The Challenge
Aclaro needed an intelligent recommendation engine that could evaluate customer campaigns by VIN, recommend service actions, calculate retention discounts dynamically, and optimize offers based on product age, end-of-month timing, and financing status. The business rules changed frequently, requiring a system where non-technical users could modify recommendation logic without code deployments.
Our Approach
Daiviksoft built a JBoss Drools (BRMS) rules engine with REST APIs, enabling business analysts to manage recommendation rules through Excel spreadsheets. The system evaluates customer campaign history against VIN selection criteria, applies multi-factor discount calculations (product age, retention profiles, end-of-month timing, finance status), and routes service queue actions. Multiple rule set versions (v1.0–v4.0) demonstrate iterative refinement of the recommendation logic.
Key Deliverables
- VIN-based vehicle selection and recommendation rules (3+ iterative versions)
- Dynamic discount calculations: product age, retention, end-of-month, finance discounts
- Customer retention profile management
- Service action rules and queue management
- Excel-based rule configuration for business analyst self-service
- JSON REST API for integration with downstream systems
- Facts Table evaluation model for complex rule chaining
- SAM2 rule sets for advanced scenarios
- Iterative rule versioning (v1.0–v4.0)
Results & Impact
Aclaro launched a production recommendation engine where business analysts modify vehicle selection and discount rules through Excel spreadsheets without developer intervention. The Drools-based architecture processes complex multi-factor recommendations in real time, enabling dynamic customer retention offers optimized for each campaign context.