Direct answer: filter for fit first using firmographic and technographic criteria, then layer intent signals on top to time outreach, then combine both into one weighted priority score. This is for Sales, Growth, and RevOps teams running outbound. Skip fit and intent signals get wasted on accounts that can never buy. Skip intent and reps waste time on accounts that fit but aren't ready yet.

Key Facts and Benchmarks at a Glance

Methodology and limitations

‍This guide draws on Unify's own product mechanics for filtering and scoring, plus four named customer outcomes (Perplexity, Anrok, Abacum, Navattic), each cited individually below with its own case study link and publication date. There is no single blended "Unify benchmark" number; every dollar figure here traces to one customer's published results, not an average across customers. Results vary by ICP size, how many intent sources are already connected, and whether the motion is sales-led or product-led. This guide does not cover phone or dialer-based intent capture, offline event signals, or account scoring for pure inbound lead qualification.

Why Do Fit and Intent Both Matter for Targeting?

Fit tells you which accounts can ever become customers. Intent tells you which of those accounts are close to buying right now. You need both because only a small slice of your market is actually shopping at any given moment.

Research from the Ehrenberg-Bass Institute for Marketing Science, credited to Professor John Dawes and often called "The 95:5 Rule," puts that slice at roughly 5% of B2B buyers in-market at any point in time. Firmographic fit narrows a huge list to the accounts worth watching; intent tells you which of those just entered the 5%.

Intent scoring without a fit filter floods your queue with companies researching your category that could never buy from you. Fit filtering without intent works a qualified list in the wrong order: reps call accounts that are months from a decision while a genuinely ready account sits untouched.

How Do You Set Your Firmographic Filters First?

Set 4 to 6 firmographic and technographic filters pulled from your closed-won accounts, not your aspirational TAM. The filters define the fit gate every account has to clear before intent data is even considered.

Start with the fields that actually correlate with your closed-won deals over the last two to four quarters:

• Company size: employee count or revenue band where your win rate is highest, not your broadest addressable range
• Industry and vertical: the two or three verticals that make up most of your existing base, plus any adjacent vertical you're actively expanding into
• Geography: regions where you have sales coverage, support, and (if relevant) compliance to sell
• Technographic fit: tools already in the account's stack that indicate compatibility or a specific pain point (a CRM, a competing point solution, a stack gap)
• Hard exclusions: existing customers, active competitors, and accounts already in a live sales cycle

A filter is only as good as the company data behind it. Unify's B2B Company & Contact Data product indexes more than 1.1B contacts and 65M companies across 40+ signal and intent sources, so the fields used to build the filter stay current instead of decaying in a static list.

How Do You Layer In Intent Signals for Timing?

Once an account clears the fit filter, layer 2 to 4 weighted intent signals on top to determine timing. These tell you which fit-qualified accounts are showing buying behavior this week, not just which ones match your ICP on paper.

Not all signals carry equal weight. First-party signals, meaning behavior on your own website or inside your own product, tend to be the strongest predictors. Per Unify's Outbound Sweet Spot guide, product usage signals drive a 9.1% positive reply rate, the highest of any signal type Unify tracks.

Common signal categories worth layering onto a fit-qualified list:

• Website intent: pricing page visits, repeat visits to product pages, content downloads
• Product usage (for PLG motions): free-trial activity, seat growth, feature adoption, or hitting a paywall
• People signals: a new hire in a decision-making role, or a former champion moving to a new company
• Third-party research: competitor G2 page visits, technology install or uninstall events, funding announcements

For a longer list of triggers that correlate with buying readiness, see Unify's guide on what signals tell you a company is ready to buy. Unify's Signals & Intent product consolidates 40+ of these sources into one feed; per Unify's product page, signal-driven outbound built this way gets replied to 73% more often than cold outbound.

How Do You Combine Fit and Intent Into One Priority Score?

Multiply fit and intent instead of adding them. Fit should function as a gate (an account either passes or it doesn't), and intent should function as a decaying score layered on top of every account that passes the gate.

