The Accumulation Problem
MarTech stack sprawl does not happen in a single dramatic decision. It happens one tool at a time, one budget cycle at a time, one well-intentioned pilot at a time. A social listening platform here, a personalisation engine there, a landing page builder that marketing liked better than the one already in the stack, an analytics tool that a new VP brought from their previous company. Each individual acquisition is rational. The aggregate result is not.
MarTech.org's recent examination of the hidden costs of MarTech sprawl catalogs the growing concern among marketing leaders about tool proliferation. The data supports their anxiety. Gartner's 2025 Marketing Technology Survey found that the average enterprise marketing organisation uses 91 distinct tools — up from 68 just three years prior. Yet utilisation rates tell the real story: only 33 percent of MarTech capabilities are fully utilised, according to the same survey. The remaining two-thirds represent a staggering amount of wasted expenditure, operational complexity, and unrealised potential.
This article argues that stack sprawl is not merely a budget problem. It is an architectural, operational, and strategic problem whose true costs are systematically underestimated because they are distributed across categories that no single budget owner is accountable for. Understanding those costs — and then applying a disciplined rationalization methodology — is one of the highest-return investments a marketing operations leader can make.
Anatomy of Sprawl: How Stacks Grow Beyond Control
Before addressing remediation, it is worth understanding the mechanisms that produce sprawl. They are not random; they are predictable consequences of how enterprise marketing organisations are structured and incentivised.
The Point Solution Reflex
When a marketing team encounters a capability gap, the instinctive response is to acquire a tool that addresses it. Need better landing pages? Buy a landing page builder. Need webinar capabilities? Buy a webinar platform. Need intent data? Buy an intent data provider. Each acquisition solves the immediate problem, but each also introduces a new integration requirement, a new login, a new vendor relationship, a new contract renewal, and a new data silo.
The point solution reflex is reinforced by the SaaS model's low barriers to entry. Free trials, monthly subscriptions, and product-led growth motions mean that individual contributors and team leads can adopt tools without formal procurement involvement. Shadow IT in marketing is not an aberration; it is the norm.
The Legacy Accumulation Effect
Enterprise marketing stacks have histories. Mergers and acquisitions bring overlapping tools from acquired companies. Leadership transitions bring preference-driven replacements that coexist with their predecessors during indefinite "transition" periods. Platform migrations — from one marketing automation system to another, for instance — leave behind partially decommissioned systems that continue to run automated campaigns and hold historical data long after the migration was supposed to be complete.
Organisations that approach platform migration without a deliberate decommissioning plan — a challenge we address comprehensively in our enterprise platform migration playbook — for legacy systems reliably end up operating — and paying for — both the old and new platforms simultaneously, sometimes for years.
The Overlapping Capabilities Problem
Modern MarTech platforms have expanded their feature sets aggressively. A marketing automation platform now includes landing page builders, social media schedulers, basic analytics, and rudimentary CRM functionality. A CRM now includes email marketing, basic automation, and reporting. A CDP includes segmentation, activation, and analytics. The result is that enterprises pay for the same capabilities multiple times across different platforms — not because they chose to, but because feature expansion made overlap inevitable.
A marketing team might, for example, have landing page capabilities in their marketing automation platform, their CMS, their ABM platform, and a dedicated landing page builder. Each was acquired for legitimate reasons. None has been retired. The organisation pays four license fees for a capability it needs once.
The True Cost Taxonomy
Stack sprawl costs are typically discussed in terms of license fees — the most visible and easily quantified expense. But license fees represent a fraction of the true cost. A comprehensive cost taxonomy reveals at least six distinct cost categories.
Direct License Costs
This is the obvious one. Each tool in the stack carries a license fee, typically structured as an annual subscription based on user seats, contact volume, or feature tier. For enterprise marketing organisations, the aggregate annual spend across all tools routinely exceeds $500,000 and often exceeds $2 million.
The less obvious aspect of license costs is the renewal trap. SaaS contracts typically auto-renew, and procurement teams are rarely equipped to evaluate whether each tool's utilisation justifies its cost at renewal time. Tools that were critical when acquired may have been superseded by capabilities added to the core platform, but nobody has done the analysis to confirm this. The default action — renewal — prevails over the correct action — consolidation.
