Guide
A repeatable framework for structuring value models
Stop rebuilding business cases from scratch every deal. Get a framework your whole team can follow—so quality stops depending on who builds the model.
Most organizations build business cases from scratch every time. Each deal gets a new spreadsheet, a new structure, a new set of assumptions. The result is inconsistency, rework, and quality that depends entirely on who happens to be building the model.
This playbook exists because value modeling should be a discipline, not an art. You'll get a structured approach that works across deal types, clarity on which value drivers actually move decisions, and a workflow that scales beyond your best individual contributors.
A robust value model has four layers, each serving a distinct purpose:
Input Layer: Customer-specific data points that drive the model. These should be gathered systematically during discovery.
Driver Layer: The mechanisms that convert inputs into value. These are the "physics" of your value story—how does doing X lead to outcome Y?
Output Layer: The quantified benefits, costs, and net value. These should be time-phased and risk-adjusted.
Narrative Layer: The story that makes the numbers meaningful. Numbers without narrative are forgettable; narrative without numbers is unbelievable.
Not all value drivers are created equal. Organize yours into categories based on how they influence decisions:
Revenue Drivers: New revenue, accelerated revenue, protected revenue. CFOs love these but scrutinize them heavily.
Cost Drivers: Direct cost reduction, cost avoidance, efficiency gains. Easier to defend but often less exciting.
Risk Drivers: Risk mitigation, compliance, business continuity. Undervalued in most models but powerful when quantified.
Strategic Drivers: Competitive advantage, market positioning, capability building. Hardest to quantify but often the real reason for the investment.
The art is knowing which drivers to emphasize for which audience and which deal.
The key to scalable value modeling is reusable components. Instead of building from scratch, assemble from proven building blocks:
Driver Templates: Pre-built calculations for common value drivers with clear input requirements and assumption documentation.
Industry Benchmarks: Curated data sets for key metrics by industry, company size, and region.
Assumption Libraries: Validated assumption ranges with source documentation and update frequencies.
Output Formats: Standardized deliverables for different audiences (executive summary, detailed model, sensitivity analysis).
Build these once, maintain them continuously, and your team can produce consistent quality at scale.
Value modeling isn't a phase—it's a continuous activity that starts in discovery and evolves through the deal:
Discovery Phase: Gather the inputs you'll need. Use a structured discovery guide to ensure you capture the data points that drive your model.
Hypothesis Phase: Build a preliminary model based on benchmarks and assumptions. This becomes your discussion document.
Validation Phase: Refine the model with customer input. Replace benchmarks with actual data wherever possible.
Defense Phase: Stress-test the model and prepare for scrutiny. Document your assumptions, build your evidence packages.
Evolution Phase: Update the model as new information emerges. A living model beats a static document.
Learn from the mistakes that sink most value models:
Overcomplication: Models with 50 drivers aren't more accurate—they're more fragile. Focus on the 5-7 drivers that account for 80% of the value.
False Precision: Projecting savings to the dollar when your inputs are estimates to the nearest million. Match your output precision to your input quality.
Assumption Hiding: Burying critical assumptions in formulas instead of making them visible and adjustable.
Benefit Stacking: Adding up benefits without accounting for dependencies and overlaps.
Static Snapshots: Presenting a point-in-time model when the customer's reality is constantly changing.
Value modeling at scale requires clear roles and governance:
Model Owners: Responsible for model accuracy and maintenance. Usually solution consultants or value engineers.
Component Owners: Responsible for specific driver templates or benchmark sets. Usually subject matter experts.
Quality Reviewers: Responsible for reviewing models before customer delivery. Usually senior practitioners.
Governance includes:
- Version control for all model components
- Review requirements before external sharing
- Update cadences for benchmarks and templates
- Training requirements for new team members
A structured approach to building value models with distinct layers for inputs, drivers, outputs, and narrative.
A classification system for organizing value drivers based on their impact type.
A phase-based approach to value modeling that evolves throughout the deal cycle.
Audit your current value models against the Four-Layer Architecture
Inventory your value drivers and categorize them using the framework
Identify your most common drivers and build reusable templates for them
Create a discovery guide that systematically captures model inputs
Establish a governance process for model quality and maintenance
Train your team on the workflow and hold them accountable to it
Download the PDF version to reference offline or share with your team.
Download PDF VersionWalk into your next CFO meeting knowing exactly what questions are coming—and how to answer them. You'll learn to spot the assumptions that trigger skepticism, structure value so it invites validation rather than attack, and present with the confidence that comes from genuine defensibility.
Stop modeling everything. Find out which value drivers actually matter for your deal—and which ones to leave out of your business case.
See the operational drag that's slowing your deals—before it shows up in your pipeline. You'll quantify the rework you didn't know was happening, recognize how version sprawl erodes trust with finance, and build the case for governance that actually sticks.