ML-Based Investment Appraisal & Pricing Optimization Platform

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:

1. Discovery & Requirements (4 weeks): Interviewed 12 analysts and 4 senior managers to map workflows, identify pain points, and define success criteria.
2. Data Audit & Preparation (6 weeks): Assessed data quality across source systems, established data governance protocols, and built ETL pipelines.
3. Model Development & Validation (8 weeks): Trained models on historical data, validated predictions against known outcomes, and refined algorithms with analyst feedback.
4. Pilot Deployment (4 weeks): Rolled out to 3 analysts handling HQ deals, iterated based on real-world usage, and refined UX.
5. Full Rollout & Training (6 weeks): Deployed across entire commerical operations team, conducted hands-on training, and established support protocols.

Business Outcomes

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.