What are Marketing Operating Models, why they matter, and how to build one that works.
Two organizational forces, Friction and Fluency, significantly shape how marketing teams operate and what they deliver. Friction refers to structural and coordination barriers that slow, distort, or block marketing work regardless of who occupies the roles. In contrast, Fluency describes the individual employee experience that enables them to do effective marketing work. This chapter describes each construct, reports their prevalence across the sample, and quantifies their independent effects on people and business outcomes.
The organizational conditions that get in the way of effective marketing, regardless of who holds the roles. Friction is found in the interaction between teams and people.
| Resource Rigidity | The inability to shift budgets or redeploy people when market conditions change. |
| Organizational Silos | When teams work in isolation, get left out of decisions, or cannot collaborate effectively across functions. |
| Decision Ambiguity | Unclear decision rights, frequent changes of direction, and leadership that is not aligned on priorities or strategy. |
| Lack of Empowerment | Too many layers of approval, interference from outside the team, and micromanagement that stops people from acting on their own judgment. |
The personal conditions that allow individuals to do their best work. Fluency is about the individual experience of work, not about how the organization interacts.
| Mastery | A personal commitment to developing skills and improving continuously in the work that matters most. |
| Purpose | A personal sense of meaning in the work, with a clear understanding of how it connects to something larger. |
| Focus | Personal clarity on what matters most and which tasks deserve the most energy and attention. |
| Psychological Safety | The freedom to speak up, take risks, and try new approaches without fear of negative consequences. |
| Firm Size | n | Elevated Friction | Strong Fluency |
|---|---|---|---|
| Small (<500 employees) | 270 | 38.1% | 54.4% |
| Medium (500–2,000) | 170 | 47.6% | 53.5% |
| Large (>2,000 employees) | 352 | 57.7% | 45.2% |
| Firm Age | n | Elevated Friction | Strong Fluency |
|---|---|---|---|
| Young (2005+) | 219 | 52.1% | 49.8% |
| Mid (1990–2004) | 137 | 56.2% | 52.6% |
| Legacy (pre-1990) | 141 | 54.6% | 41.1% |
Predicted outcome values as Friction and Fluency move across their full 1–5 range. Each line estimated from a separate model with controls held at sample means. n=843.
Note on WMO classification: A Winning Marketing Organization (WMO) is defined as a respondent scoring at or above the 75th percentile on all four marketing satisfaction dimensions (Commercial, Brand & Market, Operational, and Intelligence & Technology) within the high-quality sample. Low performers (LMO) score at or below the 25th percentile on three or more of the four dimensions. All quartile cutoffs are calculated within the HQ sample (n=843). WMO=19.8%, EMO=59.3%, LMO=20.9%.
Each line shows the marginal effect of the predictor alone (bivariate + controls). Linear models for all predictors. x-axis = predictor score (1–5). Controls: firm size and age at sample means. HQ sample, n=843 (n=818 for burnout and talent items).
The data reveal a clear pattern. More than half of large firms operate above the sample mean on Friction, and organizations founded before 1990 show the weakest Fluency levels in the sample. Among all firms, 37.7% fall into the High Friction / Low Fluency quadrant, the combination most associated with weaker people and business outcomes.
Friction and Fluency operate through distinct pathways. Friction is primarily a cost driver. Higher Friction is associated with greater operational costs and talent difficulties. Fluency is primarily a performance driver. Higher Fluency is associated with stronger NPS scores and better business outcomes. The data show that both forces need to be addressed. Reducing Friction without building Fluency lowers costs but does not improve people outcomes. Building Fluency without reducing Friction improves performance but does not address the structural cost burden.
The following chapters examine how the Marketing Operating Model shapes both Friction and Fluency levels, and what organizations with strong MOMs do differently.
If a company is able to turn inputs into outputs, it has a way of working, but it may not have a formal operating model. A marketing operating model is the structured and unique way by which your company manages marketing investments to achieve its desired outcomes.
Defines the purpose and direction of the marketing function. This dimension includes a shared understanding of the unique role the marketing function plays for the firm.
