Somewhere between your Series A and Series B, your GTM tech stack quietly became a £500k annual commitment.
Nobody planned it. Nobody approved it as a single decision. It happened one tool at a time — each solving a problem that shouldn't have existed if the architecture had been right in the first place.
A CPQ tool because quoting was manual. A forecasting overlay because pipeline hygiene was poor. A data enrichment platform because the CRM was never the system of record. Integration middleware because nothing talks to anything else natively.
Each purchase made sense in isolation.
Together, they form what I call a Frankenstein GTM tech stack — a bloated, fragmented architecture where the integration cost exceeds the value of any individual tool.
And this is exactly what sophisticated Series B investors now audit.
Why Investors Care About Your Tech Stack
Five years ago, investors didn't ask about your GTM architecture. The tech stack was a line item buried in operating expenses.
That has changed.
Operating partners at growth-stage funds now routinely request a full technology audit during due diligence. They want to understand three things:
- Operational efficiency. A £500k tech stack supporting £5M in ARR is a 10% tax on revenue. That doesn't scale.
- Data integrity. Can you produce reliable LTV:CAC and cohort retention metrics? Or does data flow through so many systems that reconciliation is a quarterly exercise? Fragmented stacks produce data debt that investors now explicitly price into valuation risk.
- Scalability. Will this architecture support 3–5x growth? Or will every milestone require another tool, another integration, another headcount to manage the complexity?
The answers determine whether your tech stack is an asset or a liability.
The Anatomy of a Bloated GTM Stack
I've audited dozens of GTM tech stacks at companies between £3M and £20M ARR. The pattern is remarkably consistent.
Layer 1: The core CRM (£30–80k/yr)
Salesforce, HubSpot, or occasionally something else. The system of record — in theory.
In practice, it's 60% configured and nobody trusts the data. Required fields aren't required. Validation rules don't exist. Stage definitions are interpreted differently by every rep.
Layer 2: The overlay stack (£40–80k/yr)
Tools that exist because the underlying data can't support the calculations.
Forecasting tools ingesting pipeline data the CRM should already govern. Compensation platforms managing plans that could be modelled in a spreadsheet if the input data were clean. Intelligence tools recording calls because managers aren't managing.
Layer 3: The integration tax (£50–100k/yr)
Middleware, iPaaS platforms, custom connectors, and the consultancy fees to maintain them.
This layer exists entirely because the tools in layers 1 and 2 were purchased without architectural planning. Each tool has its own data model. None of them agree. The integration layer is duct tape.
Layer 4: The data remediation tax (£30–60k/yr)
Enrichment tools, deduplication services, data quality monitoring.
These exist because the architecture produces bad data. Rather than fixing the architecture, you've purchased more tools to clean up after it.
Total: £150k–320k in tools that exist only because the underlying architecture is broken.
That's not a technology investment. That's a recurring penalty for poor system design.
The Four-Phase GTM Tech Stack Audit
Here's the framework I use when a CFO or founder asks me to rationalise their GTM spend before a raise.
Phase 1: Full inventory and true cost mapping
Pull every GTM-related contract, licence, and subscription.
Not just the obvious ones. The £200/month tools someone in marketing signed up for and nobody cancelled. The Zapier account running 47 automations nobody documented. The data enrichment API billing monthly since 2024.
Map the true cost: licence fees plus implementation, plus integration maintenance, plus internal admin hours. A £15k/yr tool requiring 10 hours per week from a £70k/yr employee actually costs £32k/yr.
Most companies have never calculated this.
Phase 2: Data dependency mapping
For every tool, answer one question: what data does it produce, consume, or transform?
Map the flows. Where does lead data originate? How does it move from marketing to sales? Where does opportunity data get enriched? How does closed-won data reach finance?
This exercise invariably reveals three things:
- Data duplicated across systems with no single source of truth.
- Transformations happening in integration middleware that nobody understands.
- Data entering the ecosystem but never reaching the team that needs it.
Phase 3: Value attribution
For each tool, ask: what specific business outcome does this enable that cannot be achieved through the CRM or a simpler alternative?
Be ruthless. "It makes reporting easier" is not a business outcome. "It produces the cohort retention analysis our board requires monthly" is.
Categorise every tool into three buckets:
- Essential: Unique capability. Directly supports revenue generation or investor reporting. Cannot be replicated.
- Redundant: Overlaps with another tool. The capability exists elsewhere but nobody configured it properly.
- Compensatory: Exists to fix a problem created by poor architecture elsewhere. The tool is a symptom, not a solution.
In a typical audit, 20–30% of tools fall into Essential. The rest split between Redundant and Compensatory.
Phase 4: Architectural consolidation
This is where the savings materialise.
For every Redundant and Compensatory tool, define the architectural change that eliminates the need for it:
- Forecasting overlay → unnecessary when pipeline stage definitions are enforced and snapshot data is captured natively.
- Data enrichment → unnecessary when required fields are actually required and validation rules prevent garbage at point of entry.
- Integration middleware → simplifies dramatically when you reduce the number of systems that communicate.
- Revenue intelligence → optional when managers inspect pipeline directly instead of relying on AI call summaries.
Architecture over apps. Fix the system design. The compensatory tools become unnecessary. That's how you cut £100k+ without losing any actual capability.
What This Means for Series B Readiness
A rationalised tech stack doesn't just save money. It solves the data integrity problem that causes due diligence delays.
Fewer systems. Clearer governance. Numbers that reconcile.
When the CRM is actually the system of record — not one of seven systems each containing a partial version of the truth — you can produce the cohort retention, unit economics, and pipeline predictability metrics investors require.
I've watched companies spend six weeks reconstructing metrics from fragmented data during due diligence. Same companies with £400k tech stacks. The tools were supposed to make this easier.
The companies that raise at the best multiples aren't the ones with the most sophisticated stacks. They're the ones with the cleanest data.
Clean data comes from simple architecture. Not from buying another tool.
The Audit Pays for Itself Before the Raise
Here's the maths CFOs care about.
A typical audit at Series A/B stage identifies £100–200k in eliminable annual spend. Takes 4–6 weeks. Consolidation takes 2–3 months. Savings begin immediately as contracts renew.
But the real return isn't cost savings. It's valuation impact.
If data debt costs you 2–3 turns on your revenue multiple and your ARR is £8M, that's £16–24M in lost enterprise value. The £100k in software savings is a rounding error against that.
The audit isn't a cost-cutting exercise. It's valuation protection. The savings are a side benefit.
Your tech stack should be an architecture that compounds value.
If it's a collection of compensatory tools held together by integration middleware, you don't have a revenue engine. You have a Frankenstein stack costing you far more than the licence fees suggest.
Fix the architecture. The tools problem solves itself.