The company operated in a high-volume manufacturing environment where customers expected quick responses and accurate pricing for highly configurable products. Although Salesforce CPQ had been implemented earlier, the system was never optimized to match the company’s expanding product lines, evolving pricing structures, or increasing sales velocity.
Over time, the CPQ setup became more of a limitation than a support system. Sales representatives frequently bypassed quoting rules, applied discounts manually, or used outdated documents to build proposals. Because the product catalog had grown without proper governance, reps often selected items incorrectly, missed required components, or created bundles that downstream operations could not fulfill without clarification.
These inefficiencies slowed approvals, created inconsistent customer experiences, and forced Finance to intervene repeatedly to correct pricing or validate discounting. Leadership lacked visibility into quoting quality and trends, and the company struggled to maintain predictable margins. What the organization needed was not simply a functioning CPQ tool, but a fully optimized, guided selling system that could scale with the business.
Although CPQ existed, it was not doing enough work for the sales team. Representatives had to manually identify the right products, apply the correct attributes, and remember configuration rules by memory. New hires struggled significantly because the system did little to guide them, which resulted in a wide range of quote quality depending on who was preparing it. This inconsistency created rework and delayed deals.
Because discount thresholds and approval logic were not automated, reps frequently applied discretionary discounts or entered price adjustments without guidance. As a result, some deals moved forward with unnecessarily low margins, while others were stalled in approval queues for inputs that should have been automatically validated. Finance spent considerable time correcting submissions or rejecting quotes that were structurally inaccurate.
Although proposal templates existed, they required manual editing for each deal. Sales had to adjust formatting, update product descriptions, or insert legal terms manually. Every revision introduced compliance risks and created the possibility that multiple versions of proposals—with inconsistent branding—were being sent to customers.
Since reps frequently created quotes outside the intended rules, downstream systems experienced data inconsistencies. Operations received incomplete configuration details, Finance received inaccurate pricing, and leadership struggled to rely on CPQ reports to understand margin impact, approval cycle time, or quoting patterns. This made forecasting uncertain and slowed strategic decision-making.
The CPQ instance had not kept pace with new product releases, pricing scenarios, bundling complexities, or sales processes. What started as a functional setup gradually became outdated, forcing manual workarounds and inconsistent usage across the sales organization.
The implementation began by addressing the most critical point of failure: the product catalog. Over time, the catalog had grown organically, leaving sales reps with long lists of products, inconsistent naming, and unclear relationships between bundles and components. We redesigned the catalog from the ground up, grouping products into intuitive families and applying clearly defined attributes, variations, and dependency rules.
By structuring the catalog logically and attaching configuration intelligence at the product level, CPQ became significantly more intuitive. Sales representatives no longer needed to interpret product logic manually. Every item carried the right metadata to drive accurate selections during quoting.
The next step was to enhance the configuration engine so CPQ could prevent errors before they occurred. We implemented selection rules, validation rules, and configuration constraints that automatically guided sales users:
Instead of relying on internal knowledge or spreadsheets, sales reps now built quotes that aligned perfectly with engineering, operations, and finance requirements.
To eliminate guesswork, we replaced the old browsing experience with a guided, question-driven flow. Reps answered a structured set of prompts—capacity needs, region, customer segment, or use case—and CPQ instantly filtered the catalog to show only compliant products and configurations.
This not only simplified onboarding for new reps but also ensured that experienced reps followed a consistent, scalable process.
Pricing was one of the biggest sources of inefficiency before optimization. We embedded pricing logic directly into the system, automating:
Einstein's insights were added to flag discount patterns that deviated from trends, helping leadership maintain margin discipline. Approval workflows became faster and far more consistent because thresholds were system-driven rather than dependent on rep judgment.
To eliminate the manual editing of proposals, we redesigned the document generation process. CPQ now produces fully formatted, branded, and compliant proposals automatically. These documents included accurate pricing, product descriptions, legal language, and terms of sale—without requiring any manual fixes. Sales reps could generate polished proposals in seconds, even for complex configurations.
Once the quoting engine was optimized, leadership needed insight into how it performed. Einstein dashboards were implemented to surface trends such as discount ranges, approval bottlenecks, frequently configured bundles, and rep-level quote quality. This intelligence helped the company identify training needs, optimize pricing strategy, and understand how quoting behaviors influenced overall deal velocity.
The transformation fundamentally changed how the organization quoted, priced, and approved deals. What used to be a complex, error-prone workflow became an intuitive, automated, and scalable quoting engine that supported every team involved in the revenue lifecycle.
Below are the key outcomes achieved after the implementation:
If your CPQ feels more like manual Excel with extra steps…
Show me the better version →