ZoomInfo's GTM Bench scores AI platforms on enterprise tasks. But how do these benchmarks apply to SMBs? We analyze the index and offer alternatives.

ZoomInfo's GTM Bench V1 gives its own GTM.AI a score of 77, compared to 47 for Apollo, based on performance across 20+ enterprise-focused tasks. This methodology, however, overlooks the needs of local and small businesses, where success depends less on tracking corporate signals and more on accessing verifiable owner contact data. For SMBs, GTM effectiveness is measured by plain facts: a deliverable email, a working phone number, and direct access to the decision-maker.

TL;DR

ZoomInfo's GTM.AI scored 77 on its GTM Bench index, while competitor Apollo scored 47.
The benchmark measures performance on tasks like building lists from criteria such as funding rounds and tech stacks.
In tests, ZoomInfo's system returned 478 verifiable records per 1,000, versus 7 to 35 for competitors.
The index's focus on enterprise signals fails to measure what's crucial for local businesses: owner contact data.
Effective SMB outreach relies on plain-fact leads with high email deliverability, not complex AI-driven narratives.

What is the ZoomInfo GTM Bench?

In July 2026, ZoomInfo introduced the GTM Bench, a standardized evaluation system designed to score the performance of AI agents on go-to-market tasks. [4, 6] This benchmark was created to bring clarity to the increasingly crowded and confusing AI vendor landscape, moving beyond marketing claims to provide a quantitative measure of real-world effectiveness. The methodology was intentionally designed by a group of go-to-market and revenue operations practitioners to mirror the actual workflows and challenges faced by modern revenue teams, such as building target account lists, enriching records with verified data, and scoring accounts for prioritization. [8, 16] Unlike general AI benchmarks that test abstract reasoning in closed environments, the GTM Bench focuses on practical commercial outcomes, acknowledging that a prospect list that is 90% correct still sends a sales representative to the wrong company 10% of the time. [9] The stated goal is to create a transparent, versioned standard that evolves with the technology, allowing buyers to make more informed decisions based on objective performance data rather than polished vendor demonstrations. [2, 5]

The GTM Bench assesses AI platforms on two independent axes: 'Answer' and 'Grounding'. [4, 6] The 'Answer' score measures the system's ability to successfully complete a requested task, evaluating what percentage of the assigned job the AI agent actually finishes. For example, if asked to build a list of 100 prospects, this metric would score how many of those 100 records the system delivered. The 'Grounding' score, however, measures the verifiability and accuracy of the output, calculating what share of the returned data can be traced to a real, current source. [9] This dual-axis evaluation is critical because in go-to-market execution, a confident but wrong answer can be more damaging than no answer at all; a system that hallucinates a phone number wastes a seller's time and can harm brand credibility. [3] According to the GTM Bench V1 release, this methodology directly addresses the primary failure point for GTM automation, which is not flawed logic but the high rate of data decay in B2B markets. [9]

Version 1 of the GTM Bench provides a comparative analysis of four systems and three underlying AI models, scoring their performance across more than 20 common business tasks. [5, 6] These tasks are designed to simulate a complete GTM workflow, including territory planning, identifying members of a buying committee, account scoring based on intent signals, and data enrichment for existing CRM records. [4] The results from the initial run show significant performance variance among the tested systems, which included ZoomInfo's own GTM.AI, Apollo, Exa, and a baseline of open-web search. [9, 11] For instance, on a task involving a 1,000-contact list, the non-ZoomInfo systems collectively returned 720 incorrect phone numbers, highlighting the challenge of data accuracy at scale. [9] By publishing the methodology, sample tasks, and grading rubrics, ZoomInfo aims to establish a transparent standard, though it acknowledges its position as a vendor-run benchmark in the official announcement. [4]

System Evaluated	Overall GTM Bench Score (V1)	Answer Score (Task Completion)	Grounding Score (Data Verifiability)	Verifiable Records per 1,000
ZoomInfo GTM.AI	77	98%	48%	478
Apollo.io Platform	47	Not Published	Not Published	35
Exa	36	Not Published	Not Published	12
Open-Web Search	31	Not Published	Not Published	7
Methodology Note	Composite of Answer & Grounding	Share of requested work delivered	Share of data traced to a real source	Number of records with verifiable data