Describes the priorities that guide marketing work, including priority capabilities, priority competencies, and priority metrics.
Defines how marketing work is organized and conducted. Covers the structures, processes, tools, and methods that govern how marketing work is done.
The MOM Presence Index measures how formally and consistently an organization has defined its marketing operating model. It is the mean of 8 survey items across three dimensions, each rated 1–5.
This chapter presents the causal pathway through which MOM Presence shapes marketing outcomes, and the practical difference between organizations in the bottom versus top quartile of MOM Presence.
This is a partial mediation. That means Friction and Fluency explain some — but not all — of MOM’s effect on outcomes. Even after accounting for the reduction in Friction and the increase in Fluency that a stronger MOM produces, MOM Presence continues to predict better outcomes on its own. In other words, operating model strength matters independently of its contribution to reducing friction or increasing fluency.
These direct effects are significant across every outcome tested — WMO classification (+1.476***), revenue growth (+0.483***), and operating costs (+0.162***) — suggesting that a well-developed operating model creates value through additional mechanisms beyond the structural and psychological conditions it enables.
Figures 3.2–3.9 show how MOM Presence predicts each outcome across its 1–5 scale. Each curve is estimated from a separate regression model with firm size and age held at sample means (HQ sample, n=843). A rising curve means stronger MOM is associated with better performance on that outcome.
AI adoption is widespread in the sample. What varies significantly is what organizations get from it. This section examines whether having a stronger Marketing Operating Model changes the returns on AI investment, and finds that it does, particularly for business performance outcomes.
Each analysis tests whether an organizational condition changes the relationship between AI Readiness and marketing outcomes. The interaction term captures whether the condition strengthens or weakens AI returns. Highlighted rows indicate significant interactions (p<.05).
| Outcome | AI b | MOM Presence | MOM Pres×AI | Fit |
|---|---|---|---|---|
| Business Outcomes | ||||
| WMO rate | +0.059 | +1.708*** | +0.437** | McF R²=0.254 |
| Operating Cost | +0.028 | -0.079 | -0.011 | R²=0.080 |
| Revenue | +0.112*** | +0.483*** | -0.026 | R²=0.158 |
| Growth vs Peers | +0.059* | +0.395*** | -0.007 | R²=0.170 |
| People Outcomes | ||||
| NPS Promoter | +0.072 | +1.420*** | -0.036 | McF R²=0.180 |
| Friction | +0.002 | -0.299*** | -0.015 | R²=0.124 |
| Fluency | +0.031* | +0.471*** | -0.007 | R²=0.414 |
| Talent Cost | +0.008 | -0.171*** | -0.006 | R²=0.052 |
Of the conditions tested, MOM Presence is the only one that significantly moderates the AI Readiness → WMO relationship (interaction b=+0.437, p<.01). The practical range is large: among high-MOM organizations, WMO probability rises from 16% to 64% as AI Readiness increases — a 48-percentage-point span. Among low-MOM organizations, the same increase in AI Readiness produces no meaningful WMO improvement; the line runs near-flat or slightly declines. Neither Friction, Fluency, Fit, nor Embeddedness produced a significant interaction with AI Readiness on WMO. The implication is structural: AI investment requires operating model infrastructure to convert into competitive performance. Adoption without that infrastructure is not neutral — it appears to be associated with marginally worse outcomes at the business level.
The analysis above shows that MOM Presence moderates what organizations get from AI investment. But in practice, three distinct levers interact: AI Adoption (how broadly AI agents are used), AI Stewardship (the discipline of governing AI — strategy, data governance, human vs. AI decision authority, and performance measurement), and Operational Clarity (the organizational foundation in which AI operates).
The interactive below shows how each combination affects the percentage of Winning Marketing Organizations and Employee NPS across 843 firms. Select one, two, or all three levers to see the impact. The headline finding: organizations with all three levers in place achieve 84% WMO and +61 NPS, compared to 28% WMO and −36 NPS with none.
