Blog · Alternatives and comparisons · Sales teams
Choose sales forecasting software by the inputs, decisions and review evidence your team needs. Compare approaches on the same sales export and check forecast error before committing.
Sales forecasting software should fit the work your team needs to complete: maintaining deal estimates, building a company outlook, or reviewing supplied forecasts against actuals. Choose by the inputs and evidence that job requires, then test the shortlist on a representative sales export.
The useful question is whether the team can explain and reproduce its forecast. A polished chart does not establish that opportunity values, dates and sales totals use the same definition.
Write down the decision the forecast supports. A sales manager deciding which opportunities need attention needs different information from a finance manager comparing monthly sales with the outlook approved earlier.
Specify the forecast measure: orders, bookings, shipments or recognized revenue. Also specify the horizon, update schedule, currency and organizational level. A region-level quarterly bookings forecast is not directly comparable with a monthly revenue ledger.
Then identify the source of truth. Opportunity owners may maintain current deal records, while finance owns actual revenue. The software evaluation must show how those definitions will meet. Keep sales forecast categories consistent before judging any tool's rollup.
Territory design and quota allocation are a separate buying problem; use the workflow checks in sales-planning software when that is the main decision.
These are evaluation categories, not promises that every product in a category supports every requirement. Ask the provider to demonstrate the requirements on your own files.
| Approach | Start here when | Evidence to request | Work to assign internally |
|---|---|---|---|
| Spreadsheet | An owner maintains a limited set of assumptions | Visible calculations, version date and total checks | File ownership and review discipline |
| CRM forecasting workflow | Sellers and managers maintain opportunity estimates | Opportunity detail, category definitions and accountable rollups | Consistent deal updates |
| Planning tool | Finance coordinates assumptions across teams | Scenario assumptions, approval history and reconciliation | Agreed business definitions |
| BI reporting approach | The main task is reviewing prepared measures | Source detail behind each reported figure | Preparation and ownership of the measures |
| File analysis approach | The team brings exported forecasts and actuals | Matching periods, checked totals and traced differences | Supplying comparable files |
Two named examples show why the distinction matters. Microsoft's Excel documentation describes Forecast Sheet as using historical time-based data to create a table and chart of historical and predicted values. That addresses a different starting point from collecting current opportunity judgments.
Microsoft's Dynamics 365 Sales documentation describes forecasting that combines pipeline activity, forecast categories, quotas and hierarchy rollups. Evaluate that kind of workflow when the forecast must be maintained inside the sales process.
Covirage is not affiliated with Microsoft, Excel or Dynamics 365 Sales.
If unit demand or stock availability is the main question, demand forecasting software has a different set of input and evaluation requirements.
Prepare a frozen forecast file and the corresponding actual-sales file for a completed month. Preserve the forecast date. Include stable product and region identifiers, the sales measure, period, currency and forecast amount. Add opportunity identifiers when the review needs deal detail.
Ask each provider to reproduce the same totals before showing a different calculation. Record any changes needed to make the files usable. Those changes become part of the ongoing operating cost, even when the software itself is easy to use.
The following illustrative US distributor export contains September sales forecasts frozen before the month began. The figures are inputs to the evaluation, not results produced by any named product.
| Region | Frozen forecast (USD) | Actual sales (USD) | Actual minus forecast (USD) | Absolute error (USD) |
|---|---|---|---|---|
| East | 400,000 | 380,000 | -20,000 | 20,000 |
| Central | 300,000 | 330,000 | 30,000 | 30,000 |
| West | 200,000 | 170,000 | -30,000 | 30,000 |
| Total | 900,000 | 880,000 | -20,000 | 80,000 |
Every approach should return a forecast of $900,000, actual sales of $880,000 and a net shortfall of $20,000. The check is straightforward: the regional differences sum to the difference between the company totals.
The absolute errors sum to $80,000 because favorable and unfavorable differences do not cancel. Weighted absolute percentage error for this defined regional comparison is:
WAPE = Sum of regional absolute errors / Sum of actual sales
Here it is 80,000 / 880,000 = 9.1%, rounded to one decimal place. Report the level of aggregation alongside that percentage. Calculating error only after combining the regions would hide the offsetting misses.
Use the regional result to test investigation. Ask why Central exceeded forecast, whether West's missing sales moved into another month, and which source rows establish the answers. A tool should make the evidence accessible for the job you selected it to do.
Keep a short acceptance record with the input files, required outputs, responsible reviewer and unresolved differences. Finance should confirm the actual-sales total, while sales operations confirms the forecast snapshot and commercial definitions. A forecast-versus-actual review needs both owners.
For a deal-based explanation, the forecast bridge shows the reconciliation question to ask without turning this selection exercise into a forecasting-method comparison.
Choose the approach that completes the required review with evidence the responsible team can maintain. Record additional requirements separately so a broad feature list does not obscure the forecast job.
Bring your sales export and supplied forecast as a file or Google Sheet; Covirage maps the columns, and its tools compute and check the figures. The external AI model explains the results with every figure traced to its rows. Create a free Covirage account and bring a sample of your own data; account creation takes about a minute.
See forecast analysis for the supporting business context.
Start with consistent periods, clear sales definitions, documented assumptions and a way to compare forecasts with actual outcomes. For opportunity management, also assess responsibility and review workflow. For file analysis, assess column mapping, reconciliation and the ability to trace results to source rows.
A spreadsheet can be sufficient when an accountable owner maintains the assumptions and the review process is manageable. Test whether colleagues can reproduce the calculation, identify the forecast version and distinguish missing values from zero before adding another tool.
Use identical historical information, the same forecast cutoff and the same sales definition. Preserve the original estimates and compare them with later actuals by product or region. Evaluate the work required to investigate a miss as well as the reported error.