Cindy La

I’m a product designer who believes in the role of empathy in design and innovation.


I enjoy using a psychological and holistic systems perspective to find solutions that ultimately help people reach their hidden human potential. Product design challenges me to rethink our own ideas of history and shape an ongoing dialogue of human attitude and behaviour. You can find me talking about psychology, Drake lyrics, and food.

I’m currently working on making renting and buying houses as easy as online shopping — if you’re looking for a place to live, check out Casalova and let me know what you think. 🙂

Favourite or most effective re-brand you can think of?


How would you describe your design style?

My design style is shaped by the form of the problem and function of the solution.

What is the most useful piece of design advice that you have received?

In some ways each person is like all other persons; in some ways each person is like some other persons; in some ways each person is like no other persons.

What do your parents think you do?

App and fashion design.

Design Interests

Editorial and Book Design
Web Design
UI/UX Design
Design Studies and Theory



Jamie is a predictive commerce-based system that makes you a smarter shopper by connecting your daily activities and digital life. Using rich algorithm and data pulled from various platforms and networks, Jamie aggregates information about almost any product in seconds, allowing you to make better purchasing decisions.


In recent years, there has been an ongoing friction between physical and digital retail. Jamie aims to bridge the gap between both spaces and improve the user experience as a result. Imagine a purchasing experience that fits within the context of your location, the events that you attend, and your current shopping desires. Jamie helps makes shopping an immersive activity that’s more organic and social, instead of feeling like calculated chores. In a seamless fashion, Jamie connects to your existing digital life to ensure that you make the best purchasing decisions possible through machine learning algorithms focused on discovering the most relevant, affordable, and accessible items.


When I was working at Garage and Dynamite, I re-organized clothing product categories because there was a lot of repetitive categories and subtle differences that created too many categories and choices. Although re-organizing by physical characteristics versus styles helped reduce categories, there was still a paradox of choice and interacting with menus and categories is impersonal.

The audit and experience led me to thinking there must be a better way of finding relevant products. I started an audit about the rise of omni-channel shopping in the marketplace and marketspace, in order to explore and identify problems between the spaces. The primary driver of omni-channel is the complete integration of the shopping and brand experience, due to the rise of social networks and personalized retail. I was researching about omni-channel integrations because it involves environmental patterns and behaviours associated with different touchpoints, in order to have an immersive, human-centric mindset for identifying problems.

Mapping the customer journey and mobile micro-moments

I used contextual cues to map the customer journey and mobile micromoments: I-want-to-go, I-want-to-know, I-want-to-buy, and I-want-to-do.

Addressing the challenges
Working between the retail and e-commerce spaces, I noticed a lack of connection with people’s daily activities and digital life. I used correlational research methodologies by gathering and analyzing scholarly books, user research, and statistics to help confirm the demand for engagement, personalization, and self-service in e-commerce. Using correlational research is challenging because I have to determine the relationship between 2 variables from the same group of subjects — which helps determine a similarity but not a difference — so I cannot establish a direct cause and relation. I am also addressing the problem based on my personal experiences in working between the retail and e-commerce spaces in customer service which translated to this product — I was frustrated by the lack of personalized, social, and contextual selling to create a magical shopping experience, which resulted to this project.

PACT Analysis


∙ Busy, on-the-go, and stressed students
∙ Price conscious
∙ Spends about $2000 a year online
∙ Gen Z to Millennials (teens to age 35)
∙ Live for today
∙ Will select and continue to buy favourite brand at pre-recession prices

∙ Heavy social media users
∙ Actively online looking for inspiration
∙ Researches the best deals online and in-store
∙ Heavy online shoppers with data to pull from
∙ Early adopters
∙ Brand loyalty: Uses social media to tell friends about products and influence upcoming trends

∙ Socially connected purchases
∙ Asks friends for feedback in purchasing decisions
∙ Browses individually for inspiration
∙ Shares inspiration using social media

∙ Current and proposed: mobile

Amazon’s Alexa and Google Home are starting the evolution of immersive shopping and smart assistants using connected devices and voices. Constraints include income and adoption for devices such as voice-activated speakers and virtual reality kits. What we need is a transitional app on smartphone devices with global adoption and inclusive reach, while reducing income barriers.

Scalable machine learning connects behaviour with environmental patterns and data from various social network profiles. By connecting to various data sources, we can recommend a personalized product catalogue which creates a sense of empathy and understanding — what humans can’t do efficiently on a global scale.



Visual Search

Nancy wants to find out more about her friend’s camera. She also wants a mirrorless camera for hobby use.

Sometimes products can’t be described in words/search field which is impersonal. Using search fields, you can generate hundreds of results with only a few that are meaningful and relevant to your life, which creates a paradox of choice and lack of personalization. By connecting your daily activities and digital life, machine learning can help create a personalized experience by using a visual conversation to describe subtle differences. Instead of spending hours researching and comparing products, people can interact with relevant products, suiting their taste, and budget to make faster purchase decisions.

Using the camera helps reduce cultural biases because Jamie automatically identifies and recommends clothing and products in a visual, abstract, and universal nature. Then Jamie will have a profile of user-generated photo segmentation photos of Nancy’s preferred products to discover similar products and find the best available deal.

I-Want-to-Know and I-Want-to-Buy
By being there in the right context before and after the purchase, the homepage involves machine learning to pick up that Nancy has been searching for a mirrorless camera and recommends products connected to an upcoming event (via email, ticketmaster, fb) and budget (via credit card limit, banking, financing). This timely recommendation can also be communicated through a notification.

Trending in Toronto
Nancy resides in Toronto so Jamie uses localized demand to forecast products for a contextual experience. Before Nancy signs up for an account, her initial product viewing would be to link cookies similarly to ads for relevance.

Jamie follows up with Nancy after purchasing with personalized price forecasting based on her visual search activity. Nancy may also seek instructions for her products.

See more of my work at the graduate showcase