How well is your organization positioned to turn AI adoption into performance? The diagnostic below assesses three dimensions — your current level of AI adoption, the stewardship practices you have in place, and the operational clarity of your marketing function. It takes approximately 5 minutes and provides a personalized score with benchmarks from the study’s 843 firms.
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Companies have different approaches for defining their way of marketing. Our analysis of how 843 firms went about this process helped us identify four MOM archetypes based on their development method. The central finding is that there are some ways of building a MOM that are better than others. In particular, the Hybrid and Orchestrated archetypes achieve comparable people outcomes despite different approaches to formalization. This suggests that how deeply an organization embeds its MOM matters as much as how formally it documents it — though the data shows formalization is still required for full competitive performance.
Organizations cluster into four distinct operating model profiles based on how intentionally their model was designed, how formally it is documented, and how deeply it is embedded in day-to-day decisions. Outcome comparisons are shown in Figure 4.1 below.
No formal MOM is present. Marketing decisions are made through individual judgment, relationships, and accumulated habit. These organizations are the highest priority for MOM investment — the performance gap between Informal and Organic is the largest single step on the archetype ladder.
The operating model is not shared across the team, not referenced in decisions, and not visible to partners. Without a shared model, leadership change is likely to create immediate capability risk. Skews toward smaller and legacy organizations; informal MOMs persist through inertia rather than design.
“We are still figuring that part out.”
“Almost nonexistent. They are working on one but employee motivation to complete the task is lacking.”
“Lack of strategy. Reactive. Without vision. On demand of regular tasks.”
Working patterns have accumulated through years of practice rather than deliberate construction. The model is partially documented and moderately company-specific but lacks intentional governance. Friction and Fluency sit mid-range — neither the disorganization of Informal nor the enablement of Hybrid.
Most organizationally diverse archetype, spanning all firm sizes and ages. Functional marketing, but lacking the architecture to survive leadership change. Organic evolution rarely produces the deliberate design needed to progress without intentional intervention.
“In short, our marketing operating model: Strategy plans, Creative builds, Media launches, Data optimizes.”
“It exists roughly, and there are some levels of documentation, but it’s not extremely organized or consolidated.”
“We have a basic marketing operating model that provides a general framework for the work, but it still needs further development and standardization.”
High embeddedness alongside lower formalization than Orchestrated. The MOM is widely shared, referenced in decisions, included in onboarding, and known by partners. Deep embedding compensates for incomplete documentation — Hybrid matches Orchestrated on people outcomes at lower operating cost.
The WMO rate and NPS trail Orchestrated materially, indicating a performance ceiling that formalization would unlock. Embeddedness infrastructure is already in place — the gap to Orchestrated is governance, not culture or capability.
“We set goals to manage and analyze our work in order to drive sales and improve — it helps us work together as teams.”
“A balance: we do a hypothesis, look for that outcome. Based on results we will make change, within the budget.”
“Structured but evolving — clearly defined strategy, digital-first execution, data-driven decision making, with ongoing optimization across teams.”
The full investment in defining how marketing operates. The model is intentionally designed, highly company-specific, formally documented, and deeply embedded across decisions, onboarding, and partner relationships. Scores highest on all operating model character dimensions and on competitive business outcomes.
The combination of embedding and formalization is what produces the strongest competitive outcomes. Skews toward larger firms with the infrastructure to maintain a deliberate MOM; the qualitative data suggests that reaching this level in established organizations typically requires sustained executive commitment.
“Our Marketing Operating Model defines how marketing plans, executes, and measures work. It clarifies roles and process so we can stay aligned and accountable.”
“The model at my firm is a top-down approach. Each department and person has a documented approach for their responsibilities.”