Integration Costs
Every tool in the stack must exchange data with other tools to be useful. Integrations require development effort to build, testing effort to validate, monitoring effort to maintain, and remediation effort when they break. The cost per integration varies widely — a pre-built connector might require a few hours of configuration, while a custom API integration might require weeks of development — but the aggregate integration cost across a sprawling stack is substantial.
More insidiously, integration complexity grows non-linearly. A stack of ten tools has up to 45 possible pairwise integrations. A stack of twenty tools has up to 190. A stack of ninety tools has up to 4,005. Not every pair needs to be integrated, of course, but the combinatorial explosion means that each additional tool increases the integration burden disproportionately.
Investing in robust data management and integration architecture can contain this complexity, but only if the investment is made deliberately rather than reactively.
Data Fragmentation Costs
Every tool maintains its own data store. When a contact interacts with a landing page built in one tool, opens an email sent from another tool, attends a webinar hosted on a third tool, and has their intent signals captured by a fourth tool, the holistic view of that contact's engagement exists nowhere. It must be assembled through integrations, and every integration introduces latency, mapping complexity, and potential data loss.
Data fragmentation undermines the three capabilities that matter most in modern marketing: personalisation (which requires a complete view of the customer), attribution (which requires a complete view of the journey), and compliance (which requires a complete view of consent). Organisations that cannot achieve these capabilities are not merely inefficient; they are strategically impaired.
Cognitive Costs
This is the most underappreciated cost category. Every tool in the stack requires its users to understand its interface, its logic, its terminology, and its quirks. Marketing operations professionals who must operate across fifteen or twenty tools carry an enormous cognitive load. Context-switching between platforms consumes time and mental energy, increases error rates, and degrades the quality of work produced.
The cognitive cost also manifests in onboarding. A new marketing operations hire joining an organisation with a sprawling stack faces months of ramp-up time before they can operate independently across all systems. In a tight labour market for marketing operations talent, this extended onboarding period is a competitive disadvantage.
Opportunity Costs
Time spent managing, integrating, and troubleshooting a sprawling stack is time not spent on activities that drive business outcomes. Every hour a marketing operations professional spends debugging a broken integration or reconciling data across platforms is an hour they could have spent optimising campaign operations, improving lead scoring models, or developing personalisation strategies.
The opportunity cost is compounding. An organisation that spends 40 percent of its marketing operations capacity on stack management has 40 percent less capacity for value-creating work than a competitor with a rationalized stack. Over years, this productivity gap translates into a meaningful competitive disadvantage.
Security and Compliance Costs
Every tool in the stack is a potential attack surface. Each vendor requires security assessment, data processing agreements, and ongoing compliance monitoring. Each tool that stores personal data must be documented in data processing inventories, included in data subject access request workflows, and governed under the organisation's privacy compliance framework.
The administrative burden of managing security and compliance across a sprawling stack is substantial — and the risk exposure is multiplicative. A data breach in any single tool compromises customer trust and triggers regulatory obligations regardless of how well-secured the other tools are.
The Rationalization Methodology
Stack rationalization is not a one-time cleanup project. It is a disciplined, ongoing practice that requires clear methodology, executive sponsorship, and cross-functional participation. The following framework, refined through dozens of enterprise engagements, provides a systematic approach.
Phase 1: Inventory and Categorisation
The first step is deceptively difficult: produce a complete inventory of every tool in the marketing technology stack. This means not just the tools that marketing operations manages directly, but also the tools adopted by content teams, demand generation teams, social media teams, event teams, and regional marketing teams. Shadow IT must be surfaced.
The inventory should capture, at minimum:
- Tool name and vendor
- Primary function (what problem it was acquired to solve)
- Annual cost (license, integration, and support)
- Contract renewal date
- Number of active users (not licensed seats — actual active users)
- Data types processed (personal data, behavioural data, transactional data)
- Integrations (which other tools it connects to and how)
- Owner (the individual accountable for the tool)
Most organisations find this exercise revelatory. It is common to discover tools that nobody knew the organisation was still paying for, tools with single-digit active users, and tools whose functions have been entirely superseded by capabilities in the core platform.
Phase 2: Utilisation Assessment
With the inventory complete, the next step is to assess utilisation — not just whether a tool is being used, but how much of its capability is being leveraged. A tool might be active in the sense that people log into it, but utilised only in the sense that they use 10 percent of its features.
