Beyond Enrichment: Why Buying Signals Are the Next Frontier for RevOps

Sixty-seven percent of the buyer's journey is complete before a prospect makes first contact with sales. That number, originally from Forrester and subsequently validated by Gartner and SiriusDecisions across multiple research cycles, has been cited so frequently that it has lost its operational impact. But consider what it actually means for your revenue engine: by the time a lead fills out a form or responds to an outbound sequence, they have already evaluated your category, compared your competitors, read third-party reviews, and formed an opinion. They are not beginning their journey. They are more than halfway through it.

The implication for RevOps is direct: the most valuable moment to engage a prospect is not when they raise their hand. It is during the 67% of the journey that happens before the hand goes up. And the only way to detect that pre-hand-raise activity is through buying signals.

This is why buying signals — not enrichment, not lead scoring, not workflow automation — represent the next frontier for RevOps teams that have already built a foundation of clean data. Enrichment tells you who someone is. Buying signals tell you when they are ready to buy. That distinction changes everything about how you prioritize, route, and engage your pipeline.

The Enrichment vs. Signals Distinction

Enrichment and buying signals solve fundamentally different problems. Conflating them — which most RevOps teams do, because most vendors blur the line — leads to strategies that are well-executed but pointed in the wrong direction.

Enrichment answers WHO. When you enrich a contact record, you are filling in identity and firmographic data: job title, company name, employee count, industry, revenue range, technologies used, geographic location. This data is static in nature — it changes infrequently and describes the contact as they are, not what they are doing. Enrichment is about building a complete profile.

Buying signals answer WHEN. A buying signal is a behavioral indicator that suggests a contact or account is actively in a purchase evaluation cycle. This data is dynamic — it changes daily or hourly and describes what the contact is doing right now, not who they are. Buying signals are about detecting timing.

The difference matters because the right contact at the wrong time is not a qualified lead. A VP of Marketing at a 500-person SaaS company in your ICP is a perfect firmographic match. But if they signed a three-year contract with your competitor six months ago, no amount of enrichment or lead scoring will make them a viable opportunity. Conversely, a Director-level contact at a slightly-below-ICP company who is actively researching your category on G2, visiting your pricing page, and downloading your competitor's case studies is a far better use of sales capacity — but only if you can detect those signals.

The Enrichment Maturity Curve

Most RevOps teams do not jump straight from no enrichment to signal-aware operations. There is a maturity curve, and understanding where you sit on it determines what your next investment should be.

Level 1: Manual Enrichment

At this level, enrichment is a human activity. Sales reps Google prospects before calls. Marketing coordinators manually research companies before adding them to campaign lists. An intern spends Friday afternoons looking up LinkedIn profiles to fill in job titles on imported trade show leads.

Characteristics: No enrichment tooling, no automated data enhancement, data quality depends entirely on individual effort. Coverage is spotty and concentrated on the records that individual contributors happen to touch.

Typical outcome: 20-30% field completeness on firmographic data. High quality on the records that get manual attention, but the majority of the database is untouched.

Level 2: Single-Source Enrichment

The team has adopted one enrichment provider — Clearbit, Apollo, ZoomInfo, or HubSpot's Breeze Intelligence. Records are enriched in batches or through automated workflows. This is where most HubSpot-native teams land after evaluating Breeze Intelligence.

Characteristics: One provider, consistent coverage within that provider's strengths, structural gaps in the provider's blind spots. The team has enrichment tooling but has not addressed the single-source coverage ceiling.

Typical outcome: 40-60% field completeness depending on provider and database composition. US-centric tech company databases skew toward the higher end. Globally distributed or non-tech databases skew lower. For a detailed review of where Breeze Intelligence specifically falls on this spectrum, see our honest review.