“Our marketing operating model is built around strategy first, execution second, and performance always.”
| Sample Mean |
Informal 38.0% |
Organic 21.6% |
Hybrid 25.6% |
Orchestrated 14.8% |
|
|---|---|---|---|---|---|
| Business Outcomes | |||||
| WMO rate | 19.8% | 5.0% -14.8pp | 21.4% +1.6pp | 26.4% +6.6pp | 44.0% +24.2pp |
| Revenue 5%+ growth % with 5%+ growth (Q35 score 6-7; top 2 box) | 45.4% | 17.0% -28.4pp | 27.1% -18.3pp | 32.1% -13.3pp | 33.2% -12.2pp |
| Operating Cost Index lower = better | 1.00x | 0.97x -3% | 1.05x +5% | 0.98x -2% | 1.05x +5% |
| Growth above peers % scoring above peers on Q34 | 52.0% | 35.0% -17.0pp | 47.0% -5.0pp | 57.0% +5.0pp | 68.0% +16.0pp |
| People Outcomes | |||||
| NPS score % promoters minus % detractors (Q12) | +11.2 | -29.4 -40.6pts | +19.2 +8.0pts | +41.2 +30.0pts | +51.2 +40.0pts |
| High friction % % above sample mean; lower = better | 49.3% | 61.9% +12.6pp | 48.9% -0.4pp | 36.1% -13.2pp | 40.8% -8.5pp |
| High fluency % | 50.2% | 26.9% -23.3pp | 46.2% -4.0pp | 74.1% +23.9pp | 74.4% +24.2pp |
| Talent difficulty | 2.5 | 3.1 +0.6 | 2.6 +0.1 | 2.1 -0.4 | 2.3 -0.2 |
Hybrid achieves Orchestrated-level people outcomes at lower operating cost — yet falls short on competitive performance (WMO 26.4% vs 44.0%). Formalization converts embeddedness into full competitive advantage.
Informal shows the highest talent difficulty and lowest fluency — the absence of a shared model creates structural people risk independent of individual capability.
The step from Hybrid to Orchestrated is the highest-leverage intervention: embeddedness is already in place; governance converts people-outcome strength into full competitive performance.
Chapter 4 established that deliberate design, deep embeddedness, and formal documentation each independently shape outcomes — and that some combinations work significantly better than others. This chapter builds on that evidence to identify the specific design decisions, organizational conditions, and adoption barriers that most consistently predict stronger marketing performance.
Five dimensions of MOM design consistently predict better outcomes across NPS, WMO, and revenue growth. The table below summarizes what the data supports across Origin, Customization, Documentation, Governance, and Evolution.
Four structural properties of the MOM — Embeddedness, Fit, Systems Fragmentation, and Learning Discipline — each independently shape how strongly MOM Presence translates into business and people outcomes. The tables below report interaction models: each tests whether the design variable strengthens or weakens the MOM–outcome relationship.
Embeddedness measures how deeply the MOM is integrated into decisions, leadership behavior, onboarding, and external partner relationships. It has a strong independent effect on all outcomes and significantly amplifies MOM’s impact on NPS (interaction b=+1.406*).
Embeddedness index (1–5 scale). Higher = more deeply embedded. Mean of 5 items:
| Outcome | MOM Presence b p |
Embed Residual b p |
MOM × Embed Resid b p |
Fit |
|---|---|---|---|---|
| WMO Logit |
+2.136*** 0.000 |
+1.062** 0.002 |
+0.008 0.983 |
McF R²=0.261 |
| NPS Score OLS |
+1.086*** 0.000 |
+0.600*** 0.000 |
-0.037 0.815 |
R²=0.273 |
| Friction OLS |
-0.281*** 0.000 |
+0.050 0.444 |
+0.134 0.123 |
R²=0.068 |
| Fluency OLS |
+0.478*** 0.000 |
+0.143*** 0.000 |
-0.021 0.656 |
R²=0.393 |
| Operating Cost OLS |
+0.016 0.804 |
+0.262** 0.001 |
+0.286** 0.004 |
R²=0.084 |
| Revenue OLS |
+0.437*** 0.000 |
+0.299*** 0.000 |
-0.047 0.646 |
R²=0.157 |
| Talent Cost OLS |
-0.075 0.324 |
+0.231* 0.013 |
+0.203† 0.095 |
R²=0.039 |
All predictors mean-centered. Design variable = residual after partialling MOM Presence Index (orthogonal to MOM by construction). Controls: firm size, firm age. n≈453–497. WMO = logistic (log-odds); all others OLS with HC3 robust SE. † p<.10 * p<.05 ** p<.01 *** p<.001. Fit shares 38.7% variance with MOM Index (r=0.62); residual isolates the component of perceived fit beyond MOM formalization. No significant interactions.
