Overview
Background & Problem
The legalization of cannabis in Canada has generated increased interest from the general public. However, online cannabis platforms often lack sufficient guidance, and retail sales interactions frequently fall short in providing personalized, one-on-one consultations to understand consumers' needs and recommend the most suitable products.
My contribution
Product strategy
User research
Product design
The team
1 × AI engineer
1 × product designer
Year
2024
Overview
Background & Problem
The legalization of cannabis in Canada has generated increased interest from the general public. However, online cannabis platforms often lack sufficient guidance, and retail sales interactions frequently fall short in providing personalized, one-on-one consultations to understand consumers' needs and recommend the most suitable products.
My contribution
Product strategy
User research
Product design
The team
1 × AI engineer
1 × product designer
Year
2024
Overview
Background & Problem
The legalization of cannabis in Canada has generated increased interest from the general public. However, online cannabis platforms often lack sufficient guidance, and retail sales interactions frequently fall short in providing personalized, one-on-one consultations to understand consumers' needs and recommend the most suitable products.
My contribution
Product strategy
User research
Product design
The team
1 × AI engineer
1 × product designer
Year
2024
Process
Hypothesis, Research & Design
By leveraging an AI training model and feeding it with relevant resources and user input through surveys, we hypothesized that we could provide users with customized cannabis recommendations tailored to their needs.
Through interviews with cannabis users, we identified that their primary concerns were discovering the right strain, potency, and effects. Based on these insights, we designed a user questionnaire to address these issues.
MVP Implementation
For the MVP, we focused on a single, broad question: "Tell us why you use cannabis." Based on the responses, the AI model provided strain recommendations. A more detailed questionnaire will be developed and implemented in the next phase to refine and enhance the recommendation process.
Process
Hypothesis, Research & Design
By leveraging an AI training model and feeding it with relevant resources and user input through surveys, we hypothesized that we could provide users with customized cannabis recommendations tailored to their needs.
Through interviews with cannabis users, we identified that their primary concerns were discovering the right strain, potency, and effects. Based on these insights, we designed a user questionnaire to address these issues.
MVP Implementation
For the MVP, we focused on a single, broad question: "Tell us why you use cannabis." Based on the responses, the AI model provided strain recommendations. A more detailed questionnaire will be developed and implemented in the next phase to refine and enhance the recommendation process.
Process
Hypothesis, Research & Design
By leveraging an AI training model and feeding it with relevant resources and user input through surveys, we hypothesized that we could provide users with customized cannabis recommendations tailored to their needs.
Through interviews with cannabis users, we identified that their primary concerns were discovering the right strain, potency, and effects. Based on these insights, we designed a user questionnaire to address these issues.
MVP Implementation
For the MVP, we focused on a single, broad question: "Tell us why you use cannabis." Based on the responses, the AI model provided strain recommendations. A more detailed questionnaire will be developed and implemented in the next phase to refine and enhance the recommendation process.
Outcome
With the launch of the MVP and no marketing budget, we successfully collected over 20,000 emails within a few months. This validated the concept of customized AI-powered cannabis recommendations. In the next phase, we plan to expand the feature to include a more comprehensive user survey and integrate cannabis social responsibility elements, as outlined in our design.
Outcome
With the launch of the MVP and no marketing budget, we successfully collected over 20,000 emails within a few months. This validated the concept of customized AI-powered cannabis recommendations. In the next phase, we plan to expand the feature to include a more comprehensive user survey and integrate cannabis social responsibility elements, as outlined in our design.
Outcome
With the launch of the MVP and no marketing budget, we successfully collected over 20,000 emails within a few months. This validated the concept of customized AI-powered cannabis recommendations. In the next phase, we plan to expand the feature to include a more comprehensive user survey and integrate cannabis social responsibility elements, as outlined in our design.