Strategic Goal

To eliminate manual data entry at the quoting front end and simultaneously equip sales representatives with data-driven pricing intelligence, directly within SAP CPQ, to improve consistency, win rates, and margin performance.

Challenges

The company’s quoting process faced two distinct but connected problems, both of which limited sales effectiveness as volumes and complexity grew:

  • Manual quote preparation: Sales representatives had to manually extract product IDs, descriptions, and quantities from incoming customer requests and enter them into SAP CPQ line by line, creating a time-consuming and error-prone bottleneck.
  • Pricing decisions made without data: When configuring products and setting prices, sales representatives had no systematic way to leverage historical sales data. Pricing was largely based on individual judgement, leading to inconsistency across the team.
  • Missed margin opportunities: Without visibility into what had historically won deals at what price points, representatives were poorly positioned to optimize margins or anchor negotiations with confidence.
  • Longer negotiation cycles: A lack of data-backed pricing confidence prolonged deal cycles, as representatives struggled to hold their positions without supporting evidence.

Solution

CLARITY deployed a two-component solution integrated directly with SAP CPQ, addressing both the data entry bottleneck and the pricing intelligence gap in a single, unified workflow.

  • Intelligent document ingestion: Users upload or forward customer request files directly into the solution interface. The platform automatically extracts product IDs, descriptions, quantities, and business partner information, eliminating manual re-keying entirely
  • Automated product recognition and validation: The solution performs real-time recognition and validation of every product against the company’s master data, ensuring only accurate, matched items proceed to quote population.
  • Seamless SAP CPQ quote population: Validated products and quantities are pushed directly into SAP CPQ via API, supporting both new quote creation and population of existing quotes.
  • ML-driven pricing recommendations: When AI mode is selected, the pricing engine analyses 12+ months of historical sales order data and surfaces a comparison table of previously configured and sold products, complete with pricing and relevant parameters. Recommendations are only shown when the model’s confidence exceeds an agreed threshold, ensuring sales teams receive only high-quality, trustworthy suggestions. For new products without sufficient history, the system notifies the user transparently rather than generating unreliable estimates.
  • Side-by-side configuration comparison: Built directly within SAP CPQ, the configuration comparison feature allows sales representatives to evaluate options side by side, including AI-recommended configurations drawn from historical data, before selecting the optimal path and proceeding to finalize the quote.
  • Human-in-the-loop design: Throughout the entire process, the sales representative retains full authority over every decision. The solution informs and supports, not overrides.

Results

The solution delivered measurable impact across the company’s quoting efficiency, pricing consistency, and revenue performance:

  • Faster quote preparation: Quote preparation time was reduced by 73%, freeing sales representatives from manual RFQ entry and allowing them to focus on higher-value activities.
  • Manual data entry eliminated: Sales staff no longer need to manually enter customer requests into SAP CPQ, recovering an estimated 6 hours per representative per week.
  • Greater pricing consistency: ML-driven recommendations reduce pricing variability across the sales team, replacing ad hoc judgement with historically validated reference points.
  • Improved win rates: Sales representatives armed with data-backed pricing are better positioned to close deals on the first proposal, with a projected improvement of 5% or more in quote-to-order conversion.
  • Margin protection: Data-driven pricing reduces unnecessary discounting, supporting stronger margins without sacrificing competitiveness.
  • Accelerated revenue cycle: Faster quote turnaround shortens sales cycles across the board. At a monthly volume of 200 deals with an average deal value of $50,000, a 5% improvement in win rate translates to approximately $6 million in additional annualized revenue.
  • Scalable operations: The company can absorb growing quote volumes without proportional headcount increases, supporting business growth without added operational cost.