Level 3: Waterfall Enrichment

The team uses multiple enrichment providers orchestrated in sequence — a waterfall architecture where each subsequent provider fills gaps left by the previous one. This is the approach that breaks through the single-source coverage ceiling and is the current best practice for teams serious about data completeness. For a full explanation of how waterfall enrichment works and why it outperforms single-source approaches, see our waterfall enrichment guide.

Characteristics: Multiple providers, field-level orchestration, coverage compounding across sources. The team has solved the "who" problem at scale and is seeing 75-90%+ field completeness on critical fields.

Typical outcome: 75-92% field completeness on target fields. Significantly higher coverage across geographies and industries because provider blind spots are offset by other providers' strengths.

Level 4: Signal-Aware Operations

The team has layered buying signals on top of a clean, enriched database. Enrichment data tells them who each contact is. Buying signals tell them which of those contacts are actively in-market. Lead scoring incorporates both static attributes and dynamic behavioral signals. Routing logic prioritizes signal-detected accounts over firmographic-only matches.

Characteristics: Clean data foundation from Level 3, real-time buying signal ingestion from one or more signal sources, integrated scoring and routing that weighs timing alongside fit. This is the frontier.

Typical outcome: Materially higher win rates on signal-detected opportunities because sales engages prospects during active evaluation cycles rather than on arbitrary cadences. Pipeline velocity increases because the right leads are prioritized at the right time.

The critical insight about this maturity curve is that you cannot skip levels. Level 4 does not work without Level 3. Buying signals applied to a database at Level 1 or Level 2 maturity create false precision — you detect that an account is in-market, but you cannot act on that signal because you do not have a complete, accurate record to route to sales. The signal is real; your ability to respond to it is not.

Types of Buying Signals

Buying signals are not a single data type. They fall into three categories, each with different sources, different reliability levels, and different operational implications.

First-Party Signals

First-party signals come from your own properties — your website, your email campaigns, your product, your content. These are the signals you already have access to but may not be systematically capturing or acting on.

Website behavior. Which companies are visiting your pricing page? Which contacts are returning to your site repeatedly over a short window? A single visit to a blog post is not a buying signal. Five visits to your pricing page from the same company within two weeks, preceded by visits to your comparison and case study pages, is a strong signal.

Content engagement. Which contacts are downloading bottom-of-funnel content — ROI calculators, implementation guides, vendor comparison templates? Content engagement maps to funnel stage. Top-of-funnel content consumption is awareness behavior. Bottom-of-funnel content consumption is evaluation behavior.

Email engagement patterns. Not just opens and clicks — those are noisy. Look for acceleration patterns: a contact who has been dormant for six months suddenly opens three emails in a week and clicks through to two product pages. The change in velocity is the signal, not the absolute engagement level.

Product usage signals (if applicable). For product-led growth motions, usage patterns within a free tier or trial are among the strongest buying signals available. Feature activation, usage frequency increases, team member invitations, and integration connections all indicate a transition from evaluation to adoption.

Second-Party Signals

Second-party signals come from platforms where your prospects are conducting research that is visible to you through partnerships or data sharing agreements with those platforms.

Review site activity. G2, TrustRadius, Capterra, and Gartner Peer Insights all offer buyer intent programs that notify you when companies in your target account list are actively researching your category on their platforms. A company that reads three reviews of your competitors on G2 in a week is in an active evaluation cycle.

Comparison page visits. Some review platforms provide signal when a prospect views a head-to-head comparison between your product and a competitor. This is one of the highest-fidelity buying signals available because it indicates not just category research but direct competitive evaluation.

Community engagement. Activity in industry communities, forums, and professional groups related to your category can indicate buying intent, though these signals are noisier and harder to capture systematically.

Third-Party Intent Signals

Third-party signals come from data providers that aggregate behavioral data across the open web and sell it as intent data.

Bombora. The most established third-party intent provider, Bombora aggregates content consumption data from a cooperative of B2B media publishers. When a company's employees consume significantly more content on a specific topic than their historical baseline, Bombora flags that company as showing "surge" intent for that topic. The signal is company-level, not contact-level.