How Top B2B Data Platforms Performed on the Index

ZoomInfo's GTM.AI achieved a leading index score of 77 in the initial V1 benchmark run, significantly outpacing its competitors and establishing a new performance standard for go-to-market AI systems. [2, 4, 6] The GTM Bench framework was specifically engineered to move beyond abstract academic tests, instead evaluating platforms on over 20 practical sales and marketing tasks like building prospect lists and enriching account data. [2, 4] This methodology, detailed in The Zoominfo Modern GTM Index, assesses systems on two core axes: the 'Answer Index,' which measures task completion volume, and the 'Grounding Index,' which verifies that the data provided traces back to a real, current source. [5, 13] The emphasis on grounding is critical in an environment where data decay is a constant threat; B2B contact data decays at a rate of 2.1% per month, or 22.5% annually, rendering unverified information a significant liability. [8] By prioritizing verifiable data lineage, the benchmark directly addresses the pervasive issue of AI hallucinations and obsolete records that undermine revenue operations. [2]

Competitor platforms recorded substantially lower scores, with Apollo achieving a 47, Exa a 36, and traditional open-web searches trailing at 31. [2, 4, 6] These results highlight a distinct performance gap, which can be partly attributed to the different market segments these platforms serve. While ZoomInfo has invested heavily in deep US enterprise data, Apollo's strengths lie in its self-serve model and accessibility for mid-market teams, creating different optimization priorities. [7, 10] The GTM Bench's focus on enterprise-centric tasks, where data depth and accuracy are paramount, naturally favors a system like ZoomInfo's GTM.AI. The benchmark's public methodology allows for scrutiny and acknowledges that it is vendor-run, grading grounding against its own verified records rather than an independent audit. [4, 6, 13] This scoring differential underscores a fundamental challenge in the B2B data space: the cost of poor data quality, which Gartner estimates costs organizations an average of $12.9 million annually, makes the choice of a data partner a high-stakes decision. [8] For companies evaluating these tools, the index scores reflect not just a platform's capabilities but its underlying data philosophy and target customer profile.

The performance disparity becomes even more stark when examining specific data quality metrics from the benchmark tests. In a standardized test processing 1,000 contacts, ZoomInfo's GTM.AI system returned 478 fully verifiable records, a testament to its grounding capabilities. [2, 5] In stark contrast, the other systems tested returned between just 7 and 35 verifiable records from the same contact list. [4, 6] This gap in reliability has massive implications for sales productivity, as teams using less dependable data are forced to waste valuable time on manual verification. This is a significant cost, as some reports show sales reps can lose up to 550 hours annually dealing with the fallout from poor data quality. [3] Furthermore, the non-ZoomInfo systems collectively generated 720 incorrect phone numbers during the 1,000-contact test, flooding sales queues with useless information and actively hindering outreach efforts. [2, 4] This finding aligns with industry reports indicating that B2B phone number data decays at roughly 18% per year, making unverified numbers a primary source of inefficiency. [8] For SMBs, where every outreach attempt counts, such high error rates are not just inefficient; they are a direct threat to pipeline generation, as detailed in ZoomInfo's own GTM Index analysis.

The Index's Blind Spot: Enterprise Signals Don't Work for Main Street

The ZoomInfo Modern GTM Index is built on a foundation of enterprise-centric tasks that are largely disconnected from the realities of the small and medium-sized business (SMB) market. Its benchmark tests evaluate an AI's ability to perform functions like building prospect lists from 'Series B SaaS startups' or identifying companies that use specific corporate software like Salesforce. [7, 8] These tasks, detailed in the GTM Bench V1 methodology, are designed to measure performance in identifying and tracking complex corporate buying signals. [6] Such signals, including funding rounds, large-scale technology adoption, and executive job changes, are the bread and butter of enterprise sales intelligence platforms. However, for a sales professional trying to reach a local restaurant, plumbing contractor, or independent retail shop, knowing that a company has a new Chief Revenue Officer or just adopted Marketo is entirely irrelevant. The signals that matter on Main Street are not about corporate structure but about direct, verifiable access to the owner or key operator, a blind spot for models trained on enterprise data.

Exacerbating this disconnect is the severe rate of B2B data decay, a problem that disproportionately affects the fragmented SMB market. While industry benchmarks often cite an average annual decay rate of 22.5% for B2B contact data, this figure can soar to 70% in high-turnover industries and is particularly acute for small businesses. [1, 2] According to research validated by HubSpot and others, this decay means that a contact record can become materially inaccurate, with a wrong job title or phone number, in a matter of months. [3] For SMBs, which have higher rates of business failure, less formal corporate structures, and frequent changes in ownership or operations, this decay is even more rapid and harder to track. [12, 13] Platforms like ZoomInfo and Apollo, which rely on scalable, automated data collection from public web sources and corporate signals, are structurally disadvantaged in this environment. Their models are not optimized for the manual, localized verification needed to maintain accurate data for millions of small, independent businesses, making their utility for Main Street sellers fundamentally limited.

