Pipeline reporting
If two people can define 'pipeline' differently, your dashboard is an opinion.
Untrusted reporting is almost never a dashboard problem. It's missing definitions, missing timestamps and attribution rules nobody chose on purpose. Fix those, and the dashboard becomes the boring part.
Part of one fixed build: $7,500, 21 days, $2,500 back if it isn't running as scoped. Pricing details
Ask yourself
Three questions that usually expose it.
- What is the exact rule that makes a lead an SQL, and where is it written?
- If you re-ran last quarter's conversion report today, would you get the same number?
- How far back does a marketing touch count toward sourced pipeline?
Failure modes
What breaks, and why.
| Failure | What you see | Root cause |
|---|---|---|
| Conversion rates that change when you re-run them | Last quarter's SQL-to-opportunity rate is different today than it was in the board deck. | Reports read current stage, not when the record entered it. Without stamped stage dates, history rewrites itself as records move. |
| Velocity that ignores skipped stages | Deals jump from discovery to contract, and average time-in-stage looks great. | Stage skipping isn't controlled or reported, so the averages only include deals that followed the path. |
| Marketing and sales disagree on sourced pipeline | Both teams claim the same deals. | Sourced and influenced were never defined, and the attribution window (how far back a touch counts) was never chosen. |
| An SQL means whatever the rep thinks | The same lead is an SQL for one rep and junk for another. | No written criteria, and no required evidence fields at the moment of qualification. |
First 48 hours
What I check before changing anything.
- Written definitions for MQL, SQL, opportunity and each stage, or the lack of them
- Stage-date properties or history: coverage, gaps and whether they record first or latest entry
- Share of deals that skipped stages or moved backwards
- Source fields: which are read-only, which are overwritten, and by what
- Attribution rules in use, including window length and first- vs. last-touch
- Which reports leadership actually opens, and what each one filters on
Decisions we make with you
Tradeoffs, written down.
Cohort vs. snapshot
Cohort reporting (deals created in a period) answers conversion; snapshot reporting (pipeline on a date) answers coverage. Most dashboards mix them. We separate them.
Attribution window
A short window undercounts long cycles; a long one credits every old touch. We set it from your actual sales cycle, in writing.
Sourced vs. influenced
Sourced is one owner per deal; influenced allows many. Leadership agrees both definitions before anything is built.
A board saw win rate fall every time the report was refreshed. The report counted deals by current stage, so closed-lost deals from months ago kept moving into the denominator. Rebuilding it on stamped creation and close dates made the number stable.
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FAQ
Common questions.
Will the new numbers match what finance reports as closed-won?
That's the first reconciliation we do. CRM closed-won is compared against finance's booked revenue for a recent period, and differences (dates, amounts, multi-year deals) are explained and either fixed or documented before the dashboard is trusted.
What if the new definitions make last quarter look worse?
Then you'll see why, side by side. We show old and new definitions on the same period so leadership agrees the change before it's adopted, instead of being surprised in a board meeting.
Who signs off the definitions of SQL, sourced and influenced?
Sales and marketing leadership, together, in writing, before anything is built. I draft them from how deals actually move in your CRM.
How do you handle deals that skipped stages or moved backwards?
They're counted and shown, not hidden. Velocity and conversion views state how skipped stages are treated, so averages don't quietly exclude the messy deals.
Can the dashboard be rebuilt historically or only from today?
Historically, wherever stage history and dates exist. Where they don't, the dashboard shows from when reliable data starts, rather than inventing a backfill.
Connected problems
The build fixes these as one system.
CRM data foundation
Duplicates are a symptom. The disease is uncontrolled record creation.
HubSpot
Most HubSpot problems come from a handful of platform behaviours nobody designed for.
Salesforce
Salesforce gives you every control. Most orgs have them configured against each other.
Signal-based pipeline
Lead quality is a model, not a list.
Find out whether your pipeline number is a definition or an opinion.
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