European Automaker Accelerates Pricing Decisions with Neo4j on AWS
Staying competitive in the auto manufacturing industry requires more than just innovative vehicles. It demands pricing strategies that can adapt to markets, product lines, new features, and accessories. This challenge became critical for one global automotive business as they sought to maintain their position as an industry leader.
The company, known for their premium vehicles, motorcycles, and technology, faced a daunting task: centralizing pricing data across their global portfolio of products and markets. Dealership staff used the data to generate accurate quotes for customers. Regional managers used it to set competitive prices in local markets. Marketing teams relied on it to create effective promotions.
The company’s existing relational database struggled under the weight of data relationships. Pricing updates that should have taken hours were stretching into days. Market-specific promotions were difficult to implement without risk of errors, limiting the company’s ability to respond to competitor deals. The business needed a dynamic internal service that could respond to market changes, support global expansion, and provide an accurate price to the right person at the right time.
In an industry where a mispriced feature or an outdated promotion could lead to lost sales or eroded margins, the company needed a solution that could handle the complexity of their data while providing the speed and accuracy their business demanded. This realization led the company to explore graph database technology, and ultimately, to Neo4j.
The Limits of Traditional Databases
The automaker chose Neo4j for its unique ability to represent and query complex relationships. Unlike relational databases, which struggle with highly interconnected data, Neo4j’s graph structure was perfectly suited to model the intricate connections between vehicles, features, accessories, and market-specific pricing.
With Neo4j, the business could finally represent their pricing data in a clear model. Each vehicle, feature, or accessory became a node, with relationships representing the connections between them. This made it incredibly intuitive to model even the business’ most complex pricing scenarios.
The impact was immediate and profound. Queries that once took hours now returned results in seconds. The team could easily visualize and navigate the relationships between different elements of their pricing structure, leading to new insights and more agile decision-making.
While Neo4j’s Community Edition initially served the company well, their growth and increasing data complexity soon demanded a more robust, cloud-based solution. The decision to migrate to Neo4j AuraDB on AWS marked a turning point in their digital transformation journey.
Above: European Automaker’s AWS Cloud Architecture
Migration to AWS brought several key benefits:
- Scalability: As the company’s data volumes grew, AuraDB on AWS allowed them to easily scale their resources up or down, ensuring optimal performance without over-provisioning.
- High Availability: The cloud-based solution provided better uptime and disaster recovery options, critical for a system that needed to be accessible 24/7 across global markets.
- Security: AWS’s advanced security features, combined with Neo4j’s role-based access control, ensured that sensitive pricing data was protected while still allowing appropriate access across different markets and user roles.
- Managed Service: By opting for a managed service, the company’s IT team could focus on innovation rather than database maintenance.
The move to AWS also aligned perfectly with the company’s broader cloud-first strategy, facilitating better integration with other cloud-based services and applications.
Transforming Pricing Operations
The implementation of Neo4j AuraDB on AWS has had a transformative effect on the company’s pricing operations. What was once a cumbersome, time-consuming process has become agile and responsive.
The numbers tell a compelling story:
- The system now manages over 15 million nodes and 70 million relationships, representing the company’s entire product portfolio across all markets.
- It handles 3 million database calls per month, providing real-time pricing information to internal teams and dealerships.
- Data loading times have been reduced from 24 hours to just a couple of hours, allowing for more frequent updates and greater agility in responding to market changes.
- The solution has achieved 99% uptime, ensuring that pricing information is always available when needed.
Perhaps most importantly, the new system has enabled the company to expand its operations more efficiently. They’ve scaled to support over 100 portfolios across multiple markets, with plans for further expansion.
The success of the pricing project has sparked interest across the organization. The company is now exploring additional use cases for graph technology, including supply chain management and customer relationship modeling.
As they look to the future, the company sees their Neo4j on AWS solution as a key competitive advantage. By using the power of graph technology and cloud computing, this global automotive leader has not only solved its immediate pricing challenges but has also laid the foundation for more agile, data-driven decision-making across its entire global operation. In an industry racing towards the future, they’ve ensured they have the engine to stay ahead.
