Sales forecasting software RFP

Forecasting software RFPs tend to ask about AI predictions and dashboards, and skip the parts that decide whether the forecast gets better: whether snapshots are kept, whether roll-ups handle overlays without double counting, and whether the tool can tell you afterwards who was accurate. This RFP weights methodology, roll-ups, snapshots, and accuracy reporting first, and tests them on your own pipeline and hierarchy.

Formats:
PDF + CSV + web view
Sections:
5
Updated:

What you get

  • Weighted requirements for forecast methods, categories, roll-ups, overrides, snapshots, and accuracy reporting
  • The standard five-level response scale with required evidence
  • Scripted demo scenarios using your hierarchy and last quarter's pipeline
  • Commercial and exit questions, including export of snapshot history
  • A note on why accuracy reporting matters more than the prediction model

Who it's for

  • RevOps leads selecting a forecasting tool
  • VPs of Sales and CROs who call the number
  • Finance partners who plan on the forecast

What's inside

  1. 1

    Requirements

    8 columns, 10 worked example rows

  2. 2

    Scripted demo scenarios

    5 columns, 5 worked example rows

  3. 3

    Commercial and exit questions

    7-point checklist

  4. 4

    RFP timeline

    3 columns, 7 worked example rows

  5. 5

    Why accuracy reporting outranks the prediction model

    Guidance notes

Preview of section 1

Requirements

Response scale: 5 Native, 4 Configuration, 3 Custom, 2 Roadmap (give a date), 1 Not supported. Weights across all requirements add up to 100; the example rows sum to 67, leaving 33 for your own. Requirement score = Response x Weight; vendor total = sum of requirement scores / 5, out of 100.

IDAreaRequirementPriorityWeightVendor response (1-5)ExplanationEvidence link
FM-01MethodologyRep call, weighted pipeline, and model-based forecast shown side by sideMust8
FM-02MethodologyForecast categories configured separately from opportunity stageMust6

The preview shows part of section 1. The full template has all 5 sections (4 not previewed here), with blank rows ready to fill in. Download the full template

How to use it

  1. 1

    Define your forecast process first

    Write down your forecast categories, who submits, who can override, and when the call is locked. A tool cannot fix a process that is not defined, and vendors will configure their default if you do not give them yours.

  2. 2

    Test roll-ups on your real hierarchy

    Give vendors your actual reporting structure, including overlays and split deals. Double counting in roll-ups is one of the most common reasons a forecasting tool's total does not match finance's number.

  3. 3

    Ask for last quarter's accuracy

    Have shortlisted vendors load last quarter's weekly snapshots, or replay it from field history, and report forecast versus actual by rep and method. If the tool cannot measure accuracy, you cannot tell whether it improves it.

  4. 4

    Score AI predictions on explanation, not confidence

    A predicted number with no reasons is hard to use in a forecast call. Score whether a manager can see which deals drive the prediction and why.

Frequently asked questions

What should a forecasting software RFP include?

Weighted requirements for forecast methods and categories, hierarchy roll-ups with overlays, overrides with audit history, weekly snapshots, change reporting, accuracy and bias reporting, CRM sync, plus scripted demos on your own pipeline and commercial and exit questions.

How do you evaluate sales forecasting software?

Define your forecast process first, then test each shortlisted vendor on your real hierarchy, overlays, and last quarter's pipeline. Score heavily on snapshots and accuracy reporting, and require AI predictions to explain which deals drive them.

What is a forecast snapshot?

A saved copy of the forecast and the opportunities in it at a point in time, usually weekly. Comparing snapshots shows what changed, and comparing them with actual results measures forecast accuracy.

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