A critical part of Toyota Canada’s direct marketing efforts is measuring how leads generated through campaigns eventually convert into customers. However, matching lead records to customer records proved challenging due to:

• Incomplete or missing key identifiers like email addresses or phone numbers
• Variations and inconsistencies in consumer-entered data
• Siloed data sources with inconsistent formatting standards

As a result, Toyota’s marketing teams faced unreliable campaign performance metrics, limiting their ability to optimize strategies effectively.

CVM developed and deployed a sophisticated AI-based matching system designed to improve lead-to-customer record linkage, even when key identifiers were missing.

Key aspects of the solution included:

• Cross-Reference Algorithms: Built machine learning models that leveraged multiple data points (e.g., name variations, postal codes, VIN numbers, communication history) to predict matches with higher accuracy.
• Data Standardization: Applied preprocessing techniques to clean, normalize, and standardize incoming lead and customer data before matching.
• Incremental Implementation: Rolled out the solution incrementally, continuously gathering feedback from Toyota’s marketing and privacy teams to refine matching logic.
• Privacy and Compliance Focus: Ensured the matching process aligned with Canadian data privacy laws and internal data governance policies.

The model was developed using Python and integrated into Toyota’s existing marketing database environment powered by Microsoft SQL Server.

• Increased Lead Matching Accuracy: Improved lead-to-customer matching rates by over 20%, enabling more accurate campaign ROI measurement.
• Enhanced Marketing Insights: Provided clearer attribution of marketing efforts to sales outcomes, empowering better strategic decision-making.
• Improved Data Confidence: Increased stakeholder trust in reporting and analysis by strengthening data quality and consistency.
• Strengthened Privacy Protections: Matching methods were designed to minimize risk and maintain compliance with evolving privacy standards.

• AI Can Bridge Data Gaps: Even imperfect data can yield high-value insights when combined with sophisticated matching algorithms.
• Iterative Refinement Matters: Continuous feedback and model improvement ensured higher accuracy and stakeholder satisfaction.
• Privacy Must Be Built In: AI solutions must account for regulatory requirements from the start, not as an afterthought.