Accelerating Customer Onboarding with Machine Learning

Introduction

A leading technology company managing over $1 billion in transactions faced a major bottleneck: customer onboarding took several weeks due to the manual process of mapping customer product catalogs to a master list of 80,000 beverages. The goal? Reduce onboarding time to just three days by automating product matching with machine learning (ML).

The Business Problem

For retailers, distributors, and suppliers, speed is everything when adopting new platforms. The slow, manual data mapping process was a critical roadblock, delaying value realization for customers. The company needed an ML-driven solution that would:

1. Automate product matching to eliminate manual effort.
2. Continuously improve through a feedback loop.
3. Deliver results in near real-time to drastically shorten onboarding.

The ML Solution

Our team built a machine learning model for probabilistic product matching using AWS SageMaker's Object2Vec embeddings. The solution featured:

Automated Product Matching

• ML model automatically mapped new customer products to the master catalog.
• Used fuzzy matching to handle variations in product names.
• Assigned confidence scores; high-confidence matches were auto-approved, low-confidence ones flagged for review.

Feedback Loop for Continuous Learning

• Integrated directly into the company's UI.
• Admin corrections were captured and fed back into the ML model.
• Automated retraining cycles via AWS SageMaker Pipelines, improving accuracy over time.

Scalable, Efficient ML Infrastructure

• Historical data stored in Aurora MySQL & S3.
• Infrastructure managed with CloudFormation.
• Data encrypted at rest and in transit.

Results & Business Impact

Faster Onboarding:

• Reduced from weeks to three days, achieving the company's goal.

Increased Accuracy:

• Initial top-1 match accuracy: 57.3% → After retraining: 68%
• Top-5 match accuracy: 75.4% → Improved to 86%

Operational Efficiency:

• Eliminated tedious manual mapping, enabling faster customer adoption.
• Feedback loop ensured ongoing accuracy improvements.

Key Takeaways

1. Machine learning delivers real business value when applied to the right problem.
2. Speed matters. The biggest impact wasn't just automation—it was reducing onboarding time to three days.
3. A feedback loop is essential to continuously refine ML models and ensure long-term reliability.

Final Thoughts

This project proved that ML can drive immediate business results, not just a vague promise or something far off in the future. By embedding machine learning into operations, we transformed a slow, manual process into a scalable, automated system - delivering faster time-to-value and setting the stage for future optimizations.