Pipeline Report: What a Weekly Pipeline Report Includes, With an Example and Free Template

A pipeline report is a weekly snapshot of the open deals that can close this period, and of how that pipeline changed since the last snapshot: what was created, pulled in, won, lost and slipped, and which deals moved stage. It tells a sales team whether there is enough pipeline to hit the number, and whether that pipeline is real.

Most pipeline report examples online are lists of nine or ten reports with a screenshot each: opportunities by stage, average deal size, win rate, revenue by region. Those are useful views. They are not a report anyone reads on Monday morning, because they show totals, and totals barely move from one week to the next while the deals underneath them move constantly. This guide shows the one weekly report I would want in front of a sales leader, a full example with numbers that reconcile, and a free template that builds it from two CRM exports.

What is a pipeline report?

A pipeline report is the record of what your pipeline holds and how it changed, for one period, at one point in time. It is not the same thing as a dashboard, a forecast or a pipeline review, though it feeds all three. The dashboard is covered in how to build a pipeline dashboard, and the meeting in the pipeline review template. The difference between the deals you have and the number you will close is in sales pipeline vs sales forecast.

The distinction matters because of one number. Across ORM customers, about 20% of the pipeline with close dates in the quarter on the first day of the quarter closes in that quarter. The other 80% of that value slips, shrinks or is lost. A report that presents its total as if it were near-revenue sets an expectation the quarter will not meet. The data behind that figure is in how much day-one pipeline actually closes.

What should a pipeline report include?

Seven sections, in this order: where the period stands, the pipeline bridge, creation, stage mix, risk, the forecast range and a three-sentence read. Each one answers a different question, and each has a red flag worth knowing in advance. Two rules shape every section.

The first is that change beats state. A deal being in Proposal is useful. Knowing it moved into Proposal yesterday is more useful. The same goes for amount: the fact that it just changed, by how much and how late in the cycle, carries more signal than the amount itself. ORM snapshots pipeline every day for that reason. A weekly report built on two snapshots captures the same idea at a lower resolution.

The second is that activity is not movement. ORM counts meaningful activity on an opportunity as a change in stage, close date or amount. Logged calls and emails are easy to produce without a deal advancing, so the stale flag in this report uses those three fields only.

Why the bridge is the heart of the report

The bridge is the section none of the top-ranking examples include, and it is the one that changes conversations. It works like an ARR bridge, but for open pipeline in one period: start of week, the lines that added value, the lines that removed it, end of week. It has to reconcile. If the start plus every line does not equal the end, a deal is missing from one of the exports, and the report says so before anyone acts on a wrong number. The chart version of this view is the pipeline waterfall.

Why coverage never appears alone

Pipeline coverage is the metric that creates the most false confidence. It makes executives feel informed while it hides the composition of the quarter. A company can carry 4x coverage and miss badly if the pipeline is stale, concentrated in early stages, dependent on a few large deals or built on close dates that keep moving. The standard rule is 3x to 5x, and most ORM customers sit near 3.5x, with real accounts anywhere from 1.4x to 5x. In the report, coverage sits next to the bridge and the risk flags, so nobody reads it as an answer. More on the ratio in pipeline coverage.

Pipeline report example

Below is a complete weekly pipeline report for an illustrative B2B SaaS company: Q3 2026, week 4 of 13, a $2,400,000 target for new business and expansion. Every account and number is made up. They are built to reconcile, and the same deals are in the example download, so you can trace every figure back to a row.

How to read this example

Start with the contradiction in the top row. Coverage went up, from 3.40x to 3.62x, in a week when the pipeline shrank by $680,000. Both are true. Last week the gap was $2,400,000 minus $520,000 won, so coverage was $6,400,000 / $1,880,000 = 3.40x. This week the gap is $1,580,000, so $5,720,000 / $1,580,000 = 3.62x. Wins shrink the denominator faster than slips shrink the numerator, so in a good closing week coverage can rise while the pipeline gets weaker. A report that showed only the ratio would call this a good week.

The pace check uses seasonality instead of a straight line. Across ORM customers, the third month of a quarter is always the largest and the first month the smallest, and a large share of business closes in the last week. The template's default weights are one ORM 13-week profile, where the first four weeks carry 0.70 + 0.59 + 0.63 + 0.81 = 2.73 of a 12.99 total, or 21.0% of the quarter. $820,000 is 34.2% of target, about $316,000 ahead of the seasonal pace. A straight line would expect 4 / 13 = 30.8% by now, so it would understate how far ahead the team is, and later in the quarter it would raise false alarms.

Then the bridge tells you where the $680,000 went: $610,000 slipped to Q4 and $390,000 was lost, against $520,000 created and $180,000 pulled in. The stage mix shows the damage is late: Proposal and Negotiation together lost $705,000 in the week. That is why the expected forecast fell $70,000 even though bookings are ahead of pace.

Three details are worth a question in the review. OPP-1073 is on its second push. The best single signal that a deal will slip is a rep changing the close date, and a deal that slips from one quarter to the next is less likely to close even when it sits in commit. Three amount cuts came from one rep, which is a coaching conversation, not a forecasting one. And the two pull-ins need a check on price: pulling deals forward usually means discounting them, and it shrinks the pipeline the next quarter starts with.

Finally, the size gap. The average open deal is $67,294 and the average won deal over the last four quarters is $41,000. A pipeline whose open deals average 1.64 times the size of the deals it actually wins is carrying more value than it will produce. Most deals close for less than the value in the CRM. I have seen pipelines with an average open deal of $80,000 where won deals averaged $40,000.