Utilisation assessment requires a combination of quantitative data (login frequency, feature usage metrics, API call volume) and qualitative input (user interviews to understand which features are valued and which are ignored). The goal is to classify each tool into one of four categories:
Core: High utilisation, high strategic value. These tools form the foundation of the stack and should be retained, optimised, and invested in.
Consolidation candidate: The tool's functions overlap with capabilities available in a core platform. Migrating the tool's users to the core platform would eliminate license, integration, and cognitive costs without sacrificing capability.
Underutilised: The tool has strategic value but is not being fully leveraged. The response is not rationalisation but enablement — training, configuration optimisation, and workflow redesign to unlock the tool's potential.
Redundant: The tool serves no unique function and has low utilisation. It should be decommissioned.
Phase 3: Capability Mapping
Before making rationalization decisions, the organisation must map required capabilities to available platforms. This is the analytical heart of the methodology.
Create a matrix with required marketing capabilities on one axis and available platforms on the other. For each intersection, assess whether the platform provides the capability natively, through an add-on, through an integration, or not at all. Rate each capability delivery on dimensions of functionality, scalability, usability, and cost.
This exercise often reveals that the core marketing automation platform — whether Oracle Eloqua, Adobe Marketo, Salesforce Marketing Cloud, or HubSpot — provides many capabilities that the organisation is currently sourcing from point solutions. A marketing automation platform's native landing page builder might be slightly less feature-rich than a dedicated landing page tool, but if it meets 90 percent of the organisation's requirements and eliminates a separate license, integration, and data silo, the trade-off is overwhelmingly positive.
A thorough platform maturity assessment can accelerate this phase significantly by establishing a clear picture of what the core platform is and is not capable of delivering.
Phase 4: Rationalization Roadmap
With inventory, utilisation, and capability mapping complete, the organisation can construct a prioritised rationalization roadmap. Prioritisation should be driven by three factors:
Contract timing: Tools approaching renewal represent the lowest-friction rationalization opportunities. Decommissioning a tool mid-contract wastes remaining license investment; timing rationalization to contract end dates maximises financial benefit.
Integration complexity: Tools with minimal integration dependencies are easier to decommission than tools deeply embedded in data flows. Start with loosely coupled tools to build organisational momentum and refine the decommissioning process before tackling deeply integrated systems.
User impact: Rationalizing a tool with two users requires minimal change management. Rationalizing a tool with two hundred users requires a comprehensive migration plan, training programme, and communication strategy. Sequence rationalization to build from low-impact to high-impact changes.
The roadmap should specify, for each tool targeted for rationalization:
- Target date (aligned to contract renewal)
- Migration plan (how users and data will transition to the replacement platform)
- Data disposition (how historical data will be preserved, migrated, or archived)
- Communication plan (how affected users will be notified and supported)
- Success metrics (how the organisation will verify that the consolidated platform delivers equivalent or superior capability)
Phase 5: Execution and Governance
Rationalization execution requires disciplined project management and stakeholder management in equal measure. The most common failure mode is not technical — it is political. Tool owners resist decommissioning because they perceive it as a diminution of their domain. Users resist migration because they have invested time in learning the existing tool and fear productivity loss during transition.
Executive sponsorship is essential. The CFO's interest in cost reduction and the CIO's interest in security simplification are natural allies for the marketing operations leader driving rationalization. Frame the initiative in terms that resonate with each stakeholder: cost savings for finance, reduced attack surface for security, faster time-to-market for marketing leadership.
Post-rationalization, governance mechanisms must prevent re-sprawl. As we argue in our perspective on marketing automation governance, the absence of formal governance frameworks is what allows sprawl to take root in the first place. A tool acquisition policy — requiring a business case, a capability overlap assessment, and marketing operations approval before any new tool can be added to the stack — is the minimum viable control. More mature organisations establish a MarTech governance board that reviews the stack quarterly, evaluates new tool requests against existing capabilities, and maintains the capability map as a living document.
Integrating this governance function with broader platform expertise and performance monitoring ensures that rationalization gains are sustained rather than gradually eroded.
