Pricing strategy and realized price are different disciplines
One high-tech manufacturer using Conga recommendations saw 7.4% higher gross margin
One distributor's disconnected tools cost ~$38M yearly
Closed loop connects control, intelligence, execution, and insight
Your pricing strategy can be right and your realized price, the price the customer ultimately accepts after discounts, approvals, and sales negotiation, can still be wrong.
Companies pour real effort into figuring out what the right price should be. But commercial value isn't created the moment a price gets set. It's created when that price makes it into a real quote and survives all the way to the deal the customer actually accepts. Across that commerce chain, from pricing strategy to configuration, quoting, approvals, and negotiation, there are multiple points where the intended price can get lost.
And that final stretch is where things often break. Pricing has a last-mile problem, and many organizations lose margin in it without fully seeing where or how.
The handoff between strategy and execution tends to fail in predictable ways. Pricing data and logic sit in one set of systems while selling happens in another. Price updates take too long to reach reps. Sellers don't trust the guidance they're given, so they override it. Approvals happen outside any governed workflow. And the pricing team has little visibility into what happens once guidance leaves their hands.
The result is a gap between the price the business intended and the price the customer got. That gap is where revenue and margin can quietly leak.
It usually comes down to 3 disconnects:
The control gap: pricing rules and approvals aren't governed consistently across channels, segments, and regions. The intelligence gap: quoting runs fine, but prices stay static or rule-based with no data-driven guidance for the specific account or deal. And the execution gap: a sound strategy exists, but complex configuration, manual quoting, and disconnected approvals stop sellers from applying it at the point of sale.
This isn't abstract. One global distributor of security and AV products estimated its disconnected tools and processes were costing them nearly $40 million a year, including roughly $17.6 million from margin leakage and manual overrides and approximately $20.3 million from manual price-setting. Its pricing team had good ideas for responding to the market but couldn't implement them in a 40-year-old ERP without calling a consultant every time.
A medical device manufacturer managed 12 product families and their configuration rules in Excel. Reps spent 1 to 4 hours just hunting for the right pricing sheet, and getting a quote approved could take a week and a half.
The gap doesn't show up in one place. It surfaces as over-discounting, slow price changes, long approval cycles, inconsistent offers, and manual busywork all at once.
It helps to separate three questions that usually get treated as one. Pricing strategy asks: what should we charge? Commercial execution asks: did that price actually reach the customer? Pricing realization asks: what did we sell at, and what happened to margin?
Here's the proof that the distance between them matters. At one high-tech manufacturer, deals that used Conga pricing recommendations delivered 7.4% higher gross margin than deals that didn't. High-adopter reps realized 13.4% higher margins than low adopters. Same strategy, same products. What varied was whether the guidance reached the seller and the deal.
Which points to something easy to miss: adoption is part of realization. A brilliant pricing recommendation does nothing if the seller never sees it, trusts it, or uses it. Put the guidance with explainability inside the seller's workflow, and adoption becomes less of a separate change-management project.
Closing the gap takes more than wiring two systems together. It takes a connected commerce chain supported by a unified operating model where pricing decisions move cleanly into execution, and deal outcomes flow back to sharpen the next decision.
That connection creates a closed loop across four stages, each with a distinct job:
Control. Centralize and govern pricing strategy, rules, price lists, and approvals in one place instead of scattering them across spreadsheets and ERP customizations. When costs shift, you update once and quickly, rather than rebuilding the change in every downstream system.
Intelligence. Governance creates consistency, but consistency alone doesn't tell you the right price for a specific deal. AI-powered price optimization adds real-time, account-specific guidance based on your own business data, including win-rate probability and margin and revenue impact insights, so reps aren't pricing on gut feel.
Execution. This is where Configure, Price, Quote (CPQ) earns its place. It brings governed, optimized pricing into the workflow where sellers configure products, build quotes, manage discounts, and request approvals, so the seller doesn’t have to leave the quote to go hunting for a price or decode a spreadsheet.
Insight. The job isn't done when the quote goes out. A price waterfall analysis shows where value was gained or lost between the intended price and the realized price, which lets pricing teams tell a strategy problem from an execution problem, and feed that back into the next decision.
That's the closed loop; Price Optimization and Management (POM) helps you set and govern the strategy, CPQ applies it at the moment of sale, and analytics measures what happened so you can improve what's next.
Results show up at every stage of the loop. Fonterra, New Zealand's largest company, moved off manual pricing and SAP workarounds to a single pricing-and-selling platform with real-time commodity data, reducing pricing errors and giving sales teams data they actually trust. An industrial manufacturer replaced 50 legacy systems with Conga Smart CPQ and dropped service quote configuration from 2 days to 30 minutes, with 80% fewer quoting errors. Belden, a global manufacturer of networking and connectivity solutions, consolidated a dozen fragmented pricing systems into one and now responds to tariffs in days instead of weeks.
And one Fortune 500 distributor, where reps were overriding pricing guidance 60 to 70% of the time, optimized $7.2 billion in revenue and lifted margin 120 basis points after connecting pricing to execution.
Different companies. Same pattern. The value showed up when the intended price and the realized price stopped drifting apart.
You don't have to fix everything at once. Most organizations are advanced in one dimension and disconnected in another. The move is to find the widest gap between pricing and execution, and close that one first.
A few questions worth sitting with: How often are your pricing rules overridden, and can anyone see where? Do sellers know the right price for the deal in front of them? Is that guidance embedded in the quoting workflow, or living in a spreadsheet somewhere? And can you compare intended versus realized price at all?
If you can't answer those with confidence, that's your last mile. And it's where your next margin point is hiding.
Ready to close the gap between the price you set and the price you get?
It's the gap between the price a business intends to charge and the price the customer actually receives. A pricing strategy can be sound, but it loses value on the last mile, the stretch where guidance has to move into the seller's workflow, shape the quote and negotiation, and become the accepted price. The gap opens through 3 disconnects: control (pricing isn't governed consistently), intelligence (no data-driven guidance for the specific deal), and execution (sellers can't apply the strategy at the point of sale).
Because setting the right price and getting the right price are different jobs. Value isn't created when a price is calculated; it's created when that price survives into the transaction the customer accepts. The proof is measurable: at one high-tech manufacturer, deals using Conga pricing recommendations delivered 7.4% higher gross margin than deals that didn't, with the same products and the same strategy. The variable measured was whether the guidance reached the deal.
POM and CPQ cover two halves of the same problem. POM determines, governs, and optimizes what the business should charge. CPQ turns those decisions into accurate, competitive offers by embedding pricing into configuration, quoting, approvals, and negotiation. POM helps you decide the price; CPQ helps you bring it to the customer. Connected, they close the gap between intended and realized price.
It's a connected operating model with 4 stages. Control centralizes and governs pricing. Intelligence (price optimization) adds real-time, deal-specific guidance. Execution (CPQ) brings that pricing into the seller's workflow. And insight (pricing analytics, like a price waterfall) measures how the intended price became the realized price, then feeds the lesson back into strategy. No stage works in isolation; together they form a continuous cycle from pricing intent to commercial outcome.
Because a sophisticated pricing recommendation has no commercial impact if sellers don't see it, trust it, or use it. At one Fortune 500 distributor, reps were overriding pricing guidance 60 to 70% of the time, which fed steady margin leakage. The fix is bringing guidance to the moment of decision, inside the quoting workflow, so adoption becomes part of the commercial process rather than a separate change-management effort. When they connected pricing to execution, the company optimized $7.2 billion in revenue and grew margin 120 basis points.