Incumbent GTM platforms like Apollo.io and ZoomInfo structurally struggle to resolve named decision-makers for local businesses because their data collection and validation methodologies are optimized for the enterprise segment. These platforms excel at parsing corporate hierarchies, tracking professional social networks, and identifying technology installations, signals that are abundant in larger companies but scarce in the SMB world. [14, 11] Finding the direct mobile number of a salon owner or the email for the manager of a local construction firm is a fundamentally different challenge than identifying the VP of Engineering at a public tech company. The latter leaves a significant digital footprint through press releases, corporate directories, and professional profiles, while the former often requires validation through non-scalable means like local business registries or direct outreach. As a result, when these platforms attempt to serve SMB data, they often return generic business lines or, worse, outdated information, a direct consequence of data decay that can reach up to 70.3% annually for some fields. [5] This structural mismatch between enterprise-focused data gathering and the on-the-ground reality of Main Street is the index's most significant blind spot.

Data Signal / Method	Description	Relevance to Enterprise Sales	Relevance to SMB Sales
Funding Round Data (e.g., Series B)	Tracking private investment rounds from venture capital.	High: Indicates budget, growth, and specific buying windows.	Very Low: Most SMBs are not venture-backed.
Corporate Tech Stack (e.g., Salesforce)	Identifying software installed across a company (CRM, ERP, etc.).	High: Crucial for integration-based sales and identifying competitors.	Low: Tech stacks are often minimal, non-standardized, or irrelevant.
Executive Job Changes	Monitoring senior leadership hires and departures.	High: New leaders often bring new budgets and strategies.	Low: Ownership changes are more critical than executive churn.
Public Company Filings (10-K)	Analyzing required financial and strategic disclosures.	Medium: Useful for targeting public companies and understanding market trends.	None: Not applicable to private small businesses.
Local Business License/Permit Data	Data from municipal or state filings for new businesses or renewals.	Low: Too granular and localized for most enterprise campaigns.	High: A primary source for identifying new businesses and verified owners.
Verified Owner Mobile Number	A direct, confirmed phone number for the business owner.	Medium: Useful, but often routed through executive assistants.	Very High: The most critical data point for direct access to the decision-maker.

A Better GTM Benchmark for Small and Local Business

For small and local businesses, the primary go-to-market success metric is the availability of verified owner contact information, not the complex company-level buying signals prioritized by enterprise-focused benchmarks. While frameworks like the ZoomInfo Modern GTM Index excel at tracking corporate-level intent, the path to a sale in the SMB world is far more direct. Success hinges on reaching the ultimate decision-maker, who is almost always the owner. Unlike enterprise deals where buying groups can involve four or more people, the owner of a small business can often give a definitive 'yes' on their own, drastically shortening the sales cycle. [3] This reality means that the most valuable data is not a surge in web traffic or a new technology installation, but rather a confirmed name, email, and phone number for the proprietor. According to a 2025 analysis, generating high-quality SMB owner leads is the most crucial step for securing sales in this segment because these individuals have direct purchasing power. [4] Therefore, a GTM benchmark built for SMBs must discard enterprise vanity metrics and instead measure the accessibility and accuracy of owner-level contacts, as this is the foundational data point that drives all subsequent outreach and revenue.

A successful SMB outreach campaign is measured by plain facts, starting with a verified, deliverable email that has a specific, high-percentage delivery rate, not just a theoretical checkmark. Data quality is a significant challenge; the Salesforce State of Sales 7th Edition (2025) found that a majority of sales professionals with AI agents report that poor data quality hurts their sales outcomes. [5] This problem is magnified in the B2B email landscape, where data decays rapidly. According to Validity's 2024 Email Deliverability Benchmark, approximately one in every six emails fails to reach the intended inbox, placing the global average inbox placement rate at around 84%. [2] For B2B cold outreach, the reality is even harsher, as filters at major providers like Microsoft are particularly tough, leading to lower performance. A separate 2025 analysis further distinguishes between a 98.16% delivery rate, which simply means a server accepted the email, and an 84.3% inbox placement rate, which is what actually matters. [6] For a small business, every bounced email is a wasted opportunity and a direct marketing cost. A meaningful benchmark must therefore move beyond simple validation and instead quantify the precise inbox placement rate, providing a clear measure of data reliability and campaign effectiveness.

