Martech Stack
A martech stack (marketing technology stack) is the collection of software tools an organization uses to execute, manage, measure, and optimize revenue operations across marketing, sales, and customer success. The average B2B company with 200+ employees uses 120-150 SaaS tools. Most couldn't name half of them.
The Four Categories
Every tool in your stack does one of four things. If you can't place a tool into one of these categories, you probably don't need it.
Capture — tools that create or collect data about prospects and customers. Your CRM, web analytics, form tools, chat platforms, intent data providers.
Route — tools that move data or people between systems and teams. Lead routing, workflow automation, integration middleware, data sync platforms.
Enrich — tools that add information to existing records. Data enrichment providers, firmographic databases, email verification, intent signal platforms.
Report — tools that transform data into decisions. BI platforms, dashboards, attribution tools, forecasting software.
This framework matters because the most common stack problem is category overlap — three tools doing enrichment, two tools doing routing, and nobody sure which one is the system of record. When you audit by category, overlap becomes immediately visible.
The Stack Audit
A stack audit is a systematic review of every tool in your revenue technology stack, assessed against what it costs, who uses it, and whether it's earning its keep. This is the single highest-ROI exercise a RevOps team can perform, and most companies have never done one.
Step 1: Inventory. Pull the complete tool list from finance (every SaaS line item) and from IT (every SSO-connected application). These lists will not match. The gap is where shelfware hides.
Step 2: Categorize. Map each tool to Capture, Route, Enrich, or Report. Any category with three or more tools is a consolidation opportunity.
Step 3: Measure usage. Check actual logins and activity (DAU/MAU) against license count. A tool with 50 licenses and 8 monthly active users is shelfware. This happens more often than anyone wants to admit.
Step 4: Score each tool. Three tiers:
- Mission-critical — revenue stops or breaks if this tool goes away
- Valuable — makes people measurably faster, but work could happen without it
- Shelfware — nobody would notice if you turned it off tomorrow
Step 5: Calculate consolidation savings. Add up the annual cost of everything scored as shelfware plus the overlap cost of duplicate tools in the same category. This number is usually 15-30% of total stack spend.
Why Stacks Get Bloated
Stacks grow because adding tools is easy and removing them is hard. A new VP of Marketing brings their preferred email tool. A sales leader signs an intent data contract at a conference. IT approves a trial that auto-renews. Nobody conducts an annual review.
The result is a stack where:
- Three tools capture form submissions
- Two tools route leads to sales reps
- Nobody knows which enrichment provider is actually populating CRM records
- The BI tool and the CRM both produce pipeline reports that show different numbers
This isn't just a cost problem — it's a data integrity problem. When multiple tools write to the same CRM fields, you get conflicting data, broken automations, and reports nobody trusts.
The Integration Tax
Every tool in your stack needs to exchange data with other tools. Each integration is a maintenance liability: APIs change, sync schedules break, field mappings drift. A 15-tool stack might have 8-12 active integrations, each one a potential failure point.
Before adding any new tool, ask: what does it need to integrate with, does a native integration exist, and who will maintain it when it breaks? If the answer to the last question is "nobody" or "we'll figure it out later," the tool will create more problems than it solves.
Related Terms
- Total Cost of Ownership (TCO) — the framework for evaluating what your stack actually costs
- Revenue Operations (RevOps) — the function responsible for stack architecture and governance
- Pipeline Velocity — a metric that suffers when stack problems corrupt data quality