The question most pricing tools answer
Ask a price optimization suite what to charge for a SKU, and it will give you a good answer. That's the job it was built for, and the best ones do it well.
But for most B2B manufacturers and distributors, "what should this cost?" isn't the question that moves the bottom line. The bigger question is: what should we do next to make more money? Sometimes the answer is a price change. Often it's a customer who's quietly slipping away, or cash stuck in stock that isn't moving. And the biggest wins tend to sit where all three meet.
That's the gap between optimizing one lever and growing the whole business.
What price optimization suites do well
Vendavo, PROS, and Zilliant are serious enterprise products. They model price elasticity, manage price lists and discount guardrails, and route quotes through approval workflows. For a large company with a dedicated pricing team, that structure is valuable.
They also come with real weight:
- A team to run them. Most need pricing analysts or data specialists to configure models and keep them current.
- One lever. Everything is seen through price. Customer health and inventory position sit in other systems, if they're read at all.
None of that makes them bad tools. It makes them pricing tools.
Why one lever isn't enough
In a B2B business, price never moves alone. Every pricing decision is tangled up with two other things:
- The customer. A price increase that's fine for a loyal, growing account can push an at-risk one out the door. A discount that wins a deal might be unnecessary for a customer who would have bought anyway.
- The inventory. Whether a product is scarce, overstocked, or about to run out changes what the right price is. Pricing slow stock the same way as fast stock leaves money on the table in both directions.
A tool that only sees price can't see those connections. It optimizes the number in front of it, while the reasons that number should change sit in the CRM and the ERP, unread.
That's why r-daniel reads price, customers, and inventory together. It cross-references all three automatically, every day, and surfaces the moves that matter most: the customer to call, the price to change, the stock to reorder. No data scientists or AI specialists are needed to run it. It discovers; your team decides.
A real example: the price nobody would have raised
A food and beverage distributor had a slow-moving item. The instinct, and the move most pricing logic would suggest, was to cut the price to get it moving.
r-daniel recommended the opposite: raise it by 9.1%.
That recommendation didn't come from looking at price alone. It came from reading the item's pricing against how its customers were buying and how its stock was moving, the kind of cross-check a team running reports by hand rarely has time to make.
The result: $45K more per month. A price-only view would most likely have pointed the other way.
Side by side
- What it looks at — Price optimization suites: price. r-daniel: customers, price, and inventory, together.
- Who runs it — Suites: pricing analysts or data specialists. r-daniel: runs on its own; no data scientists needed.
- What you get — Suites: price recommendations and guardrails. r-daniel: ranked discoveries with the next move: the customer to call, the price to change, the stock to reorder.
- How it reaches your team — Suites: a pricing workbench. r-daniel: Dani Go email, Ask Dani, the Dani App, and the r-daniel Intelligence Hub.
- Who decides — Suites: approval workflows. r-daniel: your team; r-daniel never changes prices in your ERP on its own.
When a pricing suite is still the right call
If you already have a mature pricing team and your main problem is governing thousands of quotes and discounts, a dedicated suite may be exactly what you need. Some companies run both: the suite manages price execution, and r-daniel finds where the profit is.
Three questions help you decide:
- Is price really your biggest lever? Or are churn and stuck inventory costing you as much as pricing?
- Do you have the people to run it? A suite pays off when a team can configure and maintain it.
- How soon do you need results? r-daniel is typically up and running in about 4 weeks.
If the answers point to more than price, fewer specialists, and faster results, one lever isn't enough.
The AI to grow your company
Price optimization suites answer one question well. r-daniel is built for the bigger one: where is the money in your business, and what should you do about it today?
See what it finds in your own data. Book a trial: one day, your data, no commitment. Or explore how Price Decision IQ works alongside Customer Growth IQ and Inventory Decision IQ.