Create your dream companion
Launched in 2023 September
User Growth
User growth went up by 111% compare to October, but by December it went down by 17%
/ Make it Public
Share your creation to anyone, enables anyone to chat with your creation.
Problems started to surface
Unclear Vocabulary Used -
“What’s a Snapshot?”Complex Flow -
“Didnt i just do that?”Relying on sale team -
“Can we get on a call to onboard our new analyst?”
/ Growth
After we have resolved all the painpoints users have faced since launch and made sure onboarding was clear and easy for users to understand how to train their custom model, data have shown that we have boosted total growth by 42%
Data
- amount of people reduced trying to ask sales to onboard
- flow painpoints, cut, flow counts, decision counts, time spent
- % new clients
/ Role
/ Problem
Shifting Target User
Initially, AutoML was made for sales team to hold hands with clients during onboarding and the assumption that our main users are data and ML engineers (people who understand code). Stakeholders have decided to change target users from a B2B product to a B2B and B2C product.
Onboarding
Users get confused with the complexity of the product flow and the difficult vocabulary used.
”What is Snapshot?”
”What should I do now?”
”Didn’t I just do that? Why am I doing this again?”
How do we make sure that users don’t get confused anymore? We gathered as a team and made sure these thoughts do not appear in user’s head and made sure it is simple to use.
Aim to modernize in-store retail
/ User Research
I begin this project by conducting user interviews to understand the key problem of why people are spending less time shopping in person in favor of shopping online.
/ Findings
| Best In-store Shopping Experience
Genuine Customer Service
Finding the right fit
Surprised by the store
| Worst In-store Shopping Experience
The most common in-store shopping complaint is queuing
/ Who’s the TA?
/ Competitors
Scanning techniques (such as the use of QR codes) are not yet popular in malls; mostly they’re used in stores such as IKEA or Argos where vendors sell items that are either heavy or too big.
/ Analysis
I organized the information gathered from all of the user research in order to plan user flows.
/ User Flow
/ Colors & Font
Since this is a collaboration with Westfield, I followed their color guide on designing the interface.
/ Iteration
Home Screens
I’ve created 3 versions for the most essential part of an app, the home screens. After 10 user testings, the results have shown that
users who used option B as their home screens get to their goal 13-18 seconds faster than users with options A and C.
by completing the goal faster suggests, a clearer UI structure and layout for the homepage
/ Next
For now, I am proud of this app and would love to see people actually using it. but there is still a lot of engineering that needs for this app to work and be ready to launch.
But for me, I believe the scanning technique is the future of humanity shopping.
