At Strike, we implemented a sophisticated AI/ML framework that fundamentally changed how business insights are generated and applied.
We developed a system that could seamlessly integrate and analyse diverse data types:
- Qualitative Sources: User interview transcripts, unmoderated testing videos, support conversations, survey responses, and open-ended feedback
- Quantitative Metrics: User analytics, business revenue figures, marketing performance data, conversion funnels, and engagement patterns
- External Context: Market trends, competitor performance, and seasonal factors
The AI/ML systems we developed delivered concrete business value:
- 23% improvement in prediction accuracy for user conversion compared to traditional analytics
- 18% reduction in customer acquisition costs through more precise targeting
- 15% increase in average order value through better understanding of purchase motivations
- 96% reduction in time-to-insight (from 24 hours to 1 hour)
I've developed frameworks for ensuring AI systems are:
- Transparent in how they use and analyse data
- Free from harmful biases that could impact user experiences
- Respectful of user privacy while still delivering value
- Designed to augment rather than replace human decision-making
I've led initiatives to build AI literacy across product teams:
- Created accessible training programs that demystify AI capabilities
- Developed collaboration models between data scientists and product designers
- Established shared vocabulary for discussing AI features and limitations
My ongoing development in Python and ML is focused on:
- Building more sophisticated predictive models for user behavior
- Creating design systems that can adapt to individual user needs
- Developing frameworks for testing and validating AI-driven features
- Exploring generative design approaches that expand creative possibilities
