You are probably paying for tools nobody uses
The martech landscape has grown from roughly 150 tools in 2011 to over 14,000 in 2024 (Chiefmartec). That is not a typo. The number of marketing technology products available to B2B companies has grown nearly 100x in just over a decade, and every one of those vendors has a sales team telling you that their tool is the one missing piece in your stack.
The result is predictable. The average enterprise uses 27 or more cloud-based marketing products (MarketingOps.com). Most teams use less than 40% of the features they are paying for. And 94% of sales organizations plan to consolidate their tech stacks in the near term (Salesforce State of Sales report). The marketing side of the house is no different; it is just less likely to admit the problem.
Martech sprawl is not just a budget issue, though the direct costs are significant. It creates data fragmentation, integration overhead, training gaps, and operational complexity that slows down the very marketing machine these tools were supposed to accelerate. Every tool you add introduces another data silo, another integration to maintain, another login for your team to manage, and another renewal negotiation on your calendar.
This post gives you the framework for auditing your martech stack, deciding what to keep, consolidate, or cut, and building a rationalized technology architecture that actually serves your marketing operations.
The cost of martech sprawl
The costs come in three categories, and only one of them shows up directly on your P&L.
Direct costs: license fees for underutilized tools
This is the obvious one. If you pay $50,000/year for a marketing attribution platform that three people log into and nobody trusts, that is $50,000 in waste. Multiply this across a dozen underutilized tools and the numbers add up fast.
A practical exercise: pull up every marketing tool contract your company has. Calculate the per-user cost and compare it to actual monthly active users. Most companies discover that 20-30% of their martech budget goes to tools with utilization rates below 50%.
Indirect costs: integration maintenance, data inconsistency, and training overhead
These costs are harder to quantify but often exceed the direct costs:
Integration maintenance. Every tool that connects to your CRM or marketing automation platform requires an integration. Integrations break. APIs change. Data mappings drift. Your ops team spends hours every month troubleshooting sync failures, resolving data conflicts, and rebuilding connections that silently stopped working.
Data inconsistency. When the same data lives in multiple systems (your ABM platform has one account list, your MAP has another, your enrichment tool has a third), discrepancies are inevitable. Which system has the correct industry classification? Which has the most current contact data? Nobody knows, and the time spent reconciling conflicting data across systems is time not spent running campaigns.
Training overhead. Every tool requires onboarding for new team members, ongoing training as features change, and institutional knowledge about how it is configured. When a MOps team member leaves, the knowledge of how 15+ tools are configured, integrated, and used often leaves with them. The replacement cost is not just the new hire's salary; it is the months of productivity loss while they learn the stack.
Opportunity cost: tools over execution
This is the most insidious cost. Every hour your marketing operations team spends managing, configuring, and troubleshooting tools is an hour not spent on the work that drives pipeline: building campaigns, optimizing conversion rates, improving attribution, and refining lead scoring.
Teams with bloated stacks tend to become tool administrators instead of revenue operators. The stack becomes the job, rather than the means to do the job.
The martech stack audit framework
A martech audit is not a one-time cleanup project. It is a structured assessment that should happen annually, with lighter quarterly reviews in between. Here is the five-step framework.
Step 1: Inventory everything
Before you can rationalize, you need to know what you have. Most companies do not have a complete, current inventory of their marketing tools. Shadow IT purchases, free-tier tools that evolved into paid subscriptions, and tools inherited from acquisitions all contribute to an incomplete picture.
Build a comprehensive inventory with the following fields for each tool:
| Field | What to Capture |
|---|---|
| Tool name | Official product name |
| Category | Function the tool serves (automation, analytics, ABM, etc.) |
| Annual cost | Total annual contract value including all seats and tiers |
| Contract owner | Who signed the contract and manages the vendor relationship |
| Technical owner | Who configures and maintains the tool day-to-day |
| Licensed users | Total seats or licenses purchased |
| Active users (90-day) | Users who logged in at least once in the past 90 days |
| Primary integrations | What other systems does this tool connect to? |
| Data flow direction | Does data flow in, out, or bidirectionally? |
| Renewal date | When the contract renews (critical for timing decisions) |
Where to find tools you might not know about: check your SSO provider's application list, review expense reports for software charges, audit your CRM and MAP integration logs, and ask team leads directly. You will almost certainly discover tools that nobody in operations knew about.