A simple version of the formula looks like this:

• Fit Gate = 1 if the account clears your firmographic and technographic filters, 0 if it doesn't. Accounts scoring 0 never enter the ranked list, regardless of intent.
• Intent Score = a weighted sum of active signals (first-party signals weighted highest, third-party signals weighted lower).
• Recency Decay = the intent score shrinks the longer it's been since the signal fired, so a pricing-page visit from yesterday outranks one from six weeks ago.
• Priority Score = Fit Gate × Intent Score × Recency Decay.

This produces one ranked list instead of two disconnected reports (a firmographic list from your data provider and a separate intent report from a signal tool). For the fuller formula, including specific component weights and decay windows, see Unify's guide to composite account scoring for signal-led outbound.

Worked Example: Sales-Led Account Moving Through the Score

A mid-market fintech SaaS company sets its fit filter at 200 to 1,000 employees, financial services vertical, and use of a specific payments stack. Account X clears the filter and enters the pool with a base intent score of zero.

Fourteen days later, Account X's VP of Sales views the pricing page; three days after that, the company posts a job listing for a Head of RevOps. Both are first-party and people signals fired inside the same window, so the score jumps into the top decile and the account routes to the owning rep as a real-time alert instead of an automated sequence. The rep books a meeting five days later.

Worked Example: PLG Motion Filtering Signups Into Enterprise Pipeline

Per the Perplexity case study, the team faced the opposite problem: heavy self-serve signup volume, most of which never justified a sales touch. The firmographic filter narrowed the pool to enterprise-scale employee counts, and intent scoring on top of that tracked usage patterns like query volume and seat growth.

That combination fed a PQL Play that, per the case study, hit a 5% reply rate on its own and contributed to $1.7M in pipeline generated in three months, without a dedicated BDR team. Jenny Sung, Product Marketing Lead at Perplexity: "Unify drives pipeline directly into our sales team's inbox. The platform's intent triggers and touch points give us the opportunity to talk to prospects at the right time." As we've covered before, your warmest leads are often already using your product, exactly the signal a firmographic-only filter misses.

30-Second Chooser: How Should You Weight Fit vs. Intent?

• If you're PLG with high signup volume and thin BDR bandwidth, weight product-usage intent heaviest and keep the fit filter loose (2 to 3 criteria) so you don't discard real signups.
• If you're sales-led or running ABM against a fixed named-account list, weight fit heavily and tight, and use intent only to sequence within that list, not to expand it.
• If you have fewer than three intent sources connected today, start with website intent plus one first-party product or CRM signal before adding third-party paid intent data.
• If your CRM firmographic fields are messy or stale, fix those first. A dirty fit filter poisons every intent score layered on top of it.
• If reply rates stay flat despite a large "high-intent" volume, your fit filter is too loose. Tighten firmographic criteria before adding more signal sources.
• If you're running expansion or customer marketing, swap new-logo fit criteria for install-base criteria (seat count, usage tier) before layering renewal or expansion intent on top.

What Are the Most Common Mistakes When Combining Fit and Intent?

The two failure modes waste the same resource: rep time. Chasing intent without fit fills the queue with accounts that were never going to buy; chasing fit without intent works a qualified list in the wrong order.

Top pitfalls to avoid:

• Treating every third-party intent hit as sales-ready without checking it against a firmographic filter first.
• Letting firmographic filters go stale for a quarter or more while sales priorities and ICP shift underneath them.
• Scoring intent signals as a flat sum instead of decaying them, so a six-week-old signal counts the same as one from yesterday.
• Routing every fit-qualified, high-intent account to an automated sequence instead of escalating multi-signal accounts to a rep in real time.
• Running fit and intent data in two disconnected tools, so reps see two separate reports instead of one ranked list.

How Do You Evaluate a Platform for Combining Fit and Intent?

Score any platform, including a manual spreadsheet process, against the same criteria regardless of vendor.

