Most teams can answer “How many leads did we get?” or “How much revenue did we book?” but struggle to answer the real question:
Which campaigns are creating pipeline and revenue right now—and what happens after customers sign up?
If your stack looks like Google Ads → Salesforce → Stripe → PostgreSQL, you already have the pieces. What’s missing is a reliable way to connect them into a single story: lead → pipeline → revenue → retention.
The three questions this should answer every week
- Where should we invest next week? (campaigns and keywords that actually create revenue)
- Where is the funnel leaking? (handoffs between marketing → sales → activation)
- Are we acquiring the right customers? (retention and expansion by source)
What you can build first (high signal, low effort)
- A lead-to-cash funnel: spend → leads → opportunities → won → paid → activated
- A pipeline health view: velocity, win rate, stage conversion by source
- A revenue + retention view: MRR, churn, and “healthy usage” by acquisition channel
Why DIY pipelines are deceptively expensive
DIY looks cheap until you price the ongoing work:
- API changes and auth flows
- Retries, backfills, and “why is yesterday missing?”
- Schema drift that breaks dashboards
- A growing “data maintenance tax” on your team
With a managed data platform, you get dependable ingestion and a stable analytics layer so your team can focus on decisions—not plumbing.
Why spreadsheets don’t solve this
Spreadsheets can’t reliably join paid acquisition, CRM lifecycle, billing, and product usage over time—especially when definitions change and you need history.
The result is usually stale data that doesn’t reflect current reality. Metric disagreement emerges as different tools calculate the same metrics differently. Most critically, time is lost reconciling numbers instead of acting on insights.
A fast rollout (two sprints)
Sprint 1: connect sources and ship the lead-to-cash funnel.
Sprint 2: add retention/expansion views and alerts (spend anomalies, pipeline drop-offs, churn signals).