Step 2: Categorize by function
Group every tool into its primary functional category. This reveals overlap immediately. Common categories for B2B marketing stacks:
Marketing automation. Platforms like Marketo, HubSpot, Pardot (Account Engagement), or ActiveCampaign. This is the core execution layer for email, nurture programs, landing pages, and lead management.
CRM. Salesforce, HubSpot CRM, Dynamics 365. The system of record for accounts, contacts, and opportunities. (CRM is shared with sales, but marketing ops often has significant configuration responsibilities.)
Analytics and BI. Tableau, Looker, Google Analytics, Amplitude, Mixpanel. Tools for measuring performance, building dashboards, and analyzing funnel data.
Account-Based Marketing (ABM). 6sense, Demandbase, Terminus, RollWorks. Platforms for identifying, targeting, and engaging specific accounts.
Content management. CMS platforms (WordPress, Contentful, Webflow), DAM tools, and content collaboration platforms.
Social media management. Hootsuite, Sprout Social, Buffer. Publishing, scheduling, and analytics for social channels.
Intent data and enrichment. Bombora, G2, TrustRadius (intent). ZoomInfo, Clearbit, Apollo, Cognism (enrichment). These tools provide the data layer that powers targeting, scoring, and personalization.
Attribution. Bizible (Marketo Measure), HubSpot attribution, CaliberMind, Dreamdata. Tools for tracking and modeling marketing's contribution to revenue.
Advertising and paid media. LinkedIn Campaign Manager, Google Ads, programmatic platforms, retargeting tools. Campaign management for paid channels.
Conversation and chat. Drift, Intercom, Qualified. Tools for engaging website visitors in real time.
When you see two or three tools in the same category, that is your first signal of potential consolidation.
Step 3: Assess utilization
For each tool, answer three questions:
What percentage of features are actually used? Most marketing platforms have extensive feature sets. If you bought an enterprise ABM platform but only use it for display advertising, you are paying for capabilities you do not use. Estimate the percentage of the tool's core features that your team actively relies on.
How many users have logged in during the last 90 days? Pull login data from the tool's admin panel or your SSO provider. If a tool has 20 licensed seats and 4 active users, utilization is 20%. Some tools charge per seat; others charge flat rates. For per-seat tools, low utilization means you are overpaying. For flat-rate tools, low user count may indicate the tool is not embedded in your team's workflow.
Are there overlapping capabilities between tools? This is the key question. If your MAP has built-in landing page functionality but you also pay for a separate landing page tool, do you need both? If your ABM platform includes intent data but you also have a standalone intent data subscription, is the overlap justified?
Step 4: Evaluate integration health
A tool is only as valuable as its ability to share data with the rest of your stack. For each tool, assess:
CRM connectivity. Does the tool sync data with your CRM? Is the integration native, third-party (via Zapier, Workato, etc.), or non-existent? Native, bidirectional integrations are the gold standard. One-way integrations are acceptable for specific use cases. No integration means the tool creates a data silo.
Data flow direction and reliability. Is data flowing in both directions, or only from one system to the other? How often does the sync run? Are there known issues with data lag, duplicate creation, or field mapping errors?
Data consistency. Pull the same record from your CRM and the marketing tool. Do the fields match? Discrepancies between systems indicate integration problems that are silently degrading your data quality.
For a deeper dive into CRM data health and integration best practices, see the CRM data hygiene guide and the RevOps tech stack guide.
Step 5: Decide: keep, consolidate, or cut
With the inventory, categorization, utilization, and integration data in hand, categorize each tool into one of three buckets:
Keep. The tool has high utilization (60%+ of features used, 70%+ of seats active), serves a critical function that no other tool in the stack covers, and integrates well with your core systems. These are your stack anchors.
Consolidate. The tool overlaps with another tool in the stack, and one of the two can serve both purposes. The most common consolidation opportunities:
- MAP landing pages replacing a standalone landing page tool
- CRM-native reporting replacing a lightweight analytics tool
- ABM platform intent data replacing a standalone intent data subscription
- All-in-one platforms (like HubSpot) consolidating point solutions
Cut. The tool has low utilization (under 40% of features used or under 50% of seats active), serves a non-critical function, or is poorly integrated. If removing the tool would not meaningfully impact marketing's ability to generate pipeline, it is a cut candidate.
Document the rationale for each decision. You will need it when the tool's internal champion pushes back, and someone always pushes back.
The core martech stack: what you actually need
Not every company needs 27 tools. Here is a tiered framework for building a rationalized stack based on what actually drives pipeline.