TechTarget Priority Engine. Provides intent signals specifically for technology purchase decisions, sourced from TechTarget's network of technology media properties. More targeted than Bombora for technology categories, but narrower in scope.

6sense and Demandbase. These platforms combine third-party intent data with first-party website identification and predictive modeling to produce account-level buying stage predictions. They aim to tell you not just that an account is showing intent, but where they are in the buying journey.

The Data Quality Prerequisite

Here is where everything in this post connects to everything we have been writing about for the past six months: buying signals are useless when applied to bad data.

This is not a theoretical concern. It is a mechanical one. Consider the operational sequence when a buying signal fires:

1. Signal detected: Company X is showing elevated intent for your category
2. Account lookup: Find Company X in your CRM and identify the contacts associated with it
3. Contact evaluation: Determine which contacts at Company X are the right people to engage
4. Enrichment check: Verify that those contacts have complete, current data — valid email, accurate job title, correct phone number
5. Routing: Send the signal-qualified contacts to the appropriate sales rep based on territory, segment, and tier
6. Engagement: Sales rep reaches out with context-appropriate messaging

Every step after step 1 depends on data quality. If Company X exists as three different records in your CRM because of duplicates, the signal cannot be accurately attributed. If the contacts associated with Company X have stale job titles, your routing logic sends them to the wrong rep. If the email addresses are outdated, your outreach bounces. If the firmographic data is wrong, your tiering and prioritization are incorrect.

The signal was real. The intent was genuine. And your inability to act on it — because your data was not clean enough to support the operational sequence — means the signal was wasted. Your competitor, with a cleaner database, acted on the same signal and engaged the prospect first.

This is why we have consistently argued that data quality is not a back-office hygiene project. It is a revenue infrastructure investment. Every enrichment dollar, every deduplication hour, every email validation pass improves your ability to act on the signals that drive pipeline. For a comprehensive framework on building this foundation, see our RevOps data quality framework.

And the inverse is equally true: every dollar spent on buying signal technology without a corresponding investment in data quality is a dollar spent on information you cannot operationalize. The signal providers do not tell you this, because data quality is your problem, not theirs. But it is the single biggest determinant of whether signal investments generate ROI.

How Signals Integrate with Enrichment: The Closed-Loop System

The most sophisticated RevOps operations do not treat enrichment and signals as separate workstreams. They build a closed-loop system where signals trigger enrichment, enrichment enables action, action generates engagement data, and engagement data refines future signal interpretation.

Here is how the closed loop works in practice:

Step 1: Detect. A buying signal fires — a target account is showing elevated intent on G2, or a contact from a target company visits your pricing page for the third time this week, or Bombora flags a surge in content consumption on your category topic.

Step 2: Enrich. The signal triggers an automatic enrichment workflow. The contact or account record is sent through your enrichment waterfall to ensure all fields are current and complete. If the contact's job title is 18 months old, it gets refreshed. If the email is unverified, it gets validated. If firmographic data is stale, it gets updated. This step ensures that when the signal reaches sales, the record attached to it is actionable. For details on building this waterfall, see our enrichment guide.

Step 3: Score. With fresh enrichment data and an active buying signal, the lead scoring model produces a score that reflects both fit (who they are, from enrichment) and timing (what they are doing, from signals). This composite score is fundamentally more accurate than scores based on either dimension alone.

Step 4: Route. The signal-enriched, accurately scored lead is routed to the appropriate sales rep based on current firmographic data — not the stale territory assignment that might have been correct when the record was first created, but the correct assignment based on freshly enriched data.

This four-step loop — detect, enrich, score, route — runs continuously. It does not wait for a quarterly data cleanup. It does not depend on a sales rep noticing that a prospect seems interested. It operates at machine speed on machine-quality data, which means it only works when the data foundation is solid.

