High ComplexityInternal Workflows & DataB2B SaaS4 weeks delivery

Revenue Ops Command Center

Unified revenue dashboard with real-time MRR, churn, expansion metrics, and AI-generated weekly narrative briefings.

Real-time

Revenue Reporting

5 days

Reporting Time Eliminated

23%

Net Revenue Retention Up

AI

Weekly Narratives

Production
156,000 runsLast: Just now
Real-time sync
Salesforce

Salesforce

Sales Data

Status
Running
TypeTrigger
Executions156,000
node-1
Pipeline data
Segment

Segment

Product Analytics

Status
Running
TypeAction
Executions148,200
node-2
Usage events
dbt

dbt

Data Transforms

Status
Running
TypeAction
Executions140,400
node-3
Clean metrics
Retool

Retool

Dashboard UI

Status
Running
TypeAction
Executions132,600
node-4
All systems operational
4 nodes3 connections

The Problem

The Challenge

A Series B SaaS company with $8M ARR had data spread across 6 tools: sales pipeline in Salesforce, product usage in Segment, support metrics in Zendesk, billing in Stripe, finance in spreadsheets, and marketing in HubSpot. The RevOps lead spent a full week every month pulling data from each tool, reconciling numbers in Excel, and building a board deck. The leadership team was making decisions based on data that was 3-4 weeks old. They had no real-time visibility into MRR, churn, expansion, or pipeline health.

How We Fixed It

Our Solution

1

Mapped the data architecture: identified every source system, the key metrics each one holds, and how they connect (e.g., Salesforce account ID → Segment user ID → Stripe customer ID).

2

Set up Segment to collect and route product usage events to a PostgreSQL data warehouse, alongside data syncs from Salesforce, Stripe, and Zendesk.

3

Built dbt models that transform raw data into clean, business-logic tables: MRR calculations (new, expansion, contraction, churn), cohort analyses, pipeline velocity metrics, and support health scores.

4

Deployed Retool dashboards with real-time views: MRR waterfall, churn analysis, expansion revenue, pipeline coverage, rep performance, support SLA compliance, and customer health scores.

5

Integrated OpenAI to generate weekly narrative briefings: plain-English summaries of key trends, anomalies, and recommended actions based on the underlying data.

6

Configured Slack delivery: Monday morning briefings with key metrics, week-over-week changes, and red flags — delivered to leadership, sales, CS, and product channels.

Tools & Infrastructure

Tech Stack

S

Salesforce

Sales data source. Provides pipeline, deal stage, revenue forecasts, and account relationship data for the unified model.

S

Segment

Product analytics pipeline. Collects user behavior events and routes them to the data warehouse for usage-based health scoring.

d

dbt

Data transformation layer. Converts raw data from multiple sources into clean, tested, version-controlled business metrics and models.

R

Retool

Dashboard and reporting UI. Real-time interactive dashboards for leadership, with drill-down capabilities and role-based access.

Impact & Outcomes

The Results

Real-time

Revenue Reporting

Revenue metrics updated in real-time instead of monthly — leadership makes decisions on current data, not 3-week-old snapshots.

5 days

Reporting Time Eliminated

Monthly reporting that took a full week now happens automatically and continuously.

23%

Net Revenue Retention Up

Better visibility into expansion and churn signals improved NRR from 108% to 133% over 6 months.

AI

Weekly Narratives

AI-generated Monday morning briefings give leadership plain-English insights without needing to interpret dashboards.

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