CPG | Customer Analytics
Customer 360 Profiling
Objective
CPG companies face an ardent need to create unified, dynamic, and actionable customer profiles to develop deep customer insights, predict behaviors, and enable personalized engagement strategies while ensuring data privacy and regulatory compliance.
Challenges
- Fragmented customer data across multiple systems.
- Data quality and standardization issues.
- Real-time profile updating requirements.
- Privacy and compliance regulations.
- Identity resolution across channels.
- Integration of offline and online behaviors.
- Scalability for millions of customer profiles.
- Historical data migration and consolidation.
Solution Proposed
- The solution implements Entity Resolution algorithms, Collaborative Filtering, and Deep Learning Neural Networks for customer behavior prediction.
- Process vast amounts of structured and unstructured data while maintaining accuracy in profile creation.
- Development of a self-learning profile enrichment engine that continuously updates customer attributes and preferences using both explicit and implicit data signals, while maintaining data lineage and privacy compliance.
Outcome
- 85% improvement in customer profile accuracy.
- Increase in cross-sell/upsell effectiveness.
- Reduction in customer data consolidation time.
- Improvement in marketing campaign performance.
- Significant decrease in customer acquisition costs.
- Faster time-to-insight for customer analysis.
Design & Thinking Wins
- Real-time profile enrichment engine.
- Identity resolution framework.
- Multi-channel data integration hub.
- Privacy compliance module.
- Behavioral analytics engine.
- Predictive modeling system.
- Customer segmentation framework.
- Journey mapping capability.
- Relationship graph database.
- Custom attribute creation tool.
- Dynamic dashboard generation.
Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