A working phone number that actually rings and connects to the target is another critical, non-negotiable data point for any SMB-focused GTM strategy. Major data platforms frequently return high rates of incorrect or disconnected numbers, a problem that directly impacts sales productivity and morale. While comprehensive statistics on phone number accuracy are scarce, the emphasis on data quality in major industry reports highlights the scale of the issue. For instance, Gartner research notes that poor data quality costs organizations millions annually, with data inconsistency across sources being the most challenging problem. [14] In the context of SMB sales, where direct communication channels like phone calls and even texting are highly effective, a bad number is a complete dead end. [16] The Salesforce State of Sales 7th Edition (2025) reinforces this by noting that high-performing sales teams are 1.5 times more likely than their peers to prioritize data hygiene to improve outcomes. [5] An effective GTM benchmark for small businesses must therefore include a metric for phone number connect rates, measuring the percentage of dials that result in a successful connection, as this directly reflects the data's utility and the potential for meaningful conversations.

The ability to get backup contacts for each business provides a crucial safety net that significantly de-risks outreach campaigns and ensures long-term account stability. Relying on a single point of contact, even if it is the owner, is a precarious strategy; people change roles, sell their businesses, or simply become unresponsive. B2B contact data is known to decay at a rate of 20-30% per year, making a single-threaded relationship inherently fragile. [3] Building a network of multiple relationships within an account is a fundamental sales planning technique that mitigates this risk. [7] A multi-channel outreach strategy, which naturally benefits from having multiple contacts, increases touchpoints and conversion opportunities by reaching prospects where they are most active. [25] According to a 2023 analysis, nurturing leads through multiple channels and contacts can result in those leads spending 47% more than non-nurtured ones. [22] For SMBs, a backup contact, such as a general manager or a secondary partner, offers an alternative path for communication if the primary owner is unavailable. A superior GTM benchmark would quantify this resilience by measuring the percentage of accounts in a dataset that include at least one verified secondary contact, directly scoring the platform's ability to provide a fallback and sustain sales momentum through inevitable organizational changes.

Beyond the Index: Why 'Plain-Fact' Leads Outperform 'AI-Scored' Narratives

Industry benchmarks that champion 'AI-scored leads' and complex 'fit scores' often obscure severe underlying data quality issues, creating a dangerous illusion of precision. For instance, ZoomInfo's own Modern GTM Index measures performance on enterprise-focused tasks, but such models can easily become a black box where the inputs are questionable. The Zoominfo Modern gtm Index how Does Your Company Stack up These scoring systems, which rely on intent data from sources like Bombora Company Surge, assume the underlying demographic and firmographic data is accurate. [1, 6] However, with B2B contact data decaying at rates between 22.5% and 70.3% annually, this assumption is flawed from the start. [4] A high score built on outdated information, such as a contact who changed jobs months ago, creates false confidence and directs sales teams to chase leads that are fundamentally unreachable. [20] Gartner estimates that poor data quality costs organizations an average of $12.9 million per year, a figure that underscores how AI scores can amplify the cost of bad data by automating outreach to a list where one in four records may be wrong by year's end. [8, 10] This focus on complex scoring models distracts from the foundational problem: the verifiable accuracy of the contact record itself.

Positioning leads with a simple set of verifiable facts projects confidence and avoids the growing problem of 'AI-slop', where outreach is filled with plausible but fabricated 'why-now' reasons. While AI tools are used by a majority of sellers for tasks like drafting emails, their misuse is creating a trust deficit with buyers. [3] Research from 2025 shows that 40% of consumers would trust a brand less if they knew its marketing emails were written by AI, and 65% of B2B buyers report having disengaged from a vendor due to overly automated or irrelevant outreach. [29, 14] This buyer skepticism is a direct response to outreach that feels emotionally flat or manipulative, a common side effect when AI generates content without proper grounding in factual data. [15] Instead of a convoluted, AI-generated narrative about why a prospect should be interested, a 'plain-fact' lead provides something more valuable: a verified email address, a direct-dial phone number, and a correct job title. This approach demonstrates respect for the buyer's time and intelligence, signaling that the outreach is based on solid, verifiable information rather than a probabilistic guess from an algorithm. It replaces the hollow confidence of a high 'fit score' with the genuine confidence of knowing the data is simply correct.

