persona buckets based on their title, seniority, and function.

Step 4: The system tracks their engagement across every source - website visits, LinkedIn activity, email responses, event attendance, competitive research signals, intent data from third parties. All of it aggregates into a single Engagement Stage.

Now you have 3 independent scores that work together:

Miss any dimension and you either waste time or miss opportunity.

Most tools track engagement at the account level only. "This company is hot." But that's not enough precision.

We track engagement at 2 levels simultaneously - person and account - across 5 stages.

Someone is Hot when they've engaged directly with you multiple times - visited your website twice, downloaded a whitepaper, watched a demo. These are First-Party Signals showing active interest.

Someone is Warm when they've engaged with you once. Maybe they visited your pricing page, or responded to an email, or connected with you on LinkedIn. They're aware of you.

Someone is In-Market when external signals show they're researching solutions like yours - even if they haven't engaged with you directly yet. Engaging with competitor content, hiring for roles that suggest need, attending industry events. These are Second-Party and Third-Party Signals.

Someone is Cool when there's minimal signal - maybe one external indicator, but nothing strong.

Someone is Cold when they exist in your database but show no engagement signals at all. You imported them from Sales Navigator or got their contact from a database, but they haven't done anything yet. We call these Zero-Intent Signals - they're in your Total Addressable Market, but not showing intent.

The same 5 stages apply, but aggregated across all people at the account.

A company is Hot when multiple people are showing First-Party Signals. This is buying committee activation - not just one person browsing, but several stakeholders engaging simultaneously.

A company is Warm when at least one person has engaged directly with you, but the buying committee isn't fully activated yet.

A company is In-Market when multiple people are showing Second-Party or Third-Party Signals - they're researching, they're hiring, they're changing tech stack, but they haven't engaged with you specifically.

A company is Cool when there's minimal signal - one person showing light activity.

A company is Cold when you've imported the account but no engagement signals exist yet.

Why person AND account level? Because a single highly engaged person at a cold account tells you something very different than a cold person at a hot account.

If a Decision Maker is Hot individually but their company is Cool at the account level, that means they're personally interested but haven't activated their buying committee yet. You can reach out to them, but don't expect to close quickly.

If that same Decision Maker is Cold individually but their company is Hot at the account level, that means other people in the company are engaged - maybe their team is researching without telling them yet. You need to find those engaged people first, build champions, then get to the Decision Maker.

Both scenarios need different plays. Single-level tracking can't tell you which is which.

Individual signals roll up automatically. When one person at a company visits your pricing page, that's 1 signal for them, 1 signal for the account. When a second person downloads your whitepaper the next day, that's 2 total signals for the account. When you have 12 people showing engagement across different sources - website visits, LinkedIn activity, webinar attendance, email responses - that's not 12 separate leads. That's 1 account with an activated buying committee.

We monitor signals across 4 categories: First-Party, Second-Party, Third-Party, and Zero-Intent.

Most tools focus on one or two. We track all 4 because each solves a different blind spot.

First-Party Signals are direct engagement with your company. Website visits, content downloads, demo views, email responses, community participation, social media follows. This tells you they're aware of you specifically.

But First-Party alone isn't enough. Someone might be in-market for your category and actively researching competitors, but they haven't found you yet. If you only track First-Party, you miss them.

Second-Party Signals are observable market activity. Social media engagement on industry topics, job changes, hiring patterns, technology installations. This tells you they're in-market even if they haven't engaged with you.

But Second-Party alone isn't enough either. It's noisy. Someone engaging with competitor content might not be a decision maker. Someone hiring might not have budget yet.

Third-Party Signals are purchased intent data from providers like Bombora, G2, 6sense. They aggregate buying signals across multiple sources and tell you when someone is actively researching your category.

But Third-Party data is expensive and often not specific enough. It tells you the account is researching "marketing automation" but doesn't tell you if they're a fit for YOUR product or which persona at the company is driving the research.

Zero-Intent Signals are contacts that exist in your Total Addressable Market but show no engagement yet. You imported them from Apollo, you got their LinkedIn from Sales Navigator, you found them in a database. They're Cold right now, but they're trackable. When they move to Cool, Warm, or Hot, you want to know immediately.

All 4 types together give you complete visibility. First-Party tells you awareness. Second-Party and Third-Party tell you in-market timing. Zero-Intent gives you TAM coverage so you don't miss people when they activate.

We aggregate all 4 types, weight them appropriately, and calculate Engagement Stage from the combination. That's how you get precision.

For the detailed breakdown of every signal type we track, see our Help Center guide on Understanding Signals. But the philosophy is simple: track everything, weight intelligently, surface what matters.

Here's a philosophical choice we made early: we're not trying to be your CRM, your automation tool, your ad platform, or your outbound sequencer.

We're the intelligence layer. You already have tools you like. We make them smarter.

That's why we built webhooks first, not native integrations.

Webhooks let you push data in real-time to any system that accepts them - Clay, n8n, Zapier, Make, directly to CRMs, directly to databases, directly to custom internal tools. Configure once, data flows automatically every time a lead hits your criteria.

Native integrations are convenient, but they're also limiting. We can only integrate with tools we've specifically built for. Webhooks work with everything.

We also support CSV export/import for bulk operations. Import 50,000 contacts from Sales Navigator, export filtered lists to Google Sheets, whatever you need.