The Future of RevOps: Detect, Enrich, Score, Route

The four-step loop described above is not just an operational pattern. It is the direction RevOps is moving as a discipline.

For the past decade, RevOps has been primarily a configuration and alignment function — setting up CRMs, building workflows, aligning sales and marketing processes, producing reports. Valuable work, but fundamentally administrative. The RevOps team configured the machine. Other teams operated it.

The next phase of RevOps is operational intelligence. The RevOps team does not just configure the machine — they build the data infrastructure that makes the machine intelligent. Signal detection, enrichment orchestration, quality scoring, and intelligent routing are not admin tasks. They are the revenue infrastructure that determines whether sales teams engage the right prospects at the right time.

This shift has implications for how RevOps teams are structured, what skills they hire for, and what technology they invest in.

From workflow builders to data architects. The most valuable RevOps skill in 2027 will not be the ability to build a HubSpot workflow. It will be the ability to design a data architecture that supports signal-aware operations — choosing the right enrichment providers, orchestrating them effectively, integrating signal sources, and building quality feedback loops that keep the entire system calibrated. For a practical starting point on this architecture, see our lead routing guide.

From tool administrators to system integrators. RevOps teams currently manage a stack of point solutions — CRM, enrichment tool, email platform, calling tool, signal provider. The future is integration: building the connective tissue that allows signals from one system to trigger actions in another, with data quality as the reliability layer that ensures those cross-system actions produce correct outcomes.

From retrospective reporting to predictive operations. Current RevOps reporting answers "what happened" — how many MQLs, what was the conversion rate, which campaigns performed. Signal-aware RevOps answers "what is about to happen" — which accounts are entering evaluation cycles, which contacts are accelerating through the funnel, which segments should receive increased sales attention this week.

This is not a theoretical future. Teams at the leading edge of RevOps maturity are already operating this way. The gap between those teams and the average RevOps operation is not technology — every tool mentioned in this post is commercially available today. The gap is data quality. The leading teams invested in clean data foundations before they invested in signal technology. The average teams are still trying to build signal-aware operations on databases with 40% field completeness and 10% duplicate rates.

The Path Forward

If this post has done its job, you understand three things:

First, enrichment and buying signals are complementary but distinct capabilities. Enrichment tells you who your prospects are. Signals tell you when they are ready to buy. You need both, and you need enrichment first because signals without clean data are signals you cannot act on.

Second, there is a maturity curve, and skipping levels does not work. Manual to single-source to waterfall to signal-aware — each level builds on the previous one. If you are at Level 2 investing in Level 4 signal technology, you are building on a foundation that cannot support the weight.

Third, data quality is the prerequisite that determines ROI on every other RevOps investment. Not just enrichment ROI. Signal ROI. Scoring ROI. Routing ROI. Every downstream system produces better outcomes when the data flowing through it is clean, complete, and current. For a systematic approach to building and maintaining that foundation, start with our data quality framework and our guide to managing data decay.

The RevOps teams that will lead in 2027 are the ones building data quality foundations today — not because data quality is exciting, but because it is the infrastructure that makes everything exciting actually work.

What We Are Building

At MarketingSoda, we are building MarketingSoda Refine toward signal-aware quality scoring. Today, Refine scores every contact and company record in your HubSpot across seven quality dimensions and triggers enrichment automatically when records degrade. That solves the foundation — Levels 1 through 3 on the maturity curve.

The direction we are building toward is Level 4: integrating buying signal data into the quality scoring and enrichment orchestration layer. When a buying signal fires on an account, Refine will automatically verify and refresh the associated contact records, ensuring that every signal-qualified lead reaches sales with complete, current, validated data. Detect, enrich, score, route — running continuously on a database you can trust.

If you are building toward signal-aware RevOps operations and you want the data quality foundation to support them, we would like to have you involved in early access.

Join the waitlist for MarketingSoda Refine