Reported impact
Business outcome
IBM shortened quote approval paths, improved pricing confidence, and created an explainable AI foundation for modernizing its sales-pricing process across more business units.
- Productivity Improvement
- Revenue Growth
IBMSoftware / SaaSSales AutomationAI and Machine LearningData AnalyticsProductivity ImprovementRevenue Growth2024 IBMIBM#0f62feSoftware / SaaSGlobalIBM sellers and Business Partners needed a faster, more scalable way to generate reliable product pricing because manual quote assessments relied too heavily on personal experience, incomplete data, and lengthy approval…1 less level of approval needed when Optimal Price appliedMay 14, 20246Sales AutomationCognitive Pricing Analytics0 2024-05-14 6
IBM CIO Organization
Sales Automation
IBM sellers and Business Partners needed a faster, more scalable way to generate reliable product pricing because manual quote assessments relied too heavily on personal experience, incomplete data, and lengthy approval chains.
1 less level of approval needed when Optimal Price applied
41% quotes accepted by clients with Optimal Price
optimized, approved quote within minutes
Solution
IBM's CIO Pricing Systems IT team, Chief Analytics Office, and IBM Research co-developed the Cognitive Pricing Analytics solution to embed machine-learning-driven pricing recommendations directly into seller quoting workflows. The system combines years of IBM transactional history with customer buying patterns, geographic context, product strategy, currency conversion, inflation data, and negotiation history so regression-based models can estimate price sensitivity for each quote. Once a seller configures a quote, the solution returns an AI-driven optimal price and routes it through business rules for rapid approval. This closes the loop inside the existing quoting tools instead of forcing sellers to collect data across legacy and third-party systems by hand. The result is a decision-support layer that turns fragmented pricing data into actionable guidance and shortens the time required to produce a defensible, high-confidence quote at scale.
Data flow
Quote details flow from the seller's quoting tool into Cognitive Pricing Analytics, the solution combines historical sales, customer, market, and negotiation data to run regression-based models, and the recommended price returns to the workflow for rapid approval.
Solution architecture
- Compute
- Cognitive Pricing Analytics Integrates with seller quoting tools to score price sensitivity and recommend an optimal quote using machine learning.
Architecture clues
- IBM completed a proof of concept to extend the solution with watsonx, OpenScale, and OpenPages governance capabilities.
- Regression-based machine learning models use years of transactional data plus market factors such as inflation and currency conversion.
- The solution is integrated directly into seller quoting tools rather than being run as a separate analytics step.
Evidence from the source
"41% quotes accepted by clients with Optimal Price"
"Cognitive Pricing Analytics solution integrates seamlessly with the quoting tools used by IBM sellers and Business Partners."
"optimized, approved quote within minutes"
"uses regression-based machine learning models"
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value80/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth20/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact15/251 metric describe measurable impact.
- Deployment scale5/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/204 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.ibm.com/case-studies/cio-cognitive-pricing-analyticsIBMFirst-party
IBM CIO Organization customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page May 14, 2024 · IBM · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
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