CPQ Testing with Automated Validation Reports

Testing CPQ systems has always been a challenge. As product configurations grow more complex and rule logic more dynamic, traditional methods like manual testing or preview-based spot checks can no longer keep up.

That’s why we created a validation report driven by the CPQ engine itself — a report that’s fast, accurate, and transformative.

The Pain of Testing CPQ

CPQ systems are built to handle rich and conditional product logic. But that strength also makes testing hard. How do you know which options should be available? Which ones are hidden by logic? What happens if a rule was added that accidentally broke availability for a common scenario?

Until now, testing CPQ logic has involved trial and error, browsing through configuration previews, and relying on expert guesswork. It's tedious, hard to scale, and leaves room for gaps — especially when working across hundreds of product models or market-specific variants.

The Idea: An Automated Validation Report

We built a validation report that answers a simple but powerful question: What does the CPQ engine think is valid right now?

Here’s what our report does:

• Connects directly to the CPQ engine using the Customer Self-Service API
• Simulates real configuration steps and model inputs
• Recursively inspects the result
• Finds all valid ("green") domain elements for specific parameters
• Outputs a clear, structured overview of available options per product model

What the Report Shows

Here’s an example of what the report might look like:

This report shows at a glance which options are available per parameter, per model — and where logic might be broken or incomplete.

The Value It Creates

⏱️ Saves Time

Testing configurations manually across dozens of models can take hours. This report generates the same insight in seconds.

🔍 Exposes Logic Errors

Missing green values point directly to misconfigured conditions, broken references, or over-restricted rules. It also shows inconsistencies in domain setup.

🧪 Enables Real CPQ Testing

This is not UI testing or field coverage. It’s real-time logic validation from the CPQ engine itself. This is what makes the report trustworthy.

📈 Improves Quality

Modeling becomes more robust. Product managers and modelers gain instant insight into what's working and what isn’t. Releasing changes without unintended side effects becomes realistic.

🤝 Aligns Teams

The report can be shared across product, sales, QA, and operations — everyone sees the same truth, without needing access to admin tools or Studio environments.

How It Works

At its core, the report uses the Customer Self-Service API to:

1. Commit a configuration step (e.g. model selection)
2. Trigger a logic evaluation via step changes
3. Receive the full parameter structure
4. Recursively search for target parameters
5. Collect and display all domain elements with state = "green"

This is real, runtime CPQ logic — not a static export or guesswork. The engine answers directly, and the report reflects exactly what is currently possible.

Use Cases

• 🧭 Validate model coverage before go-live
• ⚙️ Compare configurations across product families
• 📦 Ensure markets or pricelists haven’t lost access to key options
• 🔄 Monitor model logic changes over time (e.g. before/after a deployment)
• 📋 Audit whether rules are applied consistently across parameter families

How You Can Use This for Your CPQ

If your CPQ system exposes a configuration API and uses concepts like commits, steps, and parameters, you can build this type of validation report too. Here’s what you need:

• A way to send configuration inputs (e.g. product model, customer type)
• A way to receive and parse the full configuration structure
• A recursive function that walks through the structure looking for domain elements
• A way to display or export the results (Excel, BI, CSV, etc.)

From there, you can build your own logic visibility system — and uncover insights that are otherwise hidden.

Why We Built This

At cpq.se, we’ve seen firsthand how hard it is to get clear visibility into CPQ behavior without doing everything manually. We built this report because we needed a way to:

• Understand option availability per model
• Catch misfires early
• Give non-technical stakeholders visibility into CPQ logic
• Support faster feedback loops in modeling and testing

We’ve already used it to uncover invalid rules, catch broken variants, and simplify the rollout of major configuration changes — all without loading a single product into the UI.

Conclusion

This isn’t just a utility. It’s a new way of thinking about CPQ quality. With an automated validation report, you move from “hoping it works” to knowing what works. And when it doesn’t, you have the exact data to fix it — fast.

If you want help implementing something like this for your own CPQ, or you just want to see it in action, get in touch.