Will we build native integrations eventually? Yes - you can vote on our roadmap for which ones matter most. But webhooks came first because they're more flexible, and flexibility matters more than convenience when you're the intelligence layer, not the execution layer.

If you want the technical details on configuring webhooks, see our Help Center guide. But the philosophy is: we don't lock you into our interface. We give you intelligence, you route it wherever you work.

You might notice Unstuck Engine has only 5 tabs: Dashboard, ICPs & Personas, Signals, Records, and Audiences(coming soon).

That's intentional.

We could have built 47 features with 19 nested menus and configuration screens for every edge case. We didn't.

Why? Because complexity kills adoption. If a Product Champion can't get value in 15 minutes without reading a manual, they won't bring it to their VP. If a VP can't see ROI in a 5-minute demo, they won't buy.

Our philosophy is: the product should be simple to use because we've done the hard work in the background.

Multi-dimensional ICP scoring across dozens of attributes? Hard to build, easy to use - just define your criteria, system handles the math.

Aggregating 20+ signal sources into unified engagement stages? Hard to build, easy to use - turn on the sources you want, we handle aggregation.

Routing different ICPs to different plays? Hard to build, easy to use - set filters, configure webhook, done.

We keep the interface simple by making the engine sophisticated. That's the trade-off.

And when someone does need to go deep - "how exactly does this signal work?" or "what are all the fields in the ICP scoring model?" - we have a comprehensive Help Center with step-by-step guides for every feature.

You can explore the full Help Center at learn.unstuckengine.com, or just ask Scout AI - our AI assistant trained on the entire Help Center - directly in the product. Scout can answer any question about how something works, link you to the right guide, or help troubleshoot issues.

Simple product. Deep documentation. That's the balance.

Right now, you can filter Records by ICP, Persona, Engagement Stage, Signal count, date ranges, sources - any combination you want. You can export those filtered lists via CSV or webhook.

That works. But it doesn't scale.

Here's the problem: if you're running 5 different ICPs and 4 different Personas across 3 Engagement Stages, you have 60 possible combinations. Each combination might need a different play - different messaging, different channel, different timing.

Rebuilding those filters every time you want to export a list is tedious. You end up running fewer plays than you should because configuration friction is too high.

Audiences solve this.

An Audience is a saved segment that updates automatically. "ENERGY ICP + DECISION persona + Hot stage" becomes an Audience. "BIO1 ICP + Champion persona + Warm stage" becomes another Audience. You define them once, they populate in real-time.

But here's where it gets powerful: Audiences won't just be filtered lists. They'll have native integrations.

Push this Audience to a LinkedIn ad campaign. Push that Audience to a Clay table. Push another Audience to Salesforce as a campaign. All automated. All real-time.

At that point, Unstuck Engine becomes your GTM mission control center. You're not exporting CSVs and manually uploading them to 6 different tools. You're defining strategic segments - "here's who we target for ABM, here's who gets nurtured, here's who sees ads" - and the system routes everyone to the right play automatically.

That's the vision. We're not there yet. But Audiences launch soon, and native integrations follow. When that happens, the product evolves from "intelligence layer you export from" to "orchestration center that runs your entire GTM engine."

We could have built a point solution. "Best ICP scoring tool" or "Best intent data aggregator" or "Best lead routing system."

We didn't, because point solutions don't solve the real problem.

The real problem is that GTM teams are drowning in tools that each solve one piece of the puzzle, and no tool connects the pieces. You have Apollo for data, Clay for enrichment, 6sense for intent, Salesforce for CRM, Outreach for sequencing, LinkedIn for ads. Each tool has its own scoring logic, its own definitions of "qualified," its own UI to learn.

So you spend more time moving data between tools than actually talking to customers.

We're solving the system problem, not the point problem. The system problem is: How do you turn messy multi-source data into clear prioritization, then route that prioritization to the right plays, without manual work?

That requires 3-dimensional scoring (not just ICP, not just signals, but both plus personas). It requires real-time aggregation (person-level and account-level simultaneously). It requires flexible routing (webhooks + native integrations, not just CSV export). It requires simple UI despite complex logic (so champions can adopt without training).

Point solutions can't do this. You need a system.

That's what we're building. Not "the best lead scoring tool." The GTM intelligence and orchestration layer that makes your entire stack smarter.

In why do we exist, we said our mission is: Build a world where every GTM interaction matters to both sides.

Here's how this product delivers on that mission:

For sellers: You're not wasting time on low-fit leads (ICP scoring filters them out), wrong personas (Persona scoring identifies the right people), or bad timing (Engagement Stages tell you when they're ready). Every conversation you have is with someone qualified, in the right role, showing intent. Your time matters.

For buyers: You're not getting spammed when you're Cold (we route you to nurture, not outreach), pitched before you're ready (Warm and In-Market get education, not sales calls), or contacted by irrelevant vendors (low ICP fit never makes it to outreach). When someone does reach out, it's because you match their ICP, you're in the right role, and you've shown signals that you're researching. Your attention matters.

3-dimensional scoring + real-time routing + intelligent orchestration = both sides win.

That's the philosophy. That's what Unstuck Engine does.

Next: Our culture - How Overeducate Not Oversell, Fail Fast, and other principles shape the company that builds this product.

Deep dive: How we work - Product-Led Growth Sales-Assisted, remote-first, Bounty → Standard workflow.

Try it now: [Interactive Demo](Arcade link) | Help Center | Ask Scout AI