• Signal breadth and freshness. How many first-party and third-party signal types it ingests and how often they refresh. A platform with two stale signal types produces a shallow score no matter how it's weighted. Test: ask for the connected source list and refresh cadence. Red flag: signals that refresh monthly or slower.
• Firmographic and technographic coverage. The breadth and accuracy of company attributes available to build the fit filter. Filters built on thin or outdated data produce false positives before intent is even applied. Test: spot-check 20 known closed-won and closed-lost accounts. Red flag: over 10% missing core fields.
• Scoring transparency and decay logic. Whether the platform shows its work on how fit and intent combine, and whether scores decay over time. Test: ask the vendor to walk through one live account's score. Red flag: they can't explain the weighting behind their own score.

How Unify covers this. Unify combines firmographic and technographic filtering with signal scoring inside a single Audiences workflow, so the fit gate and the intent score live in one place instead of two disconnected tools. Its Signals & Intent library pulls from 40+ data and signal vendors, and its B2B Company & Contact Data layer keeps the firmographic side current across 1.1B+ contacts and 65M+ companies. Per the Abacum case study, the team had previously pulled intent data manually across five separate tools before consolidating into Unify; afterward, Abacum cut manual contact-pulling time by 75% and generated $250,000 in pipeline in under two hours of setup. Unify's Plays then route the combined, ranked list automatically, either into a sequence or as a real-time alert to the owning rep. Unify is outbound AI for sellers: reps prompt for the account list they want, and agents apply the fit filter and intent score before a single email goes out. For a tool-by-tool comparison, see Unify's roundup of the best AI tools to turn buyer signals into outreach.

Sign up for Unify to filter for fit and layer intent signals from one Audiences workflow instead of stitching together a data provider and a separate signal tool.

How Does This Change by Role, Motion, or Team Size?

• Sales / BDR: lean on the fit gate to keep your named-account list honest, and treat any multi-signal account as an escalation, not a sequence step.
• Growth / Marketing: use intent to decide sequencing and message angle within an already fit-filtered audience, not to decide who enters the audience.
• RevOps: own the decay window and the weighting between signal types; this is the lever most likely to need quarterly tuning as the business changes.
• PLG motion: weight product usage intent heaviest and keep fit filters loose enough that a real signup from a smaller-than-typical account doesn't get discarded.
• Sales-led / ABM motion: keep fit tight and fixed around a named list, and use intent purely to time and sequence outreach within it.
• Enterprise vs. SMB: enterprise accounts justify a lower intent threshold before a rep gets involved, since the cost of a missed signal is higher; SMB accounts can stay in automated sequences longer before escalating.

What Edge Cases Should You Watch For?

• Job-seeker traffic vs. buyer intent: a spike in careers-page visits is a hiring signal, not a buying signal; don't let it inflate an account's intent score.
• Funding announcements without a role match: a funding round is only a material trigger if it maps to a budget or team change relevant to what you sell, not every funding event on a fit-qualified account.
• Existing customers showing new "buying" intent: route this to customer success or account management as an expansion signal, not into the new-logo outbound queue.
• Bot or crawler traffic: the same account triggering three or more signals within an hour is more often a scraper or a monitoring tool than a real buyer; verify before scoring.
• Opens-only email engagement: an email open alone is a weak signal and shouldn't be weighted the same as a pricing-page visit or a reply.

When Should You Stop or Adapt a Play?

Frequently Asked Questions

Should I filter on fit or intent first?

Filter on fit first. Firmographic and technographic filters set the boundary of accounts that can ever become customers, so intent signals only matter once they land inside that boundary. Running intent scoring before fit filtering means you spend time chasing in-market accounts that are the wrong size, industry, or geography and will never close.

What if a high-intent account is a poor fit?

Treat fit as a hard gate, not a tiebreaker. If an account fails two or more of your core firmographic filters, exclude it even if it shows strong intent signals. High intent on a bad-fit account is usually research noise, a vendor, a student, or a competitor, not a buyer.

How much intent is enough to act on?

One first-party signal inside a 14-day window is usually enough to trigger outreach on a fit-qualified account. Two or more signals stacking in that window should escalate the account to a real-time alert for a rep rather than an automated sequence. Lower-confidence third-party signals are better used to add weight to a score than to trigger outreach alone.

Can I automate the combined fit and intent scoring?

