Table of Contents
This companion report presents additional findings from our 2025 Marketing Measurement Benchmark — breaking down how marketing teams track performance, report results, and tie metrics to compensation based on the average selling price (ASP) of their offerings.
To understand how deal size influences marketing measurement, we grouped responses into ASP bands — ranging from lower-cost, high-volume solutions to high-value, enterprise purchases — and compared how teams adapt their strategies and metrics across that continuum.
Metrics Used to Track Performance
Approaches to Measuring ABM Impact
In our main report, we found that 8 in 10 B2B organizations reported having an account-based marketing program. Organizations selling lower-cost offerings (annualized value between $10K and $100K) are statistically less likely to report having an ABM program. Still, nearly two-thirds (63%) of companies in this segment say they do — underscoring how widespread ABM has become, even for lower-priced solutions. Among companies selling solutions priced above $250K annually, reported ABM adoption reaches over 90%.
| Cost of Product Offering (annualized value) | Orgs With ABM Program | Sample Size |
|---|---|---|
| $10,000 to $100,000 | 63% | 263 |
| $100,001 to $250,000 | 89% | 170 |
| $250,001 to $500,000 | 93% | 113 |
| $500,001 to $750,000 | 92% | 85 |
| $750,000 to $1,000,000 | 87% | 63 |
Not only does ABM adoption vary across deal sizes, but the way teams measure ABM’s impact on revenue does too.
Organizations selling lower-cost offerings ($10K to $100K) are less likely to track advanced ABM metrics like opportunities, pipeline, or closed-won deals from target accounts. These teams tend to focus on earlier-stage indicators such as MQLs, tracking an average of just under 2.4 ABM-related metrics.
Teams selling mid- to higher-priced offerings ($250K to $750K) are reliably more likely to track full-funnel metrics tied to account progression — reporting over 3.8 metrics on average. They appear to have a stronger handle on ABM measurement, with increased reporting of opportunities, pipeline, and even closed-won deals from target accounts.Still, in the broader context of ABM maturity, these figures remain modest. Even among organizations with higher ASPs, advanced measurement practices are far from universal suggesting that many are still only partially connecting ABM efforts to revenue outcomes.
Measurement Practices in Traditional Demand Gen
Across all deal sizes, non-ABM demand generation metrics continue to be widely tracked. But there are meaningful differences in how consistently they’re used. Organizations with higher deal sizes (especially $250K to $750K) are reliably more likely to track the full-funnel: they report higher usage of converted MQLs, pipeline, and early-stage opportunities. By contrast, teams with smaller ASPs (<$250K) tend to concentrate more on MQLs produced and converted only.
Account Engagement and Intent Metrics
Most B2B marketing teams track similar types of engagement and intent signals, regardless of the price point of the solutions they sell. Accounts engaging with website content are the most commonly tracked signal across all deal sizes, and teams selling mid- to high-priced offerings ($250K to $750K) are statistically more likely to track this signal than those selling lower-cost solutions. Intent signals and advertising reach, while also monitored, show less consistent differences across deal sizes. In general, the types of engagement metrics being tracked are fairly consistent—but some signals, like web engagement, appear to gain more traction among mid-market teams.
Use of Third-Party Intent Data
In addition to the engagement and intent signals above, we also asked whether teams use third-party intent data to identify pipeline opportunities. Most do.
Tracking Engagement at the Buying Group Level
We also asked whether organizations track engagement at the buying group level. Roughly three-quarters of B2B organizations say they do. While tracking appears somewhat more common among those selling mid-to-large deals—especially in the $500K to $750K range—this practice is reported across all ASP bands. The overall trend suggests widespread adoption, regardless of deal size.
Metrics Shared with the Board
Board Visibility into ABM Metrics
Across all deal sizes, B2B marketers report relatively few ABM-related metrics to the board—often zero or just one. But reporting becomes more common as deal size increases.
On average, organizations selling lower-cost offerings (under $250K) report fewer than one ABM metric to the board. Meanwhile, those selling mid-to-large deals ($500K–$750K) are reliably more likely to report at least one metric tied to ABM performance. When ABM metrics are shared with the board, the most common ones tend to be early indicators. Marketing Qualified Accounts and Opportunities from target accounts top the list — especially among organizations with deal sizes over $500K, where about 1 in 4 report these metrics to executive leadership. More advanced metrics like pipeline and closed-won revenue from ABM accounts are reported less frequently, even among higher-ASP teams.
