A fair account of what a spreadsheet does well for sales analysis, the point at which it stops, and the four signs a team has passed it: the same measure computed two ways by two people, a monthly refresh that takes days, a roll-up that does not reconcile and nobody knows why, and a number in a deck that nobody can trace. What changes when the measures are computed from stated definitions on every upload, and what does not need to change at all.
A spreadsheet built the first coverage report at most companies, and it was the right tool. At some point it stopped being the right tool, and nobody noticed because the report kept appearing. This guide gives a fair account of where a spreadsheet is enough, the four signs it has stopped, and what changes when it does.
| Condition | Why it works |
|---|---|
| One person computes the measures | One definition, in one head |
| Under a few hundred accounts | Formulas hold; nothing times out |
| Refresh takes under an hour | The person remembers the steps |
| The roll-up reconciles | The SUM ranges are right, this month |
| Numbers are traceable | The person can point at the cells |
For a founder-led sales team with an analyst, this is most of the first two years.
One measure, two values. Two people built coverage. One counted emails; one did not. Both are in decks. The argument in the meeting is about which spreadsheet, and it is the definition that was missing, not the tool.
A refresh that takes days. The steps are in one person's head, the SUM ranges have to be extended, the pivots refreshed, the subtotals stripped, and the totals compared by eye. Days, every month, and a person who cannot take a holiday in the first week.
A total that disagrees with finance. Region does not equal the sum of teams; the ledger says $4.18m and the sheet says $3.96m. Nobody knows which SUM range, which hidden row, which overwritten formula. An afternoon, sometimes a week, and the number is patched rather than fixed.
A number nobody can trace. The board asks where 27 percent came from. The sheet has been copied, pasted as values, and emailed. The rows are gone.
| Spreadsheet | Computed tables |
|---|---|
| Definition in the formula | Definition written, versioned, cited |
| Columns mapped by hand each month | Mapped once; reused |
| Roll-up checked by eye | Identity checked on every upload; failures listed |
| Number in a cell | Number citing its table, rows and version |
| Refresh in days | Refresh in minutes; the person reads six validation lines |
The exports, the person, the questions. The ledger and the CRM produce what they always did. The analyst drops the same files. The measures are the ones the spreadsheet was trying to compute, now with the definition on the page.
| Month | Spreadsheet | Computed |
|---|---|---|
| Refresh time | 3 days | 20 minutes |
| Reconciliation issues found by hand | 6 | 0; 3 exceptions listed automatically |
| Measures with two values | 2 | 0 |
| Board questions about provenance | 4 | 0; every table cites |
A bigger spreadsheet. The four problems, larger.
A dashboard tool without definitions. The four problems, with charts.
The spreadsheet thrown out. It was the right tool for exploring, and it still is.
Moved before the definitions exist. The tool computes a measure nobody agreed.
Covirage takes the same exports the spreadsheet did, maps them once, checks the identities on every upload, and computes from stated definitions with a citation on every figure. The Excel analysis solution describes the setup, and the five checks guide covers what to run on the spreadsheet in the meantime.
Exploring a question once, building a model with assumptions the reader can change, and presenting a small table. It is fast, everyone has it, and for a hundred accounts and one analyst it is the right tool. The problems start with repetition, multiple hands and scale.
The useful kind is not. The difference is that definitions are written and versioned, the mapping from columns to roles is done once and reused, the roll-up identity is checked automatically, and every figure cites its rows. A dashboard tool without those is a bigger spreadsheet with charts, and it has the same four problems.
The exports. The ledger and the CRM produce the same files they always did. The person who dropped them into the spreadsheet drops them into the tool. What changes is what happens after the drop.