Tier 1: Essential (every B2B marketing team needs these)
Marketing automation platform. This is the execution engine: email, nurture, landing pages, forms, lead scoring, and campaign management. Marketo for enterprise complexity, HubSpot for mid-market and SMB, Pardot for Salesforce-native shops. This is not optional; it is the core of marketing operations.
CRM. Salesforce for mid-market and enterprise. HubSpot CRM for SMB and lower mid-market. The CRM is the shared system of record across marketing, sales, and CS. Everything integrates with it. For more on how the CRM connects to the broader tech stack, see the RevOps tech stack guide.
Analytics. Google Analytics (or GA4) for web analytics. A BI tool (Looker, Tableau, or CRM-native dashboards) for pipeline and revenue reporting. You need both web-level and business-level analytics; they answer different questions.
These three tools form the foundation. If you are starting from scratch or doing a ground-up rationalization, get these right before adding anything else.
Tier 2: High value (meaningful pipeline impact for most B2B companies)
ABM platform. 6sense, Demandbase, or RollWorks. For companies selling to mid-market and enterprise accounts, ABM platforms provide account identification, intent scoring, and targeted advertising that inbound marketing alone cannot replicate. The ROI depends on your ACV; companies with ASPs above $25K typically see the strongest returns.
Intent data. Bombora, G2, or similar. Intent data tells you which companies are actively researching your category, enabling marketing to prioritize accounts showing buying signals. Often bundled with ABM platforms, but standalone subscriptions work too.
Data enrichment. ZoomInfo, Clearbit, Apollo, or Cognism. Enrichment fills in firmographic and contact data that powers segmentation, scoring, and personalization. The value is directly proportional to your CRM data quality; enrichment on top of clean data is powerful, enrichment on top of garbage just creates well-formatted garbage.
Tier 3: Nice to have (valuable for specific programs or at scale)
Conversation/chat. Drift, Qualified, or Intercom. Valuable for converting high-intent website visitors in real time, particularly on pricing and demo pages. The ROI depends on traffic volume; sites with under 10,000 monthly visitors may not generate enough conversations to justify the investment.
Content experience. PathFactory, Uberflip, or similar. Content experience platforms personalize content journeys for prospects. Most valuable for companies with large content libraries and content-heavy buyer journeys.
Social media management. Hootsuite, Sprout Social. Necessary if you publish frequently across multiple social channels. If your social presence is primarily LinkedIn and you publish 3-4 times per week, native platform tools may suffice.
Advanced attribution. Bizible (Marketo Measure), Dreamdata, or CaliberMind. For companies spending $1M+ on marketing annually, dedicated attribution tools provide the modeling sophistication that native MAP attribution cannot. Below that spend level, MAP-native or CRM-native attribution is usually sufficient.
The principle: start with Tier 1, add Tier 2 when the use case is clear and the budget allows, and be selective about Tier 3. Every tool you add is a tool you have to maintain, integrate, train on, and eventually rationalize again.
Making the transition
Deciding to cut or consolidate a tool is the easy part. Executing the transition without breaking workflows, losing data, or angering stakeholders is harder.
Never cut a tool without a migration plan for its data
If the tool you are removing contains historical data (campaign performance, engagement history, attribution records), that data needs to go somewhere before the subscription ends. Export it, store it in your data warehouse, or migrate it to the replacement tool. Once the contract expires and the account is deactivated, the data is gone.
Specific data to preserve: historical email performance metrics, campaign member records, engagement scoring data, and any custom reports or dashboards that stakeholders reference regularly.
Communicate changes to users before making them
The fastest way to lose credibility during a stack rationalization is to surprise people. If a team member opens their browser on Monday morning and their favorite tool is gone, you will face resistance on every subsequent change.
The communication sequence:
- Announce the rationalization initiative and its objectives (cost savings, reduced complexity, better data quality).
- Share the analysis for each tool being removed or consolidated, including the alternative.
- Provide a transition timeline with specific dates.
- Offer training on the replacement tool or workflow.
- Execute the change on the announced date.
Measure the impact
After completing a rationalization cycle, quantify the results:
- Cost savings. Total annual spend reduction from eliminated tools.
- Efficiency gains. Reduction in integration maintenance hours, training overhead, and vendor management time.
- Data quality improvement. Fewer systems means fewer data conflicts. Measure the reduction in data discrepancies between systems.
