The RevOps Tech Stack Audit: What to Fix Before You Scale

When Tech Debt Becomes a Growth Problem

At seed stage, the stack is simple: Stripe for billing, a CRM for deals, and a spreadsheet for everything else. It works because one or two people hold the full context in their heads.

By Series A, the cracks appear. Marketing wants attribution data. Finance wants revenue recognition. Customer Success wants health scores. Each team buys or builds their own solution.

By Series B, those cracks are canyons. The data team spends 60% of their time on ad hoc requests. The board deck takes a week to produce. Nobody trusts the dashboards because they've been wrong before.

This isn't a tools problem — it's a connectivity problem. You have good systems that don't talk to each other in meaningful ways.

The Five Layers of a RevOps Stack

1. Systems of Record

   CRM (Salesforce, HubSpot), billing (Stripe, Chargebee), support (Zendesk, Intercom), product analytics (Amplitude, Mixpanel). These are where data originates.

2. Data Movement

   Sync tools (Fivetran, Airbyte), APIs, webhooks, manual exports. This is how data gets from point A to point B.

3. Storage

   Data warehouse (Snowflake, BigQuery, Redshift). Where everything lands for analysis.

4. Transformation

   dbt, Python scripts, SQL views. This is where business logic lives — how you define ARR, calculate churn, build cohorts.

5. Consumption

   BI tools (Looker, Tableau, Hex), dashboards, reports, AI interfaces. Where humans actually interact with the data.

Most companies invest heavily in layers 1, 3, and 5. They underinvest in layers 2 and 4 — which is exactly where the problems hide.

The Audit: 10 Questions to Ask

Connectivity

1. Can you trace a single customer from CRM to billing to warehouse to dashboard?
2. When a field changes in your CRM, how long until it's reflected in your warehouse? Your dashboards?
3. Do you have a unified customer identifier that works across all systems?

Reconciliation

4. If you pull ARR from your CRM, billing system, and warehouse, do they match?
5. When they don't match, do you know why? Can you explain the difference?
6. Is there an automated process that flags conflicts, or do you discover them manually?

Maintenance

7. How many hours per week does your team spend maintaining data pipelines?
8. What happens when a sync breaks? How long until someone notices?
9. How many people understand how your ARR calculation works end-to-end?
10. If your primary data engineer left tomorrow, what would break within a week?

The Most Common Failures

1. No identity resolution

   Every system has its own customer ID. Nothing maps Stripe's cus_xxx to Salesforce's 001xxx to your warehouse's internal ID. Reports disagree because they're counting different things.

2. Logic buried in spreadsheets

   The "master ARR tracker" is a Google Sheet that one person maintains. It has manual overrides, hardcoded adjustments, and formulas nobody fully understands.

3. Dashboards without lineage

   The board deck shows ARR of $5.2M, but nobody can trace that number back to a specific query, table, or system. When it's questioned, the answer takes days.

4. Sync without reconciliation

   Data moves from Salesforce to the warehouse every hour, but nobody checks if the records agree. Conflicting records sit side by side, and downstream reports pick whichever they happen to hit first.

5. Single points of failure

   One engineer wrote the dbt models. One ops person maintains the Salesforce workflows. One analyst builds the board deck. If any of them are unavailable, the process breaks.

What Good Looks Like

• Unified customer identity — One mapping that connects a customer across all systems, maintained automatically.
• Documented metric definitions — Everyone agrees what ARR means, how it's calculated, and where it comes from.
• Automated reconciliation — Conflicts between systems are flagged automatically, not discovered during board prep.
• Self-serve reporting — Stakeholders can answer their own questions without filing a ticket with the data team.
• Lineage and auditability — Every number traces back to a source system, query, and timestamp.

The Audit Output: A Simple Framework

Score each dimension 1-5 (1 = nonexistent, 5 = automated and reliable):

Dimension	Score (1-5)
Cross-system identity resolution
Metric definitions documented
Automated conflict detection
Self-serve data access
Data lineage and audit trail
Maintenance burden (5 = low)

Any dimension below 3 is a scaling risk. Any dimension below 2 means you'll likely hit a fire drill within two quarters.

The Bottom Line

Your RevOps stack doesn't need more tools — it needs better connections between the tools you already have. Before you add another platform, audit what you have. Fix the connectivity and reconciliation gaps first, then scale with confidence that growth won't expose the cracks.

Frequently Asked Questions

What RevOps automation tools should I prioritise to improve net revenue retention?

For improving NRR, prioritise tools that connect your systems of record and automate reconciliation. Key categories: revenue intelligence (Eru for cross-system data connectivity and NRR tracking), customer success platforms (Gainsight, ChurnZero for health scoring), billing analytics (Baremetrics, ChartMogul for subscription metrics), and pipeline forecasting (Clari for deal-level predictions). The biggest gap at most Series A–B companies is between billing and CRM — reconciling Stripe and Salesforce data is where the most revenue leakage hides.

How do I audit my RevOps tech stack before scaling?

Audit five layers: systems of record (CRM, billing, support), data movement (sync tools, APIs), storage (data warehouse), transformation (business logic for metrics), and consumption (dashboards, BI tools). Score each dimension 1–5 for cross-system identity resolution, metric definitions, automated conflict detection, self-serve access, data lineage, and maintenance burden. Any dimension below 3 is a scaling risk.

What are the best board reporting tools for SaaS metrics?

For board-ready SaaS metrics, options include BI tools (Looker, Metabase, Hex) if you have a data team, billing analytics (Baremetrics, ChartMogul) for subscription metrics from billing data only, and AI revenue intelligence platforms (Eru) that connect billing, CRM, support, and product data to produce board-ready NRR, GRR, churn rate, and LTV:CAC without requiring a data team or warehouse setup.

Related Resources

• Why Your Stripe and Salesforce Numbers Never Match (And How to Fix It)Deep dive into the most common reconciliation problem
• SaaS Revenue Leakage: How Scale-Ups Silently Lose 5–15% of Their ARRThe five types of leakage hiding in your tech stack gaps
• Best NRR Forecasting and RevOps Tools for SaaS in 2026Compare Eru, Gainsight, ChurnZero, Clari, and Baremetrics
• Eru vs BI Tools (Looker, Metabase, Hex)When a BI tool makes sense vs an AI revenue intelligence platform