About Me.
It's nice to meet you.
Hey there, I'm Martin, a user experience designer from Toronto, Canada.
I'm currently working at Forma AI, building systems to make incentive compensation systems more effective and intuitive.
In the past I've worked with clients in the enterprise, e‑commerce, and startup spaces to create products for the web and mobile platforms.
I am experienced in creating high impact deliverables which help define design considerations and improve products - as well as defining, measuring and tracking success metrics for my projects.
My current focus is on improving enterprise data products.
What I Do.
Featured work.
Forma AI
Automating and optimizing variable sales incentive compensation.
Rubikloud
Actionable data science and analysis for enterprise retailers.
How I Work.
It's an experiment.
Understand, Explore, & Ideate
Getting an understanding of the problem being solved.
What is the problem? Who is the solution for? When will the user be using the solution? Why is our solution better suited than others?
Exploring the market to get an idea about different solutions to similar problems.
And then ideating about ways which we can tie it all together and make a better solution.
Test & Hypothesize
We have an idea. How can we test it using the least amount of bandwidth?
Paper prototypes, drawings, illustrations.
Let's create low-cost methods to help us survey the market and make sure we're on the right track.
After we're done, we can make some predictions.
These will be the base for assessing the performance of our solution post-launch.
Design, Prototype, & Deploy
Let's work at realizing the idea.
Design mockups, and prototypes can give us a tactile feel about the products we're looking to build.
It's another good opportunity where we can touch-base with our end-users to make sure we're on the right track.
We can spec and scope the project and work with the team to productionalize the vision of the product into reality.
And then it's off to the races.
Performance Assessment
It's time to test our assumptions.
Using analytics data and post-launch performance assessment exercises we can determine whether the product is meeting our client's needs.
From there we can take the gathered feedback, adjust, and optimize.