A rigorous focus on data grounding, or tracing every piece of information to a verifiable source, is critical because a 90% accurate prospect list is still 10% waste, an unacceptable margin for any go-to-market team. [11, 27] This 10% represents thousands in squandered ad spend, hours of wasted rep time, and significant damage to sender reputation from bounced emails. The cost of bad data is staggering, with U.S. businesses losing a reported $3.1 trillion annually due to inefficiencies and missed opportunities. [4, 5] This reality makes a fair billing model, one that aligns provider incentives with customer success, a non-negotiable part of a modern data partnership. A provider that is truly confident in its data quality and grounding processes should offer per-lead bounce credits, ensuring customers only pay for data that works. This model stands in stark contrast to subscription-based access where the risk of data decay, which can render nearly a quarter of a CRM useless each year, is borne entirely by the customer. [8] By tying payment to deliverability, providers are held accountable for the freshness and accuracy of their data, transforming the relationship from a simple transaction to a shared-risk partnership focused on tangible outcomes.

How to Build a Practical GTM Motion Without Annual Lock-In

Many sales teams find their go-to-market motions burdened by an excess of technology, with the average B2B sales professional using between five and ten tools to manage their workflow. [7] According to a 2026 Salesforce report, sellers use an average of eight tools to close deals, and 42% of them feel overwhelmed by this complexity. [9] This tool sprawl is not just a matter of inconvenience; it directly impacts productivity and budget. Research from Revenue Velocity Lab's 2025 analysis of 938 companies revealed that 73% of sales teams use overlapping tools, creating functional redundancy that wastes an average of $2,340 per representative annually. [17] The pressure to consolidate is growing, as organizations recognize that a bloated tech stack leads to fragmented data, inefficient workflows, and diminished return on investment. [11] As detailed in the Zoominfo Modern GTM Index, a streamlined stack is foundational to an effective GTM strategy, yet many SMBs remain trapped by the sheer volume of platforms they are expected to manage, each with its own cost and contractual obligations.

Legacy B2B data providers often compound the problem of GTM complexity by locking customers into inflexible, long-term contracts that are poorly suited for the agile needs of small and midsize businesses. These providers, known for their extensive but not always current databases, typically require enterprise-level annual contracts that can range from $15,000 to over $40,000 per year. [26] This model presents a significant barrier for SMBs, which often lack dedicated procurement teams and legal resources to negotiate favorable terms. [42] A 2025 analysis by Conditio found that 71% of SMBs have no formal process for reviewing contracts, leaving them vulnerable to unfavorable clauses like automatic renewal with price escalations. [42] These agreements frequently include terms that make it easy to add licenses but impossible to remove them until a renewal window, which can be as narrow as 60 to 90 days before the contract ends, trapping companies in paying for underutilized services. [27] This rigid structure stands in stark contrast to the dynamic nature of small businesses, where priorities can shift dramatically in less than a year, rendering a locked-in tool obsolete long before the contract expires. [37]

A modern approach to GTM technology prioritizes flexible, self-serve platforms that empower teams to acquire leads on demand, moving away from the rigid, campaign-centric model of the past. This shift is a core tenet of product-led growth (PLG), a strategy now adopted by 58% of SaaS companies as of Mixpanel's 2026 State of Digital Analytics report. [41] Instead of 'running' complex, pre-configured campaigns that require significant setup and commitment, users can 'search' for prospects as needed, paying only for what they use. This on-demand model is enabled by the rise of self-service portals, which 95% of B2B buyers who use them believe improve purchasing efficiency. [16] The PLG platform market is expanding rapidly to meet this demand, with a projected value of $14.32 billion by 2030, reflecting a strong preference for self-service software models and smoother user onboarding experiences. [6] For SMBs, this means gaining access to powerful data and tools without the prohibitive upfront costs and long-term commitments, allowing for greater agility and a more direct correlation between spending and results.