How do you build a pipeline report?

Take the same snapshot at the same time every week, match this week's deals to last week's by opportunity ID, classify each change, and sum the changes into a bridge that reconciles. Six steps:

1. Fix the definitions first. The period (usually the quarter), the one amount field you report on, and which pipelines are in scope. Report new business and expansion together, and forecast renewals separately: their amount and timing are known in advance, and mixing them hides the risk in new business. One ORM customer had nine competing amount fields (TCV, ACV, ARR, renewal amount and legacy fields that stopped being filled in after 2021), and sorting out which one meant what took several weeks.
2. Snapshot at a fixed time. Monday at 6 a.m. works for most teams, so the report reflects the week before and not whatever reps changed on Monday morning. Without a stored snapshot you cannot see what slipped, because the CRM only holds the current close date. The case for snapshots is in why what changed in your CRM beats what is in it.
3. Match and classify. Join the two snapshots on opportunity ID. Each deal is created, pulled in, won, lost, slipped out, changed in amount, moved in stage, or unchanged. The rules are in the table below.
4. Build the bridge and check it. Start + created + pulled in + increases minus decreases, slipped, lost and won must equal the ending pipeline. If it does not, a deal that was open last week is missing from this week's export. Fix the export, not the number.
5. Add creation, stage mix and risk. Creation against a weekly target, value by stage in both snapshots, and the risk flags: stale deals (no stage, amount or close date change in a set number of days), deals pushed more than once, the top five deals as a share, and the open versus won deal size.
6. Write the read and send it before the meeting. Three sentences. Name the deals. "Three deals slipped" prompts a question; which three, owned by whom and how far prompts a decision. What belongs in the weekly version is covered in what should be in a weekly sales report.

The classification rules

Every bridge line comes from one rule applied to each deal. These are the rules the template uses, with last week's amount for deals leaving the period and this week's amount for deals entering it, so the bridge always reconciles.

Building it in a spreadsheet

The template does all of this with ordinary formulas that work in Excel and Google Sheets. Paste last week's export into the Last week tab and this week's into the This week tab, using the same 14 columns. The grey columns to the right of This week then match and classify every deal:

The Report tab then sums those columns into the seven sections above, and the Trend tab keeps one row per week so you can see creation, slippage and the stale share over the quarter. No XLOOKUP, FILTER or macros, so it runs in older Excel versions too.

Building it in Salesforce or HubSpot

You need two things from the CRM: a saved opportunity view with the 14 columns, and a way to keep last week's copy. In Salesforce, a reporting snapshot can load a scheduled report into a custom object each week, which gives you the stored history; the opportunity field history report gives the dates of stage, amount and close date changes. In HubSpot, deal property history holds the same change dates, and the date-entered-stage properties show when each deal last moved. If neither is set up, exporting the same saved view to the spreadsheet every Monday works on day one. The report is only as consistent as the export, so keep the filter identical every week: every open opportunity, plus every won or lost opportunity with a close date in the period.

Do not wait for clean data to start. Every company believes its CRM data is uniquely bad, and every company's data has a run rate: even after a cleanup it drifts back to the same level of completeness. Consistent imperfect data still shows the pattern of the week. The report also improves the data, because the moment stale deals are named every Monday, reps start updating them.

What goes in each tab of the template?

Six tabs: one to explain, one report, one for settings, two snapshots and a weekly trend log. Yellow cells are inputs; grey cells are formulas. The example file holds the full Q3 week 4 data set behind the report above: 145 deals last week and 154 this week, including closed deals and deals outside the quarter, so you can see how each one is classified before you paste in your own.

What mistakes make a pipeline report misleading?

Most misleading pipeline reports show the total and hide the change. These are the failures I see most often, with the fix for each. One more, which is a mistake of interpretation rather than construction. In Q1 and Q2 of 2026, ORM saw customers whose coverage looked fine while fewer deals were being decided. Deals were not won or lost; they sat there, days in stage crept up, and velocity slowed. A bridge with small won and lost lines and a growing stale share is how that shows up in a weekly report, weeks before the total moves. When you see it, force decisions: a no today is better than a no three months from now.

How often should you run a pipeline report?

Weekly, from a snapshot taken at the same hour, and read before the pipeline review rather than during it. Reps can watch their own deals daily, and leadership needs a monthly and quarterly view, but the weekly report is the one that catches change while there is still time in the period to act. Companies that track pipeline weekly reach 87% forecast accuracy against 52% for those that track it irregularly, according to Digital Bloom's 2025 pipeline benchmarks.

Keep the cadence of the report and the cadence of the meetings tied together. The report goes out Monday morning, the pipeline review works the flagged deals, and the weekly forecast call turns the result into a range. If the report arrives after the meeting, the meeting spends its time on status updates.

Where does ORM fit?

A spreadsheet report works, and the template above is a complete one. Its limits are the snapshot (weekly, not daily), the stale rule (one threshold for every deal) and the forecast range (typed in from the call). ORM snapshots the pipeline every day, groups opportunities with a machine learning model and predicts a close curve for each group, so "stale" is measured against how long deals like this one normally take rather than a fixed number of days. Those curves run from 1 to 80 weeks, with most of the expectation before week 12. The forecast that comes out is a range, built without manual adjustments, and ORM targets 95% accuracy from day 1 to day 90 of the quarter. See sales forecasting software for how that works on your own pipeline.