- Utilization improvement. With fewer tools, the remaining tools should see higher utilization. Track active users and feature adoption.
These metrics justify the effort and build organizational support for ongoing rationalization.
The AI layer: what is changing
The martech stack is being reshaped by AI in ways that accelerate the consolidation trend. Understanding where AI fits helps you make better technology decisions today.
AI tools that augment vs. replace existing martech
Not all AI marketing tools are equal. Some augment existing platforms by adding intelligence layers on top of your current stack. Others replace existing point solutions entirely.
Augmentation examples:
- AI-powered content optimization tools that sit on top of your CMS and recommend headline changes, layout adjustments, or personalization rules.
- AI scoring models that enhance your MAP's native lead scoring with predictive analytics based on intent data and behavioral patterns.
- AI writing assistants that accelerate content creation but still require your existing publishing and distribution tools.
Replacement examples:
- AI-native attribution platforms that replace legacy attribution tools with probabilistic modeling that does not depend on cookie-based tracking.
- AI-powered email platforms that handle send-time optimization, content personalization, and audience segmentation without the manual rules that traditional MAPs require.
- AI chatbots that replace or augment traditional live chat tools with conversational AI that can qualify leads, book meetings, and route visitors without human intervention.
Where AI adds the most value in the martech stack
Based on current adoption patterns, AI delivers the strongest ROI in three areas:
Personalization at scale. AI enables true 1:1 personalization across email, web, and advertising without requiring manual segment creation for every permutation. This is particularly impactful for companies with large prospect databases and diverse ICPs.
Attribution in a post-cookie world. As third-party cookies disappear and tracking becomes less deterministic, AI-powered attribution models use statistical methods to estimate marketing's contribution to revenue. These models are imperfect, but they are more robust than cookie-dependent alternatives.
Content optimization. AI tools that analyze content performance and recommend improvements (subject lines, CTAs, page layouts, messaging) provide continuous optimization without the manual A/B testing cycles that traditional approaches require. The gains are incremental per test, but they compound over time.
The consolidation trend: platforms absorbing point solutions
The largest martech platforms (HubSpot, Salesforce, Adobe) are aggressively adding AI capabilities to their core products. HubSpot's AI content tools, Salesforce Einstein, and Adobe Sensei are all designed to reduce the need for standalone AI point solutions.
This trend has a practical implication for stack rationalization: before buying a new AI point solution, check whether your existing platforms have added (or plan to add) the same capability. Platform-native AI features are typically better integrated, require less maintenance, and add no incremental licensing cost.
The broader trend is clear. The martech landscape will continue to grow in total number of vendors, but individual company stacks will consolidate. The winners will be platforms that combine multiple capabilities with strong integration and embedded AI, not point solutions that solve one narrow problem in isolation.
Building a rationalization cadence
Stack optimization is not a one-time project. Without an ongoing cadence, sprawl returns within 12-18 months as new tools get purchased, free trials convert to paid subscriptions, and team members bring their preferred tools from previous companies.
Quarterly: Review tool utilization data. Flag any tool with utilization below 50% for discussion. Check for new shadow IT purchases.
Annually: Run the full five-step audit framework. Reassess every tool against the keep/consolidate/cut criteria. Align the stack review with budget planning so that cut decisions translate directly to savings.
At every new tool purchase: Require a justification that includes: what problem does this solve, what existing tool does it overlap with, who will own it, and what is the integration plan. A simple intake form prevents most impulse purchases.
For a broader view of how the marketing technology stack fits into the overall GTM technology architecture, see the marketing operations guide.
The bottom line
A bloated martech stack is not a sign of sophistication. It is a sign that technology decisions were made tactically instead of strategically. Every tool you cannot justify on utilization, integration, and pipeline impact is a tool that is consuming budget, creating data fragmentation, and adding operational complexity without proportional return.
The framework is straightforward: inventory what you have, categorize by function, assess utilization, evaluate integration health, and decide what to keep, consolidate, or cut. Then build a cadence that prevents sprawl from returning.
Start with the audit. Pull your tool inventory this week. Compare licensed seats to active users. Identify the tools where you are paying for capabilities nobody uses. The savings and efficiency gains from a single rationalization cycle typically pay for themselves within one quarter.
At RevenueTools, we are building the operational infrastructure that connects your martech stack to revenue execution. Clean data, intelligent routing, and streamlined operations across the full GTM lifecycle. If your stack needs to work harder with fewer tools, we would like to help.