Board Visibility into Traditional Demand Gen Metrics
When it comes to non-ABM demand generation, reporting to the board remains relatively limited across all deal sizes. MQL metrics — both volume and conversion — are again the most commonly surfaced, especially among teams selling $500K to $750K solutions. But even in that range, fewer than 30% report MQL conversion at the board level. Metrics tied to deeper funnel outcomes — like pipeline or closed-won deals — are rarely included in board-facing reports.
Which Engagement Metrics Reach the Board
When it comes to the engagement and intent signals that make it into board-level reporting, most B2B organizations share similar types of metrics. Account-level intent and website engagement are more commonly reported than advertising reach, but this general pattern doesn’t reliably differ by deal size. While some differences appear in the raw percentages, high variability in responses means there are no statistically reliable differences in which types of signals are prioritized in board conversations across ASP bands.
Board Visibility into Third-Party Intent Data
Among organizations that track pipeline opportunities sourced from third-party intent, relatively few report this information at the board level—just 13% to 27%.
Board Visibility into Buying Group Engagement
Among organizations that track engagement at the buying group level, relatively few report these metrics to the board. Across deal sizes, reporting rates remain modest—generally under 30%. While organizations selling larger deals are somewhat more likely to elevate this data to leadership, the overall picture suggests limited board-level visibility into buying group engagement.
Metrics That Influence Variable Compensation
After examining which metrics are shared with the board, we also looked at which are tied to variable compensation — such as bonuses or commissions. This helps clarify how different types of performance are prioritized.
ABM Metrics in Compensation Plans
The number of ABM metrics tied to variable compensation remains low overall. Across organizations selling lower- to mid-priced solutions (under $250K), fewer than one metric on average is linked to pay. While teams selling $500K–$750K solutions report reliably more compensation-linked metrics (over 2 on average), these teams are the exception. Even among high-value deal sizes, the number of ABM metrics tied to comp rarely exceeds one or two, suggesting that most organizations still separate performance measurement from incentive structures.Marketing Qualified Accounts (MQAs) stand out as the most commonly tied to compensation — especially among organizations selling mid- to high-value offerings. Opportunities and pipeline from target accounts also appear more frequently, particularly in the $500K to $750K range. But across all deal sizes, it’s rare for more advanced ABM metrics (like closed-won deals from target accounts) to be directly tied to pay.
Legacy Demand Gen Metrics in Compensation Plans
Just like with ABM, organizations selling higher-priced offerings are more likely to tie their non-ABM demand generation metrics to compensation. But again, the actual number of metrics tied to comp is low overall.
Teams selling lower-cost solutions (under $250K) report tying fewer than one non-ABM metric to compensation, on average. In contrast, teams in the $500K to $750K range stand out, with significantly higher averages—closer to 1.6 metrics. This group is reliably different from all others in the post hoc test.
Still, even at the higher end, we’re not seeing many metrics tied to variable pay—suggesting that while alignment between performance and incentives improves with deal size, it’s far from universal.Across deal sizes, MQL-related metrics are the most likely to be tied to compensation. MQLs produced and MQLs converted appear more frequently than other metrics in variable comp structures—particularly among teams selling mid- to high-value offerings.
Account Engagement & Intent Signals That Influence Pay
Across most deal sizes, B2B organizations report tying none or just one engagement or intent metric to compensation, on average. However, teams selling mid-to-large deals in the $500K–$750K range are statistically more likely to incentivize at least one such metric. By comparison, teams selling lower-cost offerings ($100K to $500K) are reliably less likely to link these signals to pay.When it comes to which metrics are being incentivized, website engagement appears more commonly tied to compensation among teams selling mid- to higher-priced offerings ($250K to $750K). However, these differences are not statistically reliable. In other words, while higher-ASP organizations may appear more likely to use these signals in comp plans, the variation within deal size bands is too high to say for certain.
Attribution
Across deal sizes, most organizations report using both marketing-sourced and influenced attribution models. The proportion ranges from 56% to 61% across ASP bands, with no major deviations.
How Teams Assign Credit
Multi-touch remains the dominant attribution model across deal sizes. But, as noted in past research, multi-touch typically assigns credit to just a few visible interactions, offering an incomplete and likely deceptive view of the full buyer journey.
Despite its limitations, multi-touch far outpaces more rigorous approaches. Statistical modeling—the only method capable of estimating actual impact across all activities—is the least used. Fewer than 1 in 4 teams apply it, even among those selling high-value offerings. This pattern reinforces a familiar theme: attribution remains more about what’s easy to track than what’s actually driving outcomes.