The Core Platform Strategy
Stack rationalization is not merely about removing tools. It is about strengthening the core platforms that remain. The most effective rationalization initiatives are paired with deliberate investment in maximising the value of the core stack.
For most enterprise marketing organisations, the core stack comprises four to six platforms: a marketing automation platform, a CRM, a content management system, an analytics platform, and potentially a CDP and an ABM tool. These core platforms should receive disproportionate investment in configuration, optimisation, training, and integration.
The principle is concentration of capability. Every function that can be performed within a core platform should be performed there, even if a point solution would provide a marginally superior experience for that specific function. The marginal capability gain from a point solution is almost never worth the integration, data fragmentation, and cognitive costs it introduces.
This is where deep platform expertise becomes decisive. Organisations that fully understand their marketing automation platform's capabilities — including features they have never activated — consistently discover that 30 to 50 percent of their point solutions are addressing capability gaps that do not actually exist. Programmes like structured Eloqua training or platform-specific optimisation engagements regularly surface capabilities that eliminate the need for one or more ancillary tools.
The AI Dimension: Rationalizing for the Next Generation
The rise of AI-powered marketing capabilities adds urgency to the rationalization imperative. Every major platform vendor is embedding generative AI, predictive analytics, and intelligent automation into their core offerings. Adobe's Sensei, Salesforce's Einstein, HubSpot's AI assistants, and Oracle's AI capabilities are transforming what core platforms can deliver natively.
This means that many point solutions — particularly those providing analytics, content optimisation, send-time optimisation, and predictive scoring — will find their differentiation absorbed into the core platforms within the next two to three years. Organisations that rationalise proactively, consolidating these capabilities into their core platforms as the AI features mature, will avoid paying twice for the same functionality during the transition.
Conversely, organisations that ignore rationalization will find that AI compounds their sprawl problem. Every AI tool requires training data; fragmented data produces fragmented AI; fragmented AI produces inconsistent customer experiences. The organisations best positioned to leverage enterprise AI capabilities are those with consolidated stacks that provide clean, comprehensive data to AI models.
Measuring Rationalization Success
Rationalization success should be measured across four dimensions:
Cost reduction: The most straightforward metric. Calculate total eliminated license, integration, and support costs against the investment required for the rationalization initiative. Well-executed rationalization programmes typically achieve 25 to 40 percent reduction in total MarTech spend within eighteen months.
Operational velocity: Measure time-to-market for standard campaign types before and after rationalization. Reduced tool count means reduced integration complexity, fewer handoffs, and faster execution. A 20 to 30 percent improvement in campaign velocity is a realistic target.
Data quality: Measure duplicate rates, field completion rates, and data latency across the consolidated stack. Fewer data silos means fewer reconciliation failures and fresher data. Improved data quality compounds across every activity that depends on it — personalisation, scoring, attribution, and compliance.
Team satisfaction: Survey marketing operations practitioners on their experience of the technology environment. Reduced cognitive load, fewer context switches, and clearer workflows directly improve job satisfaction and retention — outcomes that matter enormously in a competitive talent market.
The Discipline of Less
MarTech stack rationalization runs against the grain of an industry that celebrates expansion, innovation, and novelty. The annual MarTech conference showcases hundreds of new tools. Industry analysts publish landscape reports that grow larger every year. Vendor marketing relentlessly positions each new tool as the solution to a previously unrecognised problem.
Resisting this expansion imperative requires discipline — the discipline to ask, before every potential tool acquisition, whether the organisation's existing platforms can meet the need. The discipline to invest in optimising existing capabilities before acquiring new ones. The discipline to decommission tools that have served their purpose rather than letting them linger indefinitely.
This discipline is not anti-innovation. It is pro-coherence. A rationalized stack with deeply configured, well-integrated, fully utilised core platforms will outperform a sprawling stack of underutilised point solutions on every metric that matters: cost, velocity, data quality, compliance, and customer experience.
The hidden cost of MarTech stack sprawl is not a mystery. It is a choice — a choice that most organisations make passively, one tool at a time, without ever confronting the aggregate consequence. The organisations that choose differently — that treat stack architecture as a strategic discipline rather than an accidental accumulation — will find that less technology, better deployed, produces more value than the industry's relentless expansion would have you believe.
The rationalization playbook is clear. The question, as always, is whether the organisation has the discipline to execute it.