Key insight: Embeddedness has strong independent main effects on WMO (**), NPS (***), Fluency (***), and Revenue (***). Using residuals to isolate the component of Embeddedness beyond MOM formalization restores MOM’s expected effects on Friction and Fluency (both ***). One significant interaction emerges: MOM × Embeddedness is significant for Operating Cost (b=+0.286**), meaning organizations with both strong MOM and deep embeddedness face higher operating cost exposure — likely reflecting the real investment required to embed a model deeply.
Fit measures how well the MOM reflects the specific needs, structure, and context of the organization. It predicts all six outcomes independently and is among the strongest consistent predictors across the full model.
Fit (1–7 scale). Higher = stronger fit between the MOM and current organizational needs. Single item:
| Outcome | MOM Presence b p |
Fit Residual b p |
MOM × Fit Resid b p |
Fit |
|---|---|---|---|---|
| WMO Logit |
+2.377*** 0.000 |
+0.894*** 0.000 |
-0.098 0.709 |
McF R²=0.263 |
| NPS Score OLS |
+1.164*** 0.000 |
+0.464*** 0.000 |
-0.107 0.258 |
R²=0.292 |
| Friction OLS |
-0.284*** 0.000 |
-0.039 0.348 |
+0.042 0.415 |
R²=0.063 |
| Fluency OLS |
+0.493*** 0.000 |
+0.104*** 0.000 |
+0.042 0.139 |
R²=0.401 |
| Operating Cost OLS |
+0.024 0.713 |
+0.001 0.978 |
+0.091 0.141 |
R²=0.028 |
| Revenue OLS |
+0.466*** 0.000 |
+0.172** 0.001 |
+0.071 0.249 |
R²=0.153 |
| Talent Cost OLS |
-0.064 0.404 |
+0.060 0.311 |
+0.117 0.113 |
R²=0.019 |
All predictors mean-centered. Design variable = residual after partialling MOM Presence Index (orthogonal to MOM by construction). Controls: firm size, firm age. n≈453–497. WMO = logistic (log-odds); all others OLS with HC3 robust SE. † p<.10 * p<.05 ** p<.01 *** p<.001. Fit shares 38.7% variance with MOM Index (r=0.62); residual isolates the component of perceived fit beyond MOM formalization. No significant interactions.
Key insight: Fit has consistent independent main effects on WMO (***), NPS (***), Fluency (***), and Revenue (***). Using residuals restores MOM’s full effects on Friction (***) and Fluency (***), which were suppressed in the original joint model. No significant interactions emerge — Fit contributes additively: organizations with a MOM that fits their context perform better regardless of MOM formalization level, but Fit does not change how strongly MOM translates into outcomes.
Systems Fragmentation measures how disconnected data, technology, and information systems are across the organization. Unlike Fit and Embeddedness, Fragmentation changes the strength of MOM’s relationship with specific outcomes rather than just shifting the starting point.
Fragmentation index (1–5 scale). Higher = more disconnected and siloed systems. Mean of 4 items:
| Outcome | MOM Presence b p |
SF Residual b p |
MOM × SF Resid b p |
Fit |
|---|---|---|---|---|
| WMO Logit |
+2.102*** 0.000 |
-0.153 0.463 |
+0.142 0.578 |
McF R²=0.245 |
| NPS Score OLS |
+1.343*** 0.000 |
-0.113 0.220 |
+0.153 0.186 |
R²=0.283 |
| Friction OLS |
-0.260*** 0.000 |
+0.719*** 0.000 |
+0.091*** 0.000 |
R²=0.813 |
| Fluency OLS |
+0.472*** 0.000 |
-0.187*** 0.000 |
+0.046 0.106 |
R²=0.485 |
| Operating Cost OLS |
+0.054 0.276 |
+0.480*** 0.000 |
+0.132* 0.014 |
R²=0.303 |
| Revenue OLS |
+0.472*** 0.000 |
-0.118* 0.029 |
+0.152* 0.024 |
R²=0.178 |
| Talent Cost OLS |
-0.097 0.125 |
+0.532*** 0.000 |
+0.085 0.211 |
R²=0.227 |
All predictors mean-centered. Design variable = residual after partialling MOM Presence Index (orthogonal to MOM by construction). Controls: firm size, firm age. n≈453–497. WMO = logistic (log-odds); all others OLS with HC3 robust SE. † p<.10 * p<.05 ** p<.01 *** p<.001. Fit shares 38.7% variance with MOM Index (r=0.62); residual isolates the component of perceived fit beyond MOM formalization. No significant interactions.