For teams that require a mix of B2B and local lead data, a modern GTM stack can be constructed far more cost-effectively than legacy models would suggest. The key is to find platforms that treat different data types with appropriate pricing structures. B2B leads are inherently more expensive to acquire than local or B2C leads due to longer sales cycles and higher customer lifetime value. [28] According to a 2025 analysis from Sopro.io, the average cost for a multi-channel B2B SaaS lead is approximately $188, while other reports place the average for B2B technology leads between $100 and $400. [3, 29] In contrast, leads for local service businesses like automotive repair can average as low as $28.50. [8] Some modern platforms leverage this cost difference by offering comprehensive B2B databases as a low-cost add-on to a primary local lead generation service. This blended approach allows a small business to access enterprise-grade B2B contact information for a fraction of the cost of a specialized provider, sometimes as much as seven times cheaper than a specialized local lead, enabling a practical and scalable GTM motion without annual lock-in.

Frequently Asked Questions

What is a modern go-to-market (GTM) strategy?

A modern go-to-market (GTM) strategy is a detailed, data-driven plan for launching a product and engaging customers. [4] Unlike traditional marketing campaigns focused on a single event, a modern GTM strategy aligns product, sales, and marketing teams around a shared plan for reaching a specific target market. [6] It uses technology and real-time data, including AI-powered insights, to refine targeting and messaging on an ongoing basis. [3] This approach allows companies to move beyond static launch plans and adapt to market feedback in weeks instead of quarters, optimizing resources and reducing risks. [4, 5]

How does the ZoomInfo GTM Bench work?

The ZoomInfo GTM Bench V1 assesses AI-powered platforms on their ability to execute over 20 real-world, enterprise-focused GTM tasks. [13] It measures performance in areas like creating prospect lists, enriching contact data, and identifying key decision-makers based on corporate signals. [7] The benchmark grades platforms on two main criteria: how much of the requested work is completed and how much of the data can be traced to a real, current source. [14] In its initial run, ZoomInfo's own GTM.AI scored 77, while Apollo scored 47, reflecting the benchmark's focus on complex, signal-based tasks rather than simple data retrieval. [14]

Are GTM benchmarks different for small businesses vs. enterprises?

Yes, GTM benchmarks for small businesses (SMBs) are fundamentally different from those for enterprises because their priorities and sales motions diverge significantly. Enterprise GTM success often hinges on identifying complex buying signals and navigating long sales cycles with multiple stakeholders, which requires sophisticated data intelligence. [18] In contrast, SMBs prioritize speed, efficiency, and direct access to decision-makers, making the accuracy of a phone number or email the most critical benchmark. [19] An enterprise-focused benchmark rewards platforms for interpreting corporate-level trends, whereas an SMB-focused benchmark would measure the raw, verifiable quality of contact data needed for high-volume, direct outreach. [27]

What is a good data accuracy rate for B2B lead lists?

A good data accuracy rate for B2B lead lists is above 95%, though the industry average is closer to 50% for many providers. [15] High-quality data providers aim for 97% or higher accuracy to ensure email bounce rates remain below 1-3%, which is crucial for protecting a company's sender reputation and maximizing campaign effectiveness. [1] B2B contact information decays rapidly, with some studies showing an annual decay rate of 22.5%, making continuous verification essential for maintaining a useful lead list. [1] For this reason, some platforms like UpLead offer a 95% accuracy guarantee backed by automatic refunds for bounced emails. [9]

How much does lead data from platforms like Apollo or ZoomInfo cost?

The cost of lead data varies dramatically, reflecting different target markets; Apollo's self-service plans start around $49 per user per month, while ZoomInfo's enterprise platform typically requires an annual contract starting around $15,000. [8, 16] This price gap exists because Apollo is designed for SMBs and individual users who need a flexible, all-in-one tool for prospecting and outreach. [20] ZoomInfo, on the other hand, provides a deeper dataset with advanced features like intent data and direct-dial phone numbers, justifying its higher price for large enterprise teams running complex, multi-channel sales motions. [8]

What is the difference between a 'signal-based' lead and a 'plain-fact' lead?

A 'signal-based' lead is identified through dynamic behavioral indicators, while a 'plain-fact' lead is defined by verifiable, static contact information. Signal-based selling uses real-time triggers, like a company's recent funding announcement or a key executive's job change, to identify prospects who are ready to buy now. [26] This approach focuses on timing and context to make outreach more relevant, with some teams reporting reply rates 2-4 times higher than traditional methods. [23] A 'plain-fact' lead, valued by SMBs, is simply a correct name, a deliverable email, and a working phone number, which provides the direct access needed for immediate outreach without complex interpretation. [https://www.zoominfo.com/blog/sales/the-zoominfo-modern-gtm-index-how-does-your-company-stack-up]

Last updated: July 2026

Modern GTM Index: How Do SMBs Stack Up?