In a job shop, the quote log is the sales pipeline, but it's almost never managed like one. Quotes go out, some come back as orders, and most owners couldn't tell you their win rate or the dollar value of work still in play. This dashboard turns the quote log into a funnel: quotes sent, quotes won, open pipeline dollars, and the win drivers underneath - so quoting becomes something you steer instead of something that just happens.
What this dashboard answers
It answers "how much of what we quote do we win, and on what?" The funnel page counts Quotes Sent and Quotes Won, shows the Open Pipeline $ still undecided, and the Won Revenue $ already booked. Win Rate % (won over sent) and Dollar Win Rate % (won dollars over quoted dollars) ride a by-month bar/line so you can see whether you win small jobs but lose big ones, or the reverse - a distinction a single blended rate hides.
The metrics that matter
Win rate is the headline, but the win-drivers page is where decisions get made: win rate by part family (where are we genuinely competitive?), win rate by quoted-dollar band (do we lose above a price threshold?), the won/lost/open outcome mix by customer, and Open Pipeline $ by part family. Together they tell you which work to chase, which to walk away from, and which customers are quoting you for price-checks rather than real orders.
Why this data is trapped
Quote and order records live in the estimating/quoting module of JobBOSS (E2 Shoptech), ProShop ERP, Global Shop Solutions, ECI M1 / Epicor, or a QuickBooks-plus-spreadsheet quote log at the smallest shops. The ERP can list quotes and list orders, but tying a quote to its outcome and rolling that into a win-rate funnel by customer and part family is exactly the analysis it doesn't do - so win rate stays an anecdote rather than a measured number.
How to read it
Start with Dollar Win Rate % against plain Win Rate %: if you win most quotes but a small share of quoted dollars, you're winning cheap work and losing the jobs that matter. Read win rate by part family to find where you're truly competitive (and where you're quoting to lose), and by dollar band to find the price ceiling above which you stop winning. Open Pipeline $ by part family is your near-term forecast - and the customers with a high lost/open share are the ones using you as a price check.
The sample uses realistic synthetic quote data - no real customers, parts, or pricing.
Metrics it tracks
| Metric | What it means |
|---|---|
| Quotes Sent | Count of quotes sent in the period - the top of the funnel. |
| Quotes Won | Number of quotes that converted to an order. |
| Open Pipeline $ | Quoted dollars on quotes still open and undecided. |
| Won Revenue $ | Booked dollars from quotes that converted to orders. |
Used by: Owners and estimators who quote work and manage the sales pipeline
Frequently asked questions
What is a good quote win rate for a machine shop?
It varies enormously by work type, customer base, and how aggressively a shop quotes. The point of measuring it isn't a benchmark - it's seeing where you win (which part families, which dollar bands, which customers) so you can quote more of the work you win and stop spending estimating time on work you don't.
Why track dollar win rate separately from quote win rate?
Because they can tell opposite stories. A shop can win a high share of quotes but a low share of quoted dollars - winning small jobs and losing the large ones. Tracking both reveals whether your pricing is competitive on the work that actually moves revenue.
Can I build this on my own quote log?
Yes. Export your quotes with their amount, outcome (won/lost/open), customer, and part type, then use this as a template. We model it into a Power BI report. The sample shown is fully synthetic, so there's no real customer or pricing data in it.
Build this report on your own data
Clone this Machine / job shops template — describe it and we’ll generate sample data so you can try it free, or upload your own export. You get a fully modeled, branded Power BI project that opens in Power BI Desktop.
Use this as a template →