CloudKitchens — Lutra App
Overview
Lutra is the first all-in-one restaurant management solution — covering everything from location selection to day-to-day operations and performance analysis for merchants.
- Lead Product Designer
- UX design / Visual design / Motion
- Native / Mobile
- Aug 2022 — Oct 2023
Background
The potential of Chinese merchants
The Chinese restaurant industry is vast, There are many individual merchants and chain stores. Operating in this sector poses various challenges, including site selection, data analysis, operational strategies, and growth. The average business cycle of a new kitchen only survive six months in a extremely competitive environment.
Our mission is to assist merchants in enhancing their operational capabilities, fostering profitability and growth.
We initially drove got 200,000 small and medium-sized merchants by selling printers. Our goal is to better utilize our merchant resources and achieve our mission through our product, thereby helping businesses improve their operations.
Research
User Feedback
Interview: 30 min
32 Calls
Interview: 30 min
24 Visits
Form: 5 min to finish
160 Surveys
Identify problems
Experience vs. data-informed decisions
Over 70% of merchant terminate their leases annually, and over 50% of merchants have a monthly order volume of less than 100 in China. They entered the catering industry without sufficient preparation.
We need to improve their data-driven mindset and educate merchants group, meanwhile, help them increase their store orders and revenue at beginning.
New merchants are hard to get started and grow
Most of the merchants are new businesses; they need to quickly boost store visibility and orders within a short period.
Lack of operational mindset
Most merchants are still using traditional methods for marketing — they would rather have a personal human assistant than explore the product.
Complex to operate on multiple platforms
Multiple food delivery platforms require restaurants to equip multiple tablets, increasing costs and potentially reducing efficiency.
Hypothesis
Hypothesis
Providing the Coupon Distribution feature for new merchants and helping them boost their business within a short period.
Develop an AI assistant that could guide users through various scenarios, assisting merchants in making fundamental decisions.
Collect the data from all delivery platforms in a central dashboard. Users don’t need to check data on each platform.
Design process
Build an MVP to verify the hypothesis
From this 0-1 project, I worked on many more projects during my time at CloudKitchens; if you’re interested, please reach out to me.
Order Booster
The design from Storyboarding, Ideations, Prototyping. After extensive research and experimentation, we arrived at the final design, and we are still iterating based on AB test, user feedback and product research.
The MVP of Order Booster is helping merchants boost store orders and revenue by allowing them to set coupon values before initiating the distribution of coupons.
Phased Outcome
- They don’t come back to review and improve their coupon strategy.
- Merchant have no clear concept of how to set the coupon value. The coupons don’t perform well in practical usage.
- Our merchant does not believe in the product, so we need to give users the clue that we are capable of providing all the eaters data, and the coupons are truly sent to the eaters’ hands.
Iteration Goals
- AI Assistant could give user actions on the homepage card, give them more detailed data for their coupon result and lead the user to change the coupon strategy when the performance is not good. Give them the coupon history comparison page.
- Give the user a list of AI recommendation coupon values for the user to choose from, give user the opportunity to build several coupons.
- We will indicate the locations on the map where the coupon goes.
Final design
Final design & Visual craft
Iterate Goal 1
AI Assistant could give user actions on the homepage card, give them more detailed data for their coupon result and lead the user to change the coupon strategy when the performance is not good. Give them the coupon history comparison page.
Iterate Goal 2
Give the user a list of AI recommendation coupon values for the user to choose from, give user the opportunity to build several coupons.
Iterate Goal 3
We will indicate the locations on the map where the coupon goes, and provide a central dashboard of their coupon data. They don’t need to go back and forth on different platforms.
From business goal
+75.7%
Click Rate
+12.2%
Adjusting Coupon
92%
Satisfaction
From merchant goal
+31.5%
Monthly Order
+23.2%
Monthly Revenue
+14.8%
Monthly New Eater
Operation Automation
The analysis dashboard encompasses all restaurant data, utilizing AI to provide stage-specific recommendations based on each dataset for the current merchant’s store. Users can take corresponding actions in the analyze sub-features to enhance store ratings. It includes various sub-features such as Store Monitor, Review Management, Ad Bidding, etc.
Auto Ad Bidding
Assisting users in securing the most cost-effective advertising placements on delivery platforms.
Lutra AI Assistant
Offering store optimization and guidance plans within various features for user.
+0.5
Monthly Rating
+7.2%
Monthly Revenue
+9.7%
Monthly Order
+11.3%
Monthly New Eater
Core Tabs
Me Tab, Printer, and Orders. Reach out to me if you are interested in more details.
