Overview
Effective data management is a persistent challenge for businesses across industries. For our client, a leading petroleum and petrochemical company in Thailand, the issue was particularly pronounced due to the immense volume of data generated daily. In some instances, for example in our case with the sales team, data collection was relying on manual processes that limited efficiency and scalability.
Manual data entry was both labor-intensive and time-consuming, often leading to errors that resulted in inaccurate quotations. These inefficiencies not only increased operational costs but also posed risks to the client’s ability to manage the growing volume of data effectively. To address these challenges, the client needed a more streamlined and reliable approach to data handling.
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Our solution
We developed and deployed a comprehensive, AI-powered pricing and quotation engine that addressed the client’s key challenges and delivered significant business benefits:
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Higher Close Rate, Increased Customer Satisfaction Our quotation engine helped our client achieve higher closing rates and boost customer satisfaction |
Increased Workflow Efficiency We replaced manual operations with process automation to enhance workflow efficiency |
Eliminated Human Errors We removed human-made errors from quotations with system integration |
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AI-Powered Real-Time Quotation Engine: Leveraging live data sources and machine learning, the engine dynamically adjusted pricing recommendations tailored to each client and product. This allowed the client to optimize pricing strategies, improve bid success rates, and position themselves more competitively in the market. By enabling better data-driven decision-making, the solution also laid the foundation for improving profitability through smarter, more informed pricing adjustments.
Digital Transformation of the Quotation Process: We transformed the client’s entire quotation process, bringing it online and to the cloud. This digitization introduced multiple benefits:
- Enhanced data capture: The new system ensures that all pricing, quotation, and approval-related data is consistently recorded, creating a reliable foundation for analytics and insights.
- Reduced manual errors: Smart validation checks were embedded to minimize human errors during data entry or pricing adjustments, improving overall accuracy and efficiency.
- Audit trail and compliance: A complete audit trail was implemented for all quotation and approval processes, improving transparency, compliance, and accountability across teams.
- Streamlined approval workflows: Automated workflows enforce approval processes, permission controls, and escalation protocols, ensuring governance while speeding up decision-making.
Enhanced Data Integration: By implementing Azure Data Factory, we automated schema validation and cross-source integrity checks, ensuring high data quality. This robust and scalable data foundation not only supports the client’s current needs but also positions them to expand their analytics capabilities for future initiatives, such as predictive performance and market analysis.
Predictive Analytics for Pricing Optimization: We trained custom machine learning models to provide pricing recommendations that balance competitiveness and profitability. These models analyze historical and real-time data to apply discounts where they add the most value, while continuously learning to refine recommendations over time. This approach has empowered the client to strike an optimal balance between increasing win rates and maximizing profits.
Our Approach
- Comprehensive Discovery: Our team collaborated with the client to analyze their existing pricing, discounting, and data integration processes. This included identifying bottlenecks in manual operations and opportunities for automation.
- Data Consolidation and System Integration: Data from manual processes and internal platforms, including SAP, was centralized in a single, reliable repository. Automated pipelines ensured seamless data ingestion and validation, with alerts for errors or readiness for computation.
- User-Centric Design: We categorized and visualized data by product type, presenting insights in intuitive, user-friendly formats. This enhanced accessibility for decision-makers.
- Iterative Development: Feedback loops with the client ensured that business logic and specific requirements were effectively integrated into the solution, allowing us to overcome complexities without delaying delivery.
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Conclusion
The successful implementation of this advanced ML-based pricing platform marked a significant milestone in client’s digital transformation journey. By addressing inefficiencies and enabling data-driven decision-making, we empowered the client to achieve greater operational efficiency, improve profitability, and set the stage for continued innovation in their pricing strategies.
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