Machine Learning & Personalization
Allē Flash MVP
Overview
A second iteration MVP launch of a scan-to-win personalized technology to selected aesthetics providers to validate if this approach could boost sales of Allergan Aesthetic products, a first in the industry experiment.
I led the Mobile team in developing, running, and measuring the experiment, focusing on better technology stability, personalized features with machine learning, and an improved user experience for both providers and patients.
The Results
Successful launch of Allē Flash limited launch to 60+ aesthetics offices for 2 months with improved user experience and increased conversions from initial MVP
Improved user experience and satisfaction
20% increase in new user registration
43% conversion rate, a 25% increase from previous experiment
The results of this experiment indicates the ability of this product to convert interests to treatments. Following this experiment, Allē Flash is to scale up to general availability and expected to be a permanent offering in the app.
The Work
Conducted comprehensive data analysis and user interviews, revealing the need for more personalized offers and redemption window based on the treatment
Worked with data scientists to implement machine learning models based on treatment history to personalize rewards, leveraging A/B testing to experiment with different variables
Managed design and development deliverables, while aligning to business needs and timelines
Coordinated with marketing teams to develop materials and training models that boosted initial adoption
Set up detailed KPIs and tracking mechanisms to monitor usage and performance to inform product decisions
