From quote to invoice, and where it leaks

What this dashboard answers

The whole funnel on one screen. In this dataset, $32.9M quoted across 4,626 quotes, of which 30 percent convert. $3.39M booked across 416 orders. $3.37M sitting in backlog. $1.85M invoiced.

Each of those four is split the same two ways: by customer class, which here separates designers, dealers, restaurants, purchasing groups, and government, and by the individual who took the order. Because the split is identical across all four stages, you can see whether a segment quotes well and books badly, which is a completely different problem from one that never gets quoted at all.

Geography, which is usually a surprise

A second page maps revenue by bill-to zip code and lists it by state. In this dataset that is $48.4M spread across every state plus a few international entries, with California, Florida, and Illinois carrying a disproportionate share.

Most companies have a rough sense of where their customers are. Very few have seen it plotted, and the gap between the assumption and the map is often where a territory decision has been quietly wrong for years.

Trend and rep performance

A third page covers invoiced revenue over time: $40.3M across 6,517 invoices, an average invoice of $2,788, charted monthly across four years so seasonality is obvious rather than inferred.

Underneath it, revenue by rep group by year, which shows not just who is producing but whose book is growing and whose is quietly shrinking. That second pattern is much harder to see in a monthly report and much more useful to catch early.

What it takes to build, with a real example

The hard part is almost never the charts. In this very dataset, the state field contains MD, MARYLAND, and M as three separate values, and GU and GUAM as two more. Left alone, every geographic total is quietly wrong and nobody notices because the map still renders.

So the work is mapping those values to something consistent, resolving customers that exist under several names, and defining the funnel stages so a quote counted at one stage is not double counted at the next. Then the totals hold whichever way somebody slices them, which is the only reason anyone ends up trusting the report.

Where the data comes from

Order entry and invoicing out of the ERP. We have built this kind of reporting on Sage, SAP, Epicor, Woodware, Jobscope, and FDM4, and on QuickBooks for companies that have not moved to a full ERP. Quote data often lives in a separate system or in the CRM.

Customer class, rep assignment, and territory attributes usually have to be pulled from wherever they are maintained, which in a lot of businesses turns out to be one person's spreadsheet.

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