How Marketers Attribute Influence
Marketers use a variety of methods to measure influence, but most focus on either program-level or tactic-level reporting. Teams selling mid-range solutions ($250K to $500K) are reliably more likely to measure influence at the tactic level compared to several other ASP groups. This suggests a stronger focus on tactic attribution among these teams.
In contrast, teams with lower-cost offerings ($100K to $250K) are reliably less likely to measure influence at the program level, indicating a more limited approach to assessing marketing’s broader contribution. In contrast, while the chart suggests differences in overall-level influence measurement by ASP category, high variation in responses prevented detection of any statistically reliable differences across ASP groups for this type of measurement.
ROI
Marketing ROI measurement is nearly universal. Regardless of deal size, the vast majority of B2B organizations report tracking marketing return on investment—suggesting that ROI measurement is now a baseline expectation.
Overall contribution is the most commonly used approach across all deal sizes. Teams selling $500K to $750K offerings are reliably more likely than several other ASP groups to measure ROI at the tactic level. This suggests a stronger emphasis on tracking individual campaign performance among this segment—potentially reflecting the need to justify higher-cost initiatives with more granular evidence of return.
Mapping Relationship Journeys: Funnel and Waterfall Models
Waterfall models offer a structured way to map the relationship between buyers and sellers over time. They help organizations track how accounts move through defined stages of engagement and opportunity creation. Some models begin with initial sales interactions, while others take a broader view — starting from the moment an account is identified or targeted, well before direct engagement from sales. In either case, the goal is to bring consistency and visibility to the stages leading up to, and including, deal conversion. Teams selling $500K to $750K solutions are the most likely to use opportunity-based waterfall models—suggesting a focus on buying-stage progression. But they also top the list for lead-based models, highlighting how legacy and modern practices often coexist.
Keep Reading: More Benchmark Breakdowns
This is just one slice of our 2025 Marketing Measurement Benchmark. For a broader look at how B2B teams approach measurement, explore our other reports covering ABM maturity, traditional demand generation, and more.
Appendix
Respondent and Company Profiles
We surveyed 634 B2B marketers in the early months of 2025. Responses were collected through a third-party panel provider. To address underrepresentation of VC-backed companies, the final dataset also includes 100 synthetic responses generated using predictive modeling based on patterns from real VC-backed respondents. This is described in more detail below.
Where appropriate, we compare this year’s results with responses collected in previous years — 716 additional responses gathered across 2023 and 2024 — to identify patterns over time.
Industries Represented
Respondents were most likely to work in Services organizations (57%), followed by Technology companies (18%) and Manufacturing firms (12%). The remaining 14% fell into an “Other” category for which respondents provided their own industry descriptions.
Seniority and Role Scope
A plurality of respondents held manager-level roles (35%), with directors making up another 23%. C-level leaders represented 18% of the sample, while 15% were individual contributors and 9% were VPs.
Most respondents (83%) reported working across multiple marketing functions, highlighting a strong presence of cross-functional marketers in the sample. Only 17% identified as single-function marketers.
Funding and Ownership Structure
Just over half of respondents (51%) said their company is publicly traded. An additional 25% came from privately held firms, while 21% worked at private equity (PE)-backed organizations. Venture capital–backed companies made up the smallest share at 3%.
Expanded Sample for VC-Backed Organizations
To address the limited representation of marketers from VC-backed organizations, we included 100 synthetic responses from simulated VC-backed organizations. These responses were generated using predictive modeling based on patterns observed in our real VC-backed respondents. Statistical tests confirm that there are no meaningful differences between the real and synthetic responses, indicating that the simulated data provides an accurate reflection of how additional VC-backed organizations would be expected to respond. Including these responses enhances the overall balance and representativeness of the sample — ensuring VC-backed companies are proportionally reflected in the findings.
Company Size
Respondents came from organizations of all sizes. The largest group (44%) worked at small companies with 200 or fewer employees, where average revenue was under $1 million. Mid-sized companies (201 to 1,000 employees) made up 27% of the sample, with revenue ranging from just under $1 million to around $30 million.
Larger companies were also well represented:
16% from firms with 1,001 to 5,000 employees (~$80 million average revenue)
6% from firms with 5,001 to 10,000 employees (~$644 million)
6% from firms with more than 10,000 employees (~$4.6 billion)