Yes. Most teams automate this as a standing firmographic filter plus a rules engine that applies weighted, decaying intent scores to whichever accounts pass it. Unify runs this as Audiences layered with Signals, so the ranked list updates automatically instead of being rebuilt in a spreadsheet.

What data do I need to start combining fit and intent?

You need a firmographic and technographic data source, at least one first-party intent source such as website or product usage, and a way to write both back to your CRM as one ranked list. Start with closed-won accounts to define the firmographic filter before adding any intent signal.

What is the difference between intent data and firmographic data?

Firmographic data describes what a company is: employee count, revenue, industry, location. Intent data describes what a company is doing right now, such as visiting your pricing page or hiring for a role your product supports. Firmographic data answers whether an account can buy; intent data answers whether it is close to buying.

How often should firmographic filters be refreshed?

Review the filter criteria quarterly, or immediately after a pricing change, a new product launch, or a shift in your closed-won mix. The underlying firmographic data itself should refresh continuously, at least monthly, since stale records are the most common cause of accounts sitting in the wrong tier.

Does combining fit and intent work for ABM as well as PLG motions?

Yes, but the weighting flips. In an ABM or sales-led motion, keep fit filters tight and fixed around a named-account list, using intent only to sequence within it. In a PLG motion, keep fit filters loose enough not to discard real signups, and let product usage intent carry most of the weight.

Glossary

• Firmographic data: company-level attributes such as employee count, revenue, industry, and location used to define who can fit your ICP.
• Technographic data: data on the specific software and tools an account already uses, used to refine fit beyond basic firmographics.
• Intent data: behavioral data showing that an account is actively researching or engaging with a category or product, split into first-party (your own site or product) and third-party (external research activity) sources.
• Fit gate: a pass/fail firmographic and technographic filter an account must clear before any intent score is applied.
• Composite account score: a single ranked score combining fit, intent, and recency into one number used to prioritize outbound.
• Signal decay: the reduction in an intent score's weight as time passes since the signal fired, so recent activity outranks older activity.
• PQL (product-qualified lead): a signup or free-trial account whose in-product usage indicates buying intent, common in PLG motions.
• ICP (ideal customer profile): the defined firmographic and technographic profile of accounts most likely to become successful customers.
• Waterfall enrichment: a process of checking multiple data vendors in sequence to fill in missing contact or firmographic fields.

Sources

• Ehrenberg-Bass Institute for Marketing Science, John Dawes, "The 95:5 Rule: Why B2B Growth Starts Long Before the Purchase": marketingscience.info
• Unify, Signals & Intent product page: unifygtm.com/products/signals
• Unify, B2B Company & Contact Data product page: unifygtm.com/product/b2b-company-contact-data
• Unify, Plays product page: unifygtm.com/product/plays
• Unify, The Outbound Sweet Spot guide: unifygtm.com/resources/the-outbound-sweet-spot
• Unify, Perplexity customer story: unifygtm.com/customers/perplexity
• Unify, Anrok customer story: unifygtm.com/customers/anrok
• Unify, Abacum customer story: unifygtm.com/customers/abacum
• Unify, Navattic customer story: unifygtm.com/customers/navattic
• Unify, Composite Account Scoring for Signal-Led Outbound: unifygtm.com/explore/composite-account-scoring-signal-led-outbound
• Unify, What Signals Tell You a Company Is Ready to Buy: unifygtm.com/explore/signals-company-ready-to-buy
• Unify, Best AI Tools to Turn Buyer Signals Into Outreach: unifygtm.com/explore/best-ai-tools-buyer-signals-to-outreach
• Unify, Your Warmest Leads Are Already Using Your Product: unifygtm.com/blog/your-warmest-leads-are-already-using-your-product

Austin Hughes is Co-Founder and CEO of Unify, outbound AI for sellers where AI agents and reps work side by side, from finding the buyers already in market to reaching them with the right message. Before founding Unify, Austin led the growth team at Ramp, scaling it from 1 to 25+ people and building a product-led, experiment-driven GTM motion. Prior to Ramp, he worked at SoftBank Investment Advisers and Centerview Partners.