Slack Pricing & Growth Experiment

Unlocking Growth through Data-Driven Discounts

As a member of Slack's growth team, I spearheaded the development of ML-Discount — an AI-powered solution to optimize pricing strategies. The goal was simple yet impactful: increase conversion rates by matching users with personalized discount offers.

1. Data Collection

The project began with systematic A/B testing of random discount amounts to gather insights into user behavior and price sensitivity. This controlled experimentation phase established the baseline data needed to understand how different discount levels influence conversion across user segments.

2. Model Training

Using the collected data, I built and evaluated various ML models to predict the optimal discount for each user. After rigorous experimentation, I arrived at an algorithm that outperformed random discounts — delivering the right discount, to the right customer, at the right time.

3. Deployment & Validation

ML-Discount was released into production. Through continued A/B testing, it proved successful in boosting conversions and unlocking new revenue opportunities. The system demonstrated measurable lift over the baseline random discount strategy.

4. Impact & Takeaways

At its core, ML-Discount illuminated how data and AI can be leveraged to understand customers and grow business. The project sharpened my instincts for identifying high-impact data solutions that merge analysis with action. Most importantly, it exemplified my passion for translating numbers into narratives that engage and compel.