14 findings

Research Data

The empirical findings, statistics, and structural results from the RevOps research programme — adoption rates, maturity gaps, and predictive coefficients.

Research finding

75% of the highest-growing B2B technology companies will have a RevOps model by 2026

This projection helped catalyse widespread practitioner interest in RevOps and drove a corresponding rise in academic attention. The figure refers specifically ...

Research finding

Approximately 82% of RevOps implementations operate at a developing rather than developed level of maturity

The maturity gap is one of the principal explanations for the disconnect between RevOps adoption rates and demonstrated RevOps impact. Stage one (getting starte...

Research finding

RevOps Drivers explain 67% of the variance in business Outcomes in the empirical RevOps Value Chain model

This is a substantial explanatory power for perceptual social-science research on organisational mechanisms. The R² indicates that alignment, integration, and c...

Research finding

RevOps Resources explain 56.5% of the variance in Drivers in the empirical RevOps Value Chain model

Resources (data, systems, processes, enablement) are strong predictors of the integration mechanisms (alignment, integration, collaboration) that produce busine...

Research finding

Roughly 50% of GTM stakeholders disagree that RevOps effectively provides training, onboarding, and documentation support

The enablement deficit is one of the two principal divergence findings from the cross-functional stakeholder survey. It indicates that RevOps excels at structur...

Research finding

Only 34% of GTM stakeholders agree or strongly agree that RevOps drives customer experience outcomes

The customer experience blindspot is the second principal divergence finding. It is concerning given that, in subscription business, lifetime value depends on r...

Research finding

Standardised path coefficient (β) of 0.752 from Resources to Drivers in the RevOps Value Chain

The β coefficient indicates a strong positive structural relationship between RevOps resources and the integration drivers they enable. The result is highly sta...

Research finding

Standardised path coefficient (β) of 0.818 from Drivers to Outcomes in the RevOps Value Chain

The β coefficient indicates a very strong positive structural relationship between RevOps drivers and the business outcomes they produce. The Drivers-Outcomes r...

Research finding

Indirect effect of Resources on Outcomes through Drivers is 0.615 in the RevOps Value Chain

Calculated as the product of the two path coefficients (0.752 × 0.818), the indirect effect demonstrates that RevOps resources translate into outcomes substanti...

Research finding

13 senior B2B RevOps practitioners across companies ranging from 30 to 300,000 employees informed the qualitative foundation

Participants accounted for more than 150 years of corporate experience and approximately 30 years of cumulative RevOps experience. Recruitment was via purposive...

Research finding

82 cross-functional GTM stakeholder responses informed the empirical PLS-SEM analysis of the RevOps Value Chain

From 285 collected responses, 82 met inclusion criteria for completeness and seniority. 89% of respondents came from core GTM functions (sales, marketing, custo...

Research finding

RevOps integrates three or more differentiated functions, extending integrative device theory beyond the dyadic cases studied previously

Prior empirical work on integrative devices (trade marketing, category management, sales and operations planning) has been confined to dyadic relationships betw...

Research finding

RevOps operates at the operational layer rather than the functional layer, providing integration without disruption to specialised functions

Earlier integrative devices typically required functional-layer structural change. RevOps achieves coordination by adding an operational layer beneath the diffe...

Research finding

Overall ordinal alpha of 0.89 across the 13 RevOps measurement items demonstrates strong scale internal consistency

Ordinal alpha is the appropriate reliability measure for ordinal Likert-scale data, calculated using a polychoric correlation matrix. Values above 0.70 are conv...