FP&A Deepdive

Use-case deepdive: In-quarter Bookings / Revenue forecasting

Build a transparent, defensible in-quarter forecast by combining CRM pipeline data with historical conversion behavior, scenario inputs, and overrides for deal-specific intelligence. Do this in under 30 mins with an AI native approach.

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Overview

This model starts with a current-quarter pipeline snapshot, layers in historical conversion ranges by deal-type and pipeline stage, and produces base, bull, and bear forecasts with reactive inputs. The result is a living forecast with traceable assumptions, clear scenario deltas, and a path to shareable executive narratives.

Objectives

  • Use current pipeline snapshot from CRM combined with historical conversion rates to estimate total expected bookings for the quarter.
  • Incorporate historical conversion ranges for different deal-types (e.g. new business vs expansion), pipeline stages, and segments (SME vs. Enterprise).
  • Create base, bull, and bear scenarios with dynamic reactive inputs tied to percentile conversion rates by segment.
  • Create a dynamic mechanism to override deal-specific probabilities where additional information is available.
  • Calculate key metrics like ACV, TCV, and renewal rates and compare results to plan and same-quarter prior year for easy YoY compares.
  • Build easy-to-understand charts and convert the model to an interactive surface (App) for easy sharing with stakeholders.

Source data

  • Current quarter pipeline snapshot
  • Same quarter prior year data
  • Account master
  • Historical conversion rate ranges

Resources

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Trailer

In-quarter Forecasting Walkthrough

Outputs we will build

  • Forecast estimates by segment with dynamic base/bull/bear scenarios.
  • Deal mix and conversion contribution charts for executive readouts.
  • Estimates for KPIs including ACV, TCV, renewal rates, with YoY compares and plan variance.

Full tutorial

Deep-dive walkthrough for the complete forecasting model.

Methodology

  1. 1

    Assemble source data

    Load CRM pipeline, account master, prior-year quarter, and historical conversion data tables.

  2. 2

    Create dynamic inputs

    Create dynamic inputs for base/bull/bear scenarios and deal specific overrides

  3. 3

    Enter workbook knowledge

    Specify metric definitons and modeling methodology as context using a standard company specific approach.

  4. 4

    Build draft model

    Prompt agent to build draft model including key metrics (ACV, TCV, renewal rates), YoY comparisons and charts.

  5. 5

    Validate model and iterate on changes

    Validate the AI generated model step by step and make necessary adjustments including fine-tuning visuals.

  6. 6

    Run scenarios

    Apply manual overrides for specific deals and adjust inputs for various what-if scenarios.

  7. 7

    Interactive surface

    Publish the model as a shareable App with executive filters and narrative notes.

Written tutorial

Follow the guided walkthrough to build the forecasting model from scratch using the starter workbook (linked above).