Business Context
A digital technology business's commercial operations serve as the commercial intelligence backbone for
Operating Companies across EMEA. The team is responsible for pricing analysis, investment appraisal,
supply chain forecasting, market intelligence, and strategic budget development for multi-million-euro
deals involving enterprise technology deployments.
Before the intervention, commercial analysts spent approximately one hour per deal manually extracting
data from disparate systems, building Excel models, calculating pricing scenarios, assessing rebate
structures, and formulating recommendations. With each analyst handling 10+ deals daily, the process was
not only time-intensive but also prone to inconsistency, human error, and decision delays that impacted
competitiveness in fast-moving procurement cycles.
The Challenge
- Manual data aggregation from CRM, ERP, supply chain, and market intelligence
systems consumed 60-70% of analyst time
- Pricing recommendations lacked consistency across analysts and geographies
- Deal turnaround time averaged 60-90 minutes, creating bottlenecks during peak bid
periods
- Limited capacity for scenario modeling and sensitivity analysis within tight
deadlines
- Inconsistent application of pricing guardrails and margin thresholds
- No systematic mechanism to learn from historical deal outcomes
Solution Design & Implementation
Designed and deployed an ML‑based web application that automated the end‑to‑end investment appraisal and
pricing optimization workflow.
Technical Architecture:
- Data Integration Layer: Automated ingestion pipelines pulling data from Salesforce
(CRM), SAP (ERP), supply chain forecasting tools, and market intelligence databases.
- ML Models: Gradient boosting algorithms (XGBoost) trained on 3+ years of historical
deal data to predict optimal pricing, rebate structures, discount levels, and supply chain
configurations.
- Business Logic Engine: Rule-based guardrails encoding corporate pricing policies,
internally developed algorithms for the commercial team, margin thresholds, competitive positioning
guidelines, and risk parameters.
- Web Application Interface: Intuitive dashboard allowing analysts and operating
companies to input deal parameters and receive instant recommendations with confidence intervals and
scenario comparisons.
- Feedback Loop: System captured actual deal outcomes to continuously retrain and
improve model accuracy.
Implementation Approach:
- Discovery & Requirements (4 weeks): Interviewed 12 analysts and 4 senior
managers to map workflows, identify pain points, and define success criteria.
- Data Audit & Preparation (6 weeks): Assessed data quality across source
systems, established data governance protocols, and built ETL pipelines.
- Model Development & Validation (8 weeks): Trained models on historical data,
validated predictions against known outcomes, and refined algorithms with analyst feedback.
- Pilot Deployment (4 weeks): Rolled out to 3 analysts handling HQ deals, iterated
based on real-world usage, and refined UX.
- Full Rollout & Training (6 weeks): Deployed across entire commerical operations
team,
conducted hands-on training, and established support protocols.
Business Outcomes
| Metric |
Impact |
| Deal processing time |
Reduced from 60 mins to 2-3 mins (95% reduction) |
| Daily analyst capacity |
Increased from 10 to 50+ deals per analyst |
| Pricing consistency |
Improved by 87% (measured via standard deviation) |
| Margin protection |
Estimated £ 800k annual margin preservation |
| Decision quality |
92% of ML recommendations accepted by senior management |
| Time-to-recommendation |
From 60-90 mins to <3 mins (96% faster) |
Table 1: Quantified business impact of ML pricing optimization platform
Strategic Impact:
- Freed analysts from transactional work, enabling focus on strategic market analysis and competitive
intelligence.
- Enabled real-time scenario modeling during client negotiations, improving deal win rates.
- Established data-driven pricing culture, replacing subjective judgment with evidence-based
recommendations.
- Created competitive advantage in fast-turnaround RFP responses.
- Built organizational confidence in AI-driven decision support systems.