Key insight: Systems Fragmentation is the dominant structural driver of organizational Friction (R²=0.813, b=+0.719***) — more so than MOM formalization itself. Three significant interactions emerge: MOM’s ability to reduce Friction (b=+0.091***), lower Operating Cost (b=+0.132*), and drive Revenue (b=+0.152*) is weakened in highly fragmented environments. Organizations that invest in their operating model without addressing systems fragmentation will see diminished returns. Fragmentation requires direct structural intervention in technology integration and data infrastructure — MOM strength alone cannot overcome it.
Learning Discipline captures whether the organization has systematic mechanisms for adapting and improving marketing work. The analysis uses residuals of Learning Discipline after partialling out MOM Presence Index, isolating the independent contribution of LD beyond what MOM already explains.
Learning Discipline index (1–5 scale), reported as residuals after removing overlap with MOM Presence. Higher = stronger systematic adaptation mechanisms. Mean of 4 items:
| Outcome | MOM Presence b p |
LD Residual b p |
MOM × LD Residual b p |
Fit |
|---|---|---|---|---|
| WMO Logit |
+2.158*** 0.000 |
+0.690* 0.045 |
+0.939† 0.069 |
McF R²=0.279 |
| NPS Score OLS |
+1.392*** 0.000 |
+0.808*** 0.000 |
−0.034 0.862 |
R²=0.323 |
| Friction OLS |
−0.314*** 0.000 |
−0.176* 0.010 |
+0.030 0.746 |
R²=0.095 |
| Fluency OLS |
+0.501*** 0.000 |
+0.206*** 0.000 |
+0.021 0.692 |
R²=0.442 |
| Operating Cost OLS |
+0.032 0.576 |
−0.109 0.189 |
−0.002 0.983 |
R²=0.030 |
| Revenue OLS |
+0.514*** 0.000 |
+0.233** 0.008 |
+0.089 0.456 |
R²=0.179 |
| Talent Cost OLS |
−0.131 0.060 |
−0.071 0.472 |
+0.059 0.666 |
R²=0.015 |
All predictors mean-centered. Design variable = residual after partialling MOM Presence Index (orthogonal to MOM by construction). Controls: firm size, firm age. n≈453–497. WMO = logistic (log-odds); all others OLS with HC3 robust SE. † p<.10 * p<.05 ** p<.01 *** p<.001. Fit shares 38.7% variance with MOM Index (r=0.62); residual isolates the component of perceived fit beyond MOM formalization. No significant interactions.
Key insight: Learning Discipline, measured here as the component independent of MOM Presence, has significant main effects on WMO (*), NPS (***), Friction (*), Fluency (***), and Revenue (**). Organizations with stronger learning mechanisms achieve better outcomes above and beyond what their MOM formalization alone predicts. The interaction term is non-significant for most outcomes, with the exception of WMO (b=+0.939, p=.069†) — a marginal signal that higher LD may amplify MOM’s effect on the probability of being a Winning Marketing Organization. Taken together, Learning Discipline contributes additively to MOM: it does not require MOM to work, but the combination is associated with the strongest competitive performance.
Among organizations without a formal MOM (n=120), ten barriers were assessed. The table below ranks them not by how frequently they are cited, but by how strongly each barrier is associated with lower MOM development across the full sample — the barriers most correlated with absence of a MOM, controlling for firm size and age.
| Barrier | % Citing Among non-adopters |
Impact on MOM development OLS coefficient (higher = stronger drag) |
|---|---|---|
| Marketing lacks the influence to drive adoption | 87% |
|
| Markets change too fast for formal models to be useful | 87% |
|
| Skeptical that formal models would improve performance | 87% |
|
| Formal models would slow us down | 87% |
|
| Culture resists formal processes and documentation | 88% |
|
| Unclear what an operating model actually is | 88% |
|
| Leadership doesn't see the value or prioritize it | 90% |
|
| Lack the time and resources to develop one | 90% |
|
| Current informal approach works well enough | 89% |
|
| Too small and don't need formal structures | 91% |
|
Barriers cited by n=120 respondents without a formal MOM. Impact coefficient from OLS regression predicting MOM presence (Q24, 0-100 scale), controlling for firm size and age. Ranked by coefficient strength. n=843.
Key insight: The three strongest barriers — marketing’s lack of influence to drive adoption, pace-of-change skepticism, and performance skepticism — reflect beliefs about MOM’s value and organizational positioning, not resource constraints. These are perceptual barriers, not structural ones. The evidence base in this report directly addresses each of these beliefs: Chapter 3 shows MOM significantly improves performance even in fast-changing environments, and the archetype data in Chapter 4 shows that marketing influence tends to grow alongside MOM development.
The table below shows how the strength of MOM’s effect on NPS and WMO changes depending on whether each organizational or environmental condition is present or absent. Split-sample logistic regressions were run separately for organizations with and without each condition.
The chart below maps each organizational and environmental condition by whether it amplifies MOM’s structural effects — reducing Friction (x-axis, right = more reduction) and boosting Fluency (y-axis, up = more boost). Models use MOM_Index (1–5) interacted with each binary condition. OLS with HC3 robust SE, no controls, n=843.
x-axis: interaction coefficient on Friction (negated — right = MOM reduces friction more). y-axis: interaction coefficient on Fluency (up = MOM boosts fluency more). Highlighted rings = significant at p<.05.
MOM_Index (1–5) interacted with binary condition (0/1). All predictors mean-centered. OLS HC3 robust standard errors. No controls. n=843.
Key insight: Organizational conditions that reflect structural readiness — having the right org structure (b=−0.214** on friction) and diverse talent (b=−0.148* on friction) — amplify MOM’s ability to reduce structural friction. Highly regulated industries uniquely amplify MOM’s Fluency benefit (b=+0.131**), suggesting that regulatory clarity creates conditions where shared operating frameworks translate more readily into individual psychological enablement. Customer-led growth organizations see a weaker friction-reduction effect from MOM, indicating that growth-oriented complexity may require additional structural intervention beyond the operating model itself.
This report is the product of a collaborative research effort between MarCaps and the MMA, drawing on survey data from 843 senior marketing leaders collected in March 2026. The study was designed to examine the structural and psychological conditions that shape marketing effectiveness, and to test the role of the Marketing Operating Model as a driver of organizational performance.
The sample includes 843 respondents selected based on attention checks, response time, and overall response quality. Marketing organization performance is assessed across two dimensions. People outcomes are anchored by the Net Promoter Score (NPS). Business outcomes are anchored by the WMO (Winning Marketing Organization) index, which classifies firms in the top quartile of marketing performance. MOM Presence is an 8-item index covering the three dimensions described in the report — Strategic Mandate, Organizational Priorities, and Management Infrastructure (α=0.892). Archetypes were derived directly from the data using k-means clustering on MOM Presence, embeddedness, and MOM character dimensions. All regressions control for firm size and firm age.
This assessment helps marketing leaders understand whether their operating model needs attention and where to focus first. Twenty-four statements cover three areas: organizational conditions, operating model presence, and operating model quality. For each statement, drag the marker to indicate how well it describes your organization. You will get an automated report with your results compared to the MarCaps